The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they ofte...The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.展开更多
Objective This study aimed to assess the local staging of bladder tumors in patients utilizing preoperative multiparametric MRI(mpMRI)and to demonstrate the clinical efficacy of this method through a comparative analy...Objective This study aimed to assess the local staging of bladder tumors in patients utilizing preoperative multiparametric MRI(mpMRI)and to demonstrate the clinical efficacy of this method through a comparative analysis with corresponding histopathological findings.Methods Between November 2020 and April 2022,63 patients with a planned cystoscopy and a preliminary or previous diagnosis of bladder tumor were included.All participants underwent mpMRI,and Vesical Imaging Reporting and Data System(VI-RADS)criteria were applied to assess the recorded images.Subsequently,obtained biopsies were histopathologically examined and compared with radiological findings.Results Of the 63 participants,60 were male,and three were female.Categorizing tumors with a VI-RADS score of>3 as muscle invasive,84%were radiologically classified as having an invasive bladder tumor.However,histopathological results indicated invasive bladder tumors in 52%of cases.Sensitivity of the VI-RADS score was 100%;specificity was 23%;the negative predictive value was 100%;and the positive predictive value was 62%.Conclusion The scoring system obtained through mpMRI,VI-RADS,proves to be a successful method,particularly in determining the absence of muscle invasion in bladder cancer.Its efficacy in detecting muscle invasion in bladder tumors could be further enhanced with additional studies,suggesting potential for increased diagnostic efficiency through ongoing research.The VI-RADS could enhance the selection of patients eligible for accurate diagnosis and treatment.展开更多
Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence(AI)in medical imaging.The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologi...A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence(AI)in medical imaging.The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologies,especially deep learning,that performs image recognition,feature extraction,and pattern analysis.Furthermore,AI has demonstrated significant promise in assessing the effects of treatments and forecasting the course of diseases.It also provides doctors with more advanced tools for managing the conditions of their patients.AI is poised to play a more significant role in medical imaging,especially in real-time image processing and multimodal fusion.By integrating multiple forms of image data,multimodal fusion technology provides more comprehensive disease information,whereas real-time image analysis can assist surgeons in making more precise de-cisions.By tailoring treatment regimens to each patient's unique needs,AI enhances both the effectiveness of treatment and the patient experience.Overall,AI in medical imaging promises a bright future,significantly enhancing diagnostic precision and therapeutic efficacy,and ultimately delivering higher-quality medical care to patients.展开更多
In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential grow...In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.展开更多
Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with P...Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with PCa, clinically significant PCa （CSPCa）, or no PCa, especially among those with serum total prostate-specific antigen （tPSA） levels in the ＂gray zone＂ （4-10 ng ml-1）. A total of 308 patients （355 lesions） were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density （PSAD） were independent predictors of PCa and CSPCa. A PI-RADS v2 score L≥4 provided high negative predictive values （91.39% for PCa and 95.69% for CSPCa）. A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group （PI-RADS score = 5 and PSAD L≥0 15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score L≥4 with high tPSA level） with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.展开更多
BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the ris...BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the risk of misdiagnosing intrahepatic cholangiocarcinoma(ICC).The introduction of CEUS Liver Imaging Reporting and Data System(CEUS LI-RADS)might overcome this limitation.Even though data from the literature seems promising,its reliability in real-life context has not been well-established yet.AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis.METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis.The diagnostic standard for all nodules was either biopsy(102 nodules)or CT/MRI(409 nodules).Common diagnostic accuracy indexes such as sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)were assessed for the following associations:CEUS LR-5 and HCC;CEUS LR-4 and 5 merged class and HCC;CEUS LR-M and ICC;and CEUS LR-3 and malignancy.The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined.Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated.RESULTS CEUS LR-5 predicted HCC with a 67.6%sensitivity,97.7%specificity,and 99.3%PPV(P<0.001).The merging of LR-4 and 5 offered an improved 93.9%sensitivity in HCC diagnosis with a 94.3%specificity and 98.8%PPV(P<0.001).CEUS LR-M predicted ICC with a 91.3%sensitivity,96.7%specificity,and 99.6%NPV(P<0.001).CEUS LR-3 predominantly included benign lesions(only 28.8%of malignancies).In this class,the hypo-hypo pattern showed a much higher rate of malignant lesions(73.3%)than the iso-iso pattern(2.6%).Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect(n=511,k=0.94,P<0.001),while the agreement among raters from separate centres was substantial(n=50,k=0.67,P<0.001).Agreement was stronger for arterial phase hyperenhancement(internal k=0.86,P<2.7×10-214;external k=0.8,P<0.001)than washout(internal k=0.79,P<1.6×10-202;external k=0.71,P<0.001).CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.展开更多
BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisf...BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisfactory diagnostic value.However,a unified conclusion on the interobserver stability of this innovative ultrasound imaging has not been determined.The present metaanalysis examined the interobserver agreement of CEUS LI-RADS to provide some reference for subsequent related research.AIM To evaluate the interobserver agreement of LI-RADS on CEUS and analyze the sources of heterogeneity between studies.METHODS Relevant papers on the subject of interobserver agreement on CEUS LI-RADS published before March 1,2020 in China and other countries were analyzed.The studies were filtered,and the diagnostic criteria were evaluated.The selected references were analyzed using the“meta”and“metafor”packages of R software version 3.6.2.RESULTS Eight studies were ultimately included in the present analysis.Meta-analysis results revealed that the summary Kappa value of included studies was 0.76[95%confidence interval,0.67-0.83],which shows substantial agreement.Higgins I2 statistics also confirmed the substantial heterogeneity(I2=91.30%,95%confidence interval,85.3%-94.9%,P<0.01).Meta-regression identified the variables,including the method of patient enrollment,method of consistency testing,and patient race,which explained the substantial study heterogeneity.CONCLUSION CEUS LI-RADS demonstrated overall substantial interobserver agreement,but heterogeneous results between studies were also obvious.Further clinical investigations should consider a modified recommendation about the experimental design.展开更多
Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoidin...Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoiding unnecessary repeat transurethral resection of bladder tumor in high-risk non-muscle-invasive bladder cancer patients.This study was done to determine the validity of VIRADS score prospectively for the diagnosis of muscleinvasive bladder cancer.Methods:This study was conducted from March 2019 to March 2020 at Sawai Man Singh Medical College and Hospital,Jaipur,Rajasthan,India.Patients admitted with the provisional diagnosis of bladder tumor were included as participants.All these patients underwent a 3 Tesla mpMRI to obtain a VIRADS score before they underwent transurethral resection of bladder tumor and these data were analyzed to evaluate the correlation of pre-operative VIRADS score with mus-cle invasiveness of the tumor in final biopsy report.Results:A cut-off of VIRADS≥4 for prediction of detrusor muscle invasion yielded a sensitivity of 79.4%,specificity of 94.2%,positive predictive value of 90.0%,negative predictive value of 87.5%,and diagnostic accuracy of 86.4%.A cut off of VIRADS≥3 for prediction of detrusor muscle invasion yielded a sensitivity of 91.2%,specificity of 78.8%,positive predictive value of 73.8%,negative predictive value of 93.2%,and accuracy of 83.7%.The receiver operating curve showed the area under the curve to be 0.922(95%confidence interval:0.862e0.983).Conclusion:VIRADS score appears to be an excellent and effective pre-operative radiological tool for the prediction of detrusor muscle invasion in bladder cancer.展开更多
Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty li...Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty liver disease.However,hepatocarcinogenesis is a complex multistep process.Various factors lead to hepatocyte malignant transformation and HCC development.Diagnosis and surveillance of HCC can be made with the use of liver ultrasound(US)every 6 mo.However,the sensitivity of this imaging method to detect HCC in a cirrhotic liver is limited,due to the abnormal liver parenchyma.Computed tomography(CT)and magnetic resonance imaging(MRI)are considered to be most useful tools for at-risk patients or patients with inadequate US.Liver biopsy is still used for diagnosis and prognosis of HCC in specific nodules that cannot be definitely characterized as HCC by imaging.Recently the American College of Radiology designed the Liver Imaging Reporting and Data System(LI-RADS),which is a comprehensive system for standardized interpretation of CT and MRI liver examinations that was first proposed in 2011.In 2018,it was integrated into the American Association for the Study of Liver Diseases guidance statement for HCC.LI-RADS is designed to ensure high sensitivity,precise categorization,and high positive predictive value for the diagnosis of HCC and is applied to“highrisk populations”according to specific criteria.Most importantly LI-RADS criteria achieved international collaboration and consensus among liver experts around the world on the best practices for caring for patients with or at risk for HCC.展开更多
Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locore...Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locoregional therapies which can be used alone,in combination with each other,or in combination with systemic therapy.These treatment options have shown to be effective in achieving remission,controlling tumor progression,improving disease free and overall survival in patients who cannot undergo resection and providing a bridge to transplant by debulking tumor burden to downstage patients.Following locoregional therapy(LRT),it is crucial to provide treatment response assessment to guide management and liver transplant candidacy.Therefore,Liver Imaging Reporting and Data Systems(LI-RADS)Treatment Response Algorithm(TRA)was created to provide a standardized assessment of HCC following LRT.LIRADS TRA provides a step by step approach to evaluate each lesion independently for accurate tumor assessment.In this review,we provide an overview of different locoregional therapies for HCC,describe the expected post treatment imaging appearance following treatment,and review the LI-RADS TRA with guidance for its application in clinical practice.Unique to other publications,we will also review emerging literature supporting the use of LI-RADS for assessment of HCC treatment response after LRT.展开更多
BACKGROUND The American College of Radiology Thyroid Imaging Reporting and Data System(ACR TI-RADS)was introduced to standardize the ultrasound characterization of thyroid nodules.Studies have shown that ACR-TIRADS re...BACKGROUND The American College of Radiology Thyroid Imaging Reporting and Data System(ACR TI-RADS)was introduced to standardize the ultrasound characterization of thyroid nodules.Studies have shown that ACR-TIRADS reduces unnecessary biopsies and improves consistency of imaging recommendations.Despite its widespread adoption,there are few studies to date assessing the inter-reader agreement amongst radiology trainees with limited ultrasound experience.We hypothesize that in PGY-4 radiology residents with no prior exposure to ACR TIRADS,a statistically significant improvement in inter-reader reliability can be achieved with a one hour training session.AIM To evaluate the inter-reader agreement of radiology residents in using ACR TIRADS before and after training.METHODS A single center retrospective cohort study evaluating 50 thyroid nodules in 40 patients of varying TI-RADS levels was performed.Reference standard TI-RADS scores were established through a consensus panel of three fellowship-trained staff radiologists with between 1 and 14 years of clinical experience each.Three PGY-4 radiology residents(trainees)were selected as blinded readers for this study.Each trainee had between 4 to 5 mo of designated ultrasound training.No trainee had received specialized TI-RADS training prior to this study.Each of the readers independently reviewed the 50 testing cases and assigned a TI-RADS score to each case before and after TI-RADS training performed 6 wk apart.Fleiss kappa was used to measure the pooled inter-reader agreement.The relative diagnostic performance of readers,pre-and post-training,when compared against the reference standard.RESULTS There were 33 females and 7 males with a mean age of 56.6±13.6 years.The mean nodule size was 19±14 mm(range from 5 to 63 mm).A statistically significant superior inter-reader agreement was found on the post-training assessment compared to the pre-training assessment for the following variables:1.“Shape”(k of 0.09[slight]pre-training vs 0.67[substantial]post-training,P<0.001),2.“Echogenic foci”(k of 0.28[fair]pre-training vs 0.45[moderate]post-training,P=0.004),3.‘TI-RADS level’(k of 0.14[slight]pre-training vs 0.36[fair]post-training,P<0.001)and 4.‘Recommendations’(k of 0.36[fair]pre-training vs 0.50[moderate]post-training,P=0.02).No significant differences between the preand post-training assessments were found for the variables'composition','echogenicity'and'margins'.There was a general trend towards improved pooled sensitivity with TI-RADS levels 1 to 4 for the post-training assessment while the pooled specificity was relatively high(76.6%-96.8%)for all TI-RADS level.CONCLUSION Statistically significant improvement in inter-reader agreement in the assigning TI-RADS level and recommendations after training is observed.Our study supports the use of dedicated ACR TI-RADS training in radiology residents.展开更多
[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different d...[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.展开更多
China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this pap...China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.展开更多
