With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper d...With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.展开更多
Deep learning based analyses of computed tomography(CT)images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method tha...Deep learning based analyses of computed tomography(CT)images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19.Two ensemble strategies are considered:the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation;voting strategy.A database containing 8347 CT slices of COVID-19,common pneumonia and normal subjects was used as training and testing sets.Results show that the novel method can reach a high accuracy of 99.37%(recall:0.9981;precision:0.9893),with an increase of about 7% in comparison to single-component models.And the average test accuracy is 95.62%(recall:0.9587;precision:0.9559),with a corresponding increase of 5.2%.Compared with several latest deep learning models on the identical test set,our method made an accuracy improvement up to 10.88%.The proposed method may be a promising solution for the diagnosis of COVID-19.展开更多
It is not easy to reduce the metal artifacts of computed tomography images.However,the pixel values inside the metal artifact regions vary smoothly,while those on the borders of the metal and the bone regions vary sha...It is not easy to reduce the metal artifacts of computed tomography images.However,the pixel values inside the metal artifact regions vary smoothly,while those on the borders of the metal and the bone regions vary sharply.When the Canny operation by adaptive thresholding is conducted on the raw image,the almost continuous edges can be formed obviously on the borders of the metal and the bone regions,but this kind of information cannot be formed for the metal artifact regions.In this paper,by searching the closed areas formed by the border edges of the bone regions in the Canny image,the metal artifact regions,which are very difficult to discriminate only by intensity thresholding,can be excluded effectively.A novel prior image-based method is thus developed for metal artifact reduction.The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image.The method is suitable for the clinical application.展开更多
The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce t...The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.展开更多
BACKGROUND Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options.The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in e...BACKGROUND Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options.The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in esophageal cancer,but most follow-up computed tomography(CT)scans do not extend to L3 and limiting its utility.Radiomics has emerged as a powerful tool for extracting prognostic information from medical images.AIM To evaluate the influential features for esophageal cancer prognosis by integrating radiomic and body compositionbased indices of skeletal muscle and adipose tissue at the T12 level from both pretreatment and follow-up CT images,in order to assess their value in predicting overall survival(OS).METHODS This retrospective study included 212 esophageal cancer patients who underwent concurrent chemoradiotherapy,with both pretreatment and follow-up chest CT scans available.Body organ analysis(BOA)and radiomic features were extracted from skeletal muscle and adipose tissue at the T12 level using automated tools.Four feature subsets(no-radiomics,pretreatment only,follow-up only,and combined inputs)were developed using logistic regression(LR)with least absolute shrinkage and selection operator for feature selection,followed by Cox regression.Prognostic models-including nomogram,support vector classifier,LR,and extra trees classifier-were constructed to predict 1-,2-,and 3-year OS.RESULTS The model integrating both BOA and radiomics from pretreatment and follow-up CT,combined with clinical data,achieved the best performance for 2-year OS prediction,with an area under the time-dependent receiver operating characteristic curve of 0.91,sensitivity of 0.81,and specificity of 0.88 using the LR model.The most predictive features included both clinical variables,body composition indices,and radiomic features,particularly from follow-up VAT.Follow-up imaging contributed significantly to model performance,reinforcing its value in treatment response evaluation.CONCLUSION This is the first study to demonstrate that BOA indices and their corresponding radiomics at the T12-level from both pretreatment and follow-up CT scans-combined with clinical data-can provide accurate prognostic information for esophageal cancer.This approach offers a practical alternative when L3-level imaging is unavailable and supports the clinical integration of automated T12-based imaging biomarkers.The integration of these imaging features with clinical parameters enhances the prediction of survival outcomes and contributes to non-invasive,personalized treatment planning.展开更多