BACKGROUND Currently,only tumors classified as LR-5 are considered definitive hepatocellular carcinoma(HCC),and no further pathologic confirmation is required to initiate therapy.Previous studies have shown that the s...BACKGROUND Currently,only tumors classified as LR-5 are considered definitive hepatocellular carcinoma(HCC),and no further pathologic confirmation is required to initiate therapy.Previous studies have shown that the sensitivity of LR-5 is modest,and lesions enhanced by gadoxetic acid(Gd-EOB-DTPA)may exhibit lower sensitivity than those enhanced by Gd-DTPA.AIM To identify malignant ancillary features(AFs)that can independently and significantly predict HCC in Liver Imaging Reporting and Data System version 2018,and to develop modified LR-5 criteria to improve diagnostic performance on Gd-EOB-DTPA-enhanced magnetic resonance imaging.METHODS Imaging data from patients with HCC risk factors who underwent abdominal Gd-EOB-DTPA-enhanced magnetic resonance imaging were collected.Univariate and multivariate logistic regression analyses were performed to determine AFs that could independently and significantly predict HCC.The modified LR-5 criteria involved reclassifying LR-4/LR-3 lesions based on major features combined with independently significant AFs for HCC,or by substituting threshold growth with significant AFs.McNemar's test was used to compare the diagnostic performance of the modified LR-5 criteria.RESULTS A total of 244 lesions from 216 patients were included.Transitional phase hypointensity,mild-moderate T2 hyperintensity,and fat in mass(more than adjacent liver)were identified as significant independent predictors of HCC.Using the modified LR-5 criteria(e.g.,LR-5-M1:LR-4+transitional phase hypointensity;LR-5-M4:LR-5 by transitional phase hypointensity instead of threshold growth;LR-5-M5:LR-5 by mild-moderate T2 hyperintensity instead of threshold growth;LR-5-M8:LR-3/LR-4+any two features of transitional phase hypointensity/mild-moderate T2 hyperintensity/fat in mass),sensitivities were significantly increased(88.5%-89.1%)compared to the standard LR-5(60.6%;all P values<0.05),while specificities(84.8%-89.9%)remained largely unchanged(93.7%;all P values>0.05).The LR-5-M8 criterion achieved the highest sensitivity.CONCLUSION Mild-moderate T2 hyperintensity,transitional phase hypointensity,and fat in mass are independent and significant predictors of HCC malignant AFs.The modified LR-5 criteria can improve sensitivity without significantly reducing specificity.展开更多
Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as L...Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.展开更多
This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional...This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional algorithm: that is, the classification accura- cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.展开更多
Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of m...Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.展开更多
In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the f...In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.展开更多
文摘The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.
文摘Objective This study aimed to assess the local staging of bladder tumors in patients utilizing preoperative multiparametric MRI(mpMRI)and to demonstrate the clinical efficacy of this method through a comparative analysis with corresponding histopathological findings.Methods Between November 2020 and April 2022,63 patients with a planned cystoscopy and a preliminary or previous diagnosis of bladder tumor were included.All participants underwent mpMRI,and Vesical Imaging Reporting and Data System(VI-RADS)criteria were applied to assess the recorded images.Subsequently,obtained biopsies were histopathologically examined and compared with radiological findings.Results Of the 63 participants,60 were male,and three were female.Categorizing tumors with a VI-RADS score of>3 as muscle invasive,84%were radiologically classified as having an invasive bladder tumor.However,histopathological results indicated invasive bladder tumors in 52%of cases.Sensitivity of the VI-RADS score was 100%;specificity was 23%;the negative predictive value was 100%;and the positive predictive value was 62%.Conclusion The scoring system obtained through mpMRI,VI-RADS,proves to be a successful method,particularly in determining the absence of muscle invasion in bladder cancer.Its efficacy in detecting muscle invasion in bladder tumors could be further enhanced with additional studies,suggesting potential for increased diagnostic efficiency through ongoing research.The VI-RADS could enhance the selection of patients eligible for accurate diagnosis and treatment.
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.82360266,81960224,and 81860248)Guizhou Provincial Basic Research Program(Grant Nos.Qiankehe basic-ZK[2023]general 395+2 种基金Qiankehe basic-ZK[2023]general 324Qiankehe basic-MS[2025]548)Key Lab of Acute Brain Injury and Function Repair at Guizhou Medical University(Grant No.[2024]fy0071).
文摘A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence(AI)in medical imaging.The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologies,especially deep learning,that performs image recognition,feature extraction,and pattern analysis.Furthermore,AI has demonstrated significant promise in assessing the effects of treatments and forecasting the course of diseases.It also provides doctors with more advanced tools for managing the conditions of their patients.AI is poised to play a more significant role in medical imaging,especially in real-time image processing and multimodal fusion.By integrating multiple forms of image data,multimodal fusion technology provides more comprehensive disease information,whereas real-time image analysis can assist surgeons in making more precise de-cisions.By tailoring treatment regimens to each patient's unique needs,AI enhances both the effectiveness of treatment and the patient experience.Overall,AI in medical imaging promises a bright future,significantly enhancing diagnostic precision and therapeutic efficacy,and ultimately delivering higher-quality medical care to patients.