BACKGROUND Reliable preoperative detection of lymph node metastasis(LNM)in pancreatic cancer remains elusive:Conventional computed tomography(CT)underestimates micrometastases,and carbohydrate antigen 19-9 is hampered...BACKGROUND Reliable preoperative detection of lymph node metastasis(LNM)in pancreatic cancer remains elusive:Conventional computed tomography(CT)underestimates micrometastases,and carbohydrate antigen 19-9 is hampered by low specificity.The neutrophil-albumin ratio(NAR)simultaneously reflects systemic inflammation and nutritional depletion,but its contribution to LNM prediction in pancreatic cancer is unexplored.We hypothesised that integrating NAR with multiphase CT findings would significantly improve the accuracy of preoperative LNM assessment in patients undergoing curative-intent resection.AIM To determine whether preoperative NAR plus multi-phase CT reliably predicts nodal metastasis in pancreatic cancer.METHODS In this single-centre retrospective cohort study(February 2022 to February 2025,Ordos Central Hospital,China),129 consecutive patients undergoing curative pancreatic resection were histologically classified as LNM+(n=61)and LNM-(n=68).Preoperative NAR and platelet-albumin ratio(PAR)were calculated;optimal cut-offs were determined with X-tile.Multi-phase CT images were re-reviewed by two blinded radiologists.Independent predictors of nodal metastasis were identified by multivariate logistic regression,and model performance was evaluated with receiver operating characteristic(ROC)analysis.RESULTS Between the two cohorts,univariate comparison revealed significant divergence in age,tumour diameter,concomitant hemangioma thrombosis,PAR,NAR,and CT-detected nodal status(P<0.05).Subsequent multivariate modelling identified hemangioma thrombosis,PAR above 6.35,NAR exceeding 0.13,and radiologically positive lymph nodes as independent predictors of nodal metastasis(P<0.05).ROC evaluation indicated that the NAR-plus-CT-nodes model(model 1)reached an area under the curve(AUC)of 0.758,whereas the fourvariable composite(model 3)achieved the best performance with an AUC of 0.830(95%CI:0.753-0.890),sensitivity 83.61%,and specificity 67.65%.CONCLUSION The model 3(NAR>0.13,PAR>6.35,CT nodal positivity,hemangioma thrombosis)provides robust,clinically actionable preoperative identification of pancreatic cancer patients at high risk of LNM.展开更多
BACKGROUND Colorectal cancer(CRC)is a leading cause of cancer-related death globally,with the tumor immune microenvironment(TIME)influencing prognosis and immunotherapy response.Current TIME evaluation relies on invas...BACKGROUND Colorectal cancer(CRC)is a leading cause of cancer-related death globally,with the tumor immune microenvironment(TIME)influencing prognosis and immunotherapy response.Current TIME evaluation relies on invasive biopsies,limiting its clinical application.This study hypothesized that computed tomography(CT)-based deep learning(DL)radiomics models can non-invasively predict key TIME biomarkers:Tumor-stroma ratio(TSR),tumor-infiltrating lymphocytes(TILs),and immune score(IS).AIM To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.METHODS In this retrospective study,preoperative CT images of 315 pathologically confirmed CRC patients(220 in training cohort and 95 in validation cohort)were analyzed.Manually delineated regions of interest were used to extract DL features.Predictive models(DenseNet-121/169)for TSR,TILs,IS,and TIME classification were constructed.Performance was evaluated via receiver operating characteristic curves,calibration curves,and decision curve analysis(DCA).RESULTS The DL-DenseNet-169 model achieved area under the curve(AUC)values of 0.892[95%confidence interval(CI):0.828-0.957]for TSR and 0.772(95%CI:0.674-0.870)for TIME score.The DenseNet-121 model yielded AUC values of 0.851(95%CI:0.768-0.933)for TILs and 0.852(95%CI:0.775-0.928)for IS.Calibration curves demonstrated strong prediction-observation agreement,and DCA confirmed clinical utility across threshold probabilities(P<0.05 for all models).CONCLUSION CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation,enabling personalized immunotherapy strategies in CRC management.展开更多
BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomogra...BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomography imaging(GSI)to improve diagnostic accuracy in lymph node metastasis(LNM)assessment.AIM To extensively investigate the clinical utility of GSI in the preoperative assessment of CRC.METHODS The subject population included 200 patients with CRC who were admitted to Zibo Central Hospital from January 2022 to December 2023.All patients underwent dual-phase contrast-enhanced scans in the arterial and venous phases using GSI before surgical intervention.During the research,meticulous quantification was conducted regarding the number of patients with CRC with LNM as well as the exact count of metastatic lymph nodes.Moreover,for both metastatic and non-metastatic lymph nodes,the short diameter at the maximum crosssectional area(covering the axial,sagittal,and coronal planes),morphological features(including manifestations such as margin blurring,aggregation,and enhancement),and spectral parameters in the arterial and venous phases[specifically iodine concentration(IC),normalized IC(NIC),and the slope of the spectral curve(λHU)]were measured and recorded,and a comparative analysis was conducted.The diagnostic efficacy of each index with differences was systematically assessed using the receiver operating characteristic(ROC)curve.Concurrently,receiver