文摘In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.
文摘Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with PCa, clinically significant PCa （CSPCa）, or no PCa, especially among those with serum total prostate-specific antigen （tPSA） levels in the ＂gray zone＂ （4-10 ng ml-1）. A total of 308 patients （355 lesions） were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density （PSAD） were independent predictors of PCa and CSPCa. A PI-RADS v2 score L≥4 provided high negative predictive values （91.39% for PCa and 95.69% for CSPCa）. A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group （PI-RADS score = 5 and PSAD L≥0 15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score L≥4 with high tPSA level） with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.
基金Supported by the Fondazione di Sardegna,No.FDS2019VIDILIthe University of Sassari,No.FAR2019.
文摘BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the risk of misdiagnosing intrahepatic cholangiocarcinoma(ICC).The introduction of CEUS Liver Imaging Reporting and Data System(CEUS LI-RADS)might overcome this limitation.Even though data from the literature seems promising,its reliability in real-life context has not been well-established yet.AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis.METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis.The diagnostic standard for all nodules was either biopsy(102 nodules)or CT/MRI(409 nodules).Common diagnostic accuracy indexes such as sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)were assessed for the following associations:CEUS LR-5 and HCC;CEUS LR-4 and 5 merged class and HCC;CEUS LR-M and ICC;and CEUS LR-3 and malignancy.The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined.Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated.RESULTS CEUS LR-5 predicted HCC with a 67.6%sensitivity,97.7%specificity,and 99.3%PPV(P<0.001).The merging of LR-4 and 5 offered an improved 93.9%sensitivity in HCC diagnosis with a 94.3%specificity and 98.8%PPV(P<0.001).CEUS LR-M predicted ICC with a 91.3%sensitivity,96.7%specificity,and 99.6%NPV(P<0.001).CEUS LR-3 predominantly included benign lesions(only 28.8%of malignancies).In this class,the hypo-hypo pattern showed a much higher rate of malignant lesions(73.3%)than the iso-iso pattern(2.6%).Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect(n=511,k=0.94,P<0.001),while the agreement among raters from separate centres was substantial(n=50,k=0.67,P<0.001).Agreement was stronger for arterial phase hyperenhancement(internal k=0.86,P<2.7×10-214;external k=0.8,P<0.001)than washout(internal k=0.79,P<1.6×10-202;external k=0.71,P<0.001).CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.
基金Supported by Health Commission of Hubei Province,China No.WJ2019M077 and No.WJ2019H227Natural Science Foundation of Hubei Province,China No.2019CFB286and Science and Technology Bureau of Shihezi,China No.2019ZH11.
文摘BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisfactory diagnostic value.However,a unified conclusion on the interobserver stability of this innovative ultrasound imaging has not been determined.The present metaanalysis examined the interobserver agreement of CEUS LI-RADS to provide some reference for subsequent related research.AIM To evaluate the interobserver agreement of LI-RADS on CEUS and analyze the sources of heterogeneity between studies.METHODS Relevant papers on the subject of interobserver agreement on CEUS LI-RADS published before March 1,2020 in China and other countries were analyzed.The studies were filtered,and the diagnostic criteria were evaluated.The selected references were analyzed using the“meta”and“metafor”packages of R software version 3.6.2.RESULTS Eight studies were ultimately included in the present analysis.Meta-analysis results revealed that the summary Kappa value of included studies was 0.76[95%confidence interval,0.67-0.83],which shows substantial agreement.Higgins I2 statistics also confirmed the substantial heterogeneity(I2=91.30%,95%confidence interval,85.3%-94.9%,P<0.01).Meta-regression identified the variables,including the method of patient enrollment,method of consistency testing,and patient race,which explained the substantial study heterogeneity.CONCLUSION CEUS LI-RADS demonstrated overall substantial interobserver agreement,but heterogeneous results between studies were also obvious.Further clinical investigations should consider a modified recommendation about the experimental design.
文摘Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoiding unnecessary repeat transurethral resection of bladder tumor in high-risk non-muscle-invasive bladder cancer patients.This study was done to determine the validity of VIRADS score prospectively for the diagnosis of muscleinvasive bladder cancer.Methods:This study was conducted from March 2019 to March 2020 at Sawai Man Singh Medical College and Hospital,Jaipur,Rajasthan,India.Patients admitted with the provisional diagnosis of bladder tumor were included as participants.All these patients underwent a 3 Tesla mpMRI to obtain a VIRADS score before they underwent transurethral resection of bladder tumor and these data were analyzed to evaluate the correlation of pre-operative VIRADS score with mus-cle invasiveness of the tumor in final biopsy report.Results:A cut-off of VIRADS≥4 for prediction of detrusor muscle invasion yielded a sensitivity of 79.4%,specificity of 94.2%,positive predictive value of 90.0%,negative predictive value of 87.5%,and diagnostic accuracy of 86.4%.A cut off of VIRADS≥3 for prediction of detrusor muscle invasion yielded a sensitivity of 91.2%,specificity of 78.8%,positive predictive value of 73.8%,negative predictive value of 93.2%,and accuracy of 83.7%.The receiver operating curve showed the area under the curve to be 0.922(95%confidence interval:0.862e0.983).Conclusion:VIRADS score appears to be an excellent and effective pre-operative radiological tool for the prediction of detrusor muscle invasion in bladder cancer.
文摘Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty liver disease.However,hepatocarcinogenesis is a complex multistep process.Various factors lead to hepatocyte malignant transformation and HCC development.Diagnosis and surveillance of HCC can be made with the use of liver ultrasound(US)every 6 mo.However,the sensitivity of this imaging method to detect HCC in a cirrhotic liver is limited,due to the abnormal liver parenchyma.Computed tomography(CT)and magnetic resonance imaging(MRI)are considered to be most useful tools for at-risk patients or patients with inadequate US.Liver biopsy is still used for diagnosis and prognosis of HCC in specific nodules that cannot be definitely characterized as HCC by imaging.Recently the American College of Radiology designed the Liver Imaging Reporting and Data System(LI-RADS),which is a comprehensive system for standardized interpretation of CT and MRI liver examinations that was first proposed in 2011.In 2018,it was integrated into the American Association for the Study of Liver Diseases guidance statement for HCC.LI-RADS is designed to ensure high sensitivity,precise categorization,and high positive predictive value for the diagnosis of HCC and is applied to“highrisk populations”according to specific criteria.Most importantly LI-RADS criteria achieved international collaboration and consensus among liver experts around the world on the best practices for caring for patients with or at risk for HCC.
文摘Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locoregional therapies which can be used alone,in combination with each other,or in combination with systemic therapy.These treatment options have shown to be effective in achieving remission,controlling tumor progression,improving disease free and overall survival in patients who cannot undergo resection and providing a bridge to transplant by debulking tumor burden to downstage patients.Following locoregional therapy(LRT),it is crucial to provide treatment response assessment to guide management and liver transplant candidacy.Therefore,Liver Imaging Reporting and Data Systems(LI-RADS)Treatment Response Algorithm(TRA)was created to provide a standardized assessment of HCC following LRT.LIRADS TRA provides a step by step approach to evaluate each lesion independently for accurate tumor assessment.In this review,we provide an overview of different locoregional therapies for HCC,describe the expected post treatment imaging appearance following treatment,and review the LI-RADS TRA with guidance for its application in clinical practice.Unique to other publications,we will also review emerging literature supporting the use of LI-RADS for assessment of HCC treatment response after LRT.
文摘BACKGROUND The American College of Radiology Thyroid Imaging Reporting and Data System(ACR TI-RADS)was introduced to standardize the ultrasound characterization of thyroid nodules.Studies have shown that ACR-TIRADS reduces unnecessary biopsies and improves consistency of imaging recommendations.Despite its widespread adoption,there are few studies to date assessing the inter-reader agreement amongst radiology trainees with limited ultrasound experience.We hypothesize that in PGY-4 radiology residents with no prior exposure to ACR TIRADS,a statistically significant improvement in inter-reader reliability can be achieved with a one hour training session.AIM To evaluate the inter-reader agreement of radiology residents in using ACR TIRADS before and after training.METHODS A single center retrospective cohort study evaluating 50 thyroid nodules in 40 patients of varying TI-RADS levels was performed.Reference standard TI-RADS scores were established through a consensus panel of three fellowship-trained staff radiologists with between 1 and 14 years of clinical experience each.Three PGY-4 radiology residents(trainees)were selected as blinded readers for this study.Each trainee had between 4 to 5 mo of designated ultrasound training.No trainee had received specialized TI-RADS training prior to this study.Each of the readers independently reviewed the 50 testing cases and assigned a TI-RADS score to each case before and after TI-RADS training performed 6 wk apart.Fleiss kappa was used to measure the pooled inter-reader agreement.The relative diagnostic performance of readers,pre-and post-training,when compared against the reference standard.RESULTS There were 33 females and 7 males with a mean age of 56.6±13.6 years.The mean nodule size was 19±14 mm(range from 5 to 63 mm).A statistically significant superior inter-reader agreement was found on the post-training assessment compared to the pre-training assessment for the following variables:1.“Shape”(k of 0.09[slight]pre-training vs 0.67[substantial]post-training,P<0.001),2.“Echogenic foci”(k of 0.28[fair]pre-training vs 0.45[moderate]post-training,P=0.004),3.‘TI-RADS level’(k of 0.14[slight]pre-training vs 0.36[fair]post-training,P<0.001)and 4.‘Recommendations’(k of 0.36[fair]pre-training vs 0.50[moderate]post-training,P=0.02).No significant differences between the preand post-training assessments were found for the variables'composition','echogenicity'and'margins'.There was a general trend towards improved pooled sensitivity with TI-RADS levels 1 to 4 for the post-training assessment while the pooled specificity was relatively high(76.6%-96.8%)for all TI-RADS level.CONCLUSION Statistically significant improvement in inter-reader agreement in the assigning TI-RADS level and recommendations after training is observed.Our study supports the use of dedicated ACR TI-RADS training in radiology residents.
基金Supported by the Special Fundation of China Geological Survey(1212010911084)~~
文摘[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.
文摘China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.
基金This study was approved by the Medical Ethics Committee of Jieshou City People's Hospital,approval No.[2022]21.
文摘BACKGROUND Currently,only tumors classified as LR-5 are considered definitive hepatocellular carcinoma(HCC),and no further pathologic confirmation is required to initiate therapy.Previous studies have shown that the sensitivity of LR-5 is modest,and lesions enhanced by gadoxetic acid(Gd-EOB-DTPA)may exhibit lower sensitivity than those enhanced by Gd-DTPA.AIM To identify malignant ancillary features(AFs)that can independently and significantly predict HCC in Liver Imaging Reporting and Data System version 2018,and to develop modified LR-5 criteria to improve diagnostic performance on Gd-EOB-DTPA-enhanced magnetic resonance imaging.METHODS Imaging data from patients with HCC risk factors who underwent abdominal Gd-EOB-DTPA-enhanced magnetic resonance imaging were collected.Univariate and multivariate logistic regression analyses were performed to determine AFs that could independently and significantly predict HCC.The modified LR-5 criteria involved reclassifying LR-4/LR-3 lesions based on major features combined with independently significant AFs for HCC,or by substituting threshold growth with significant AFs.McNemar's test was used to compare the diagnostic performance of the modified LR-5 criteria.RESULTS A total of 244 lesions from 216 patients were included.Transitional phase hypointensity,mild-moderate T2 hyperintensity,and fat in mass(more than adjacent liver)were identified as significant independent predictors of HCC.Using the modified LR-5 criteria(e.g.,LR-5-M1:LR-4+transitional phase hypointensity;LR-5-M4:LR-5 by transitional phase hypointensity instead of threshold growth;LR-5-M5:LR-5 by mild-moderate T2 hyperintensity instead of threshold growth;LR-5-M8:LR-3/LR-4+any two features of transitional phase hypointensity/mild-moderate T2 hyperintensity/fat in mass),sensitivities were significantly increased(88.5%-89.1%)compared to the standard LR-5(60.6%;all P values<0.05),while specificities(84.8%-89.9%)remained largely unchanged(93.7%;all P values>0.05).The LR-5-M8 criterion achieved the highest sensitivity.CONCLUSION Mild-moderate T2 hyperintensity,transitional phase hypointensity,and fat in mass are independent and significant predictors of HCC malignant AFs.The modified LR-5 criteria can improve sensitivity without significantly reducing specificity.
文摘Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.
文摘This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de- creased as comparing with that by a conventional algorithm: that is, the classification accura- cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.
基金Under the auspices of National Natural Science Foundation of China(No.41230751,41101547)Scientific Research Foundation of Graduate School of Nanjing University(No.2012CL14)
文摘Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.
文摘In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.