operating characteristic curves were constructed for LNM screening based on the short diameter at the maximum cross-sectional area of lymph nodes and each spectral parameter in the arterial and venous phases.RESULTS The area under the curve of GSI for diagnosing LNM in patients with CRC can reach 0.897,with sensitivity,specificity,and accuracy of 92.59%,85.87%,and 89.50%,respectively.A total of 265 lymph nodes were analyzed from the 200 participants with CRC,with metastatic lymph nodes accounting for 56.60%.Compared with nonmetastatic lymph nodes,the short diameters of metastatic lymph nodes in the axial,sagittal,and coronal planes were significantly increased,whereas the IC values in the arterial and venous phases,the NIC value in the arterial phase,and theλHU values in the arterial and venous phases were significantly decreased.The short axial,sagittal,and coronal diameters,arterial-phase IC,venous-phase IC,arterial-phase NIC,arterial-phaseλHU,and venousphaseλHU for diagnosing metastatic lymph nodes demonstrated area under the curve values of 0.631,0.681,0.659,0.862,0.808,0.831,0.801,and 0.706,respectively.CONCLUSION GSI exhibits substantial clinical significance in the preoperative assessment of CRC.Among the parameters assessed,the arterial-phase IC demonstrates the most outstanding diagnostic performance,effectively improving the diagnostic efficacy for preoperative LNM in CRC.展开更多
BACKGROUND Computed tomography(CT),liver stiffness measurement(LSM),and magnetic resonance imaging(MRI)are non-invasive diagnostic methods for esophageal varices(EV)and for the prediction of high-bleeding-risk EV(HREV...BACKGROUND Computed tomography(CT),liver stiffness measurement(LSM),and magnetic resonance imaging(MRI)are non-invasive diagnostic methods for esophageal varices(EV)and for the prediction of high-bleeding-risk EV(HREV)in cirrhotic patients.However,the clinical use of these methods is controversial.AIM To evaluate the accuracy of LSM,CT,and MRI in diagnosing EV and predicting HREV in cirrhotic patients.METHODS We performed literature searches in multiple databases,including Pub Med,Embase,Cochrane,CNKI,and Wanfang databases,for articles that evaluated the accuracy of LSM,CT,and MRI as candidates for the diagnosis of EV and prediction of HREV in cirrhotic patients.Summary sensitivity and specificity,positive likelihood ratio and negative likelihood ratio,diagnostic odds ratio,and the areas under the summary receiver operating characteristic curves were analyzed.The quality of the articles was assessed using the quality assessment of diagnostic accuracy studies-2 tool.Heterogeneity was examined by Q-statistic test and I2 index,and sources of heterogeneity were explored using metaregression and subgroup analysis.Publication bias was evaluated using Deek’s funnel plot.All statistical analyses were conducted using Stata12.0,Meta Disc1.4,and Rev Man5.3.RESULTS Overall,18,17,and 7 relevant articles on the accuracy of LSM,CT,and MRI in evaluating EV and HREV were retrieved.A significant heterogeneity was observed in all analyses(P<0.05).The areas under the summary receiver operating characteristic curves of LSM,CT,and MRI in diagnosing EV and predicting HREV were 0.86(95%confidence interval[CI]:0.83-0.89),0.91(95%CI:0.88-0.93),and 0.86(95%CI:0.83-0.89),and 0.85(95%CI:0.81-0.88),0.94(95%CI:0.91-0.96),and 0.83(95%CI:0.79-0.86),respectively,with sensitivities of 0.84(95%CI:0.78-0.89),0.91(95%CI:0.87-0.94),and 0.81(95%CI:0.76-0.86),and 0.81(95%CI:0.75-0.86),0.88(95%CI:0.82-0.92),and 0.80(95%CI:0.72-0.86),and specificities of 0.71(95%CI:0.60-0.80),0.75(95%CI:0.68-0.82),and 0.82(95%CI:0.70-0.89),and 0.73(95%CI:0.66-0.80),0.87(95%CI:0.81-0.92),and 0.72(95%CI:0.62-0.80),respectively.The corresponding positive likelihood ratios were 2.91,3.67,and 4.44,and 3.04,6.90,and2.83;the negative likelihood ratios were 0.22,0.12,and 0.23,and 0.26,0.14,and 0.28;the diagnostic odds ratios were 13.01,30.98,and 19.58,and 11.93,49.99,and 10.00.CT scanner is the source of heterogeneity.There was no significant difference in diagnostic threshold effects(P>0.05)or publication bias(P>0.05).CONCLUSION Based on the meta-analysis of observational studies,it is suggested that CT imaging,a non-invasive diagnostic method,is the best choice for the diagnosis of EV and prediction of HREV in cirrhotic patients compared with LSM and MRI.展开更多
Inflammatory fibroid polyp(IFP) is a rare benign lesion of the gastrointestinal tract. We report a case of computed tomography(CT) imaging finding of a gastric IFP with massive fibrosis. CT scans showed thickening of ...Inflammatory fibroid polyp(IFP) is a rare benign lesion of the gastrointestinal tract. We report a case of computed tomography(CT) imaging finding of a gastric IFP with massive fibrosis. CT scans showed thickening of submucosal layer with overlying mucosal hyperenhancement in the gastric antrum. The submucosal layer showed increased enhancement on delayed phase imaging. An antrectomy with gastroduodenostomy was performed because gastric cancer was suspected, particularly signet ring cell carcinoma. The histopathological diagnosis was an IFP with massive fibrosis. The authors suggest that when the submucosal layer of the gastric wall is markedly thickened with delayed enhancement and preservation of the mucosal layer, an IFP with massive fibrosis should be considered in the differential diagnosis.展开更多
Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the ...Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the Schottky barrier diode detector in the terahertz frequency band （90–140GHz）. Based on the computed tomography technique, the three-dimensional image of a ceramic sample is reconstructed successfully by stacking the slices at different heights. The imaging results not only indicate the ability of terahertz wave in the non-invasive sensing and non-destructive inspection applications, but also prove the effectiveness and superiority of the uni-traveling-carrier photodiode as a terahertz source in the imaging applications.展开更多
Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learni...Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.展开更多
Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morp...Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morphology,and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task.To address these challenges,we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images.First,a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network,enhancing pancreatic feature extraction and improving localization accuracy.Second,a Multi-Scale Feature Extraction module is embedded into the network to expand the receptive field while preserving feature map resolution,mitigate feature degradation caused by network depth,and maintain awareness of pancreatic anatomical structures.Third,a Dimensional Squeeze Attention module is designed to reduce interference from adjacent organs and highlight useful pancreatic features through spatial-channel interaction,thereby enhancing sensitivity to small targets.Finally,a hybrid loss function combining Dice loss and Focal loss is employed to alleviate class imbalance issues.Extensive evaluation on two public datasets(NIH and MSD)shows that the proposed RPMS-DSAUnet achieves Dice Similarity Coefficients of 85.51%and 80.91%,with corresponding Intersection over Union(IoU)scores of 74.93%and 67.94%on each dataset,respectively.Experimental results demonstrate superior performance of the proposed model over baseline methods and state-of-the-art approaches,validating its effectiveness for CT-based pancreas segmentation.展开更多
Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of ...Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of urinary system,some problems remain unsolved completely A nine-layer Convolutional Neural Network(CNN)is proposed in this paper to classify the renal Computed Tomography(CT)images.Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07±1.67%.Although our renal CT data is not very large,we do augment the training data by affine,translating,rotating and scaling geometric transformation and gamma,noise transformation in color space.Experimental results validate the Data Augmentation(DA)on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%.This proposed algorithm gives a promising solution to help clinical doctors automatically recognize the abnormal images faster than manual judgment and more accurately than previous methods.展开更多
Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing.We propose a novel approach of automatically identifying region of interest in Computed Tomography Image...Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing.We propose a novel approach of automatically identifying region of interest in Computed Tomography Image(CT)images based on temporal and spatial data.Our method is a 3 stages approach,1)We extract organ features from the CT images by adopting the Hounsfield filter.2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’area and automatically detect a seed point.3)We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest,given a seed point as our input.We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images.With the results presented in this research work,our proposed novel sequence approach method has been proven to be superior in terms of accuracy,automation and robustness.展开更多
This paper proposes a novel exemplar- based method for reducing noise in computed tomography （CT） images. In the proposed method, denoising is performed on each block with the help of a given database of standard im...This paper proposes a novel exemplar- based method for reducing noise in computed tomography （CT） images. In the proposed method, denoising is performed on each block with the help of a given database of standard image blocks. For each noisy block, its denoised version is the best sparse positive linear combination of the blocks in the database. We formulate the problem as a constrained optimization problem such that the solution is the denoised block. Experimental results demonstrate the good performance of the proposed method over current state-of-the-art denoising methods, in terms of both objective and subjective evaluations.展开更多
The heterogeneity and invasiveness of cancer cells pose serious challenges in cancer diagnosis and treatment.Advancements and innovations in metal-based nanomedicines provide novel avenues for addressing these challen...The heterogeneity and invasiveness of cancer cells pose serious challenges in cancer diagnosis and treatment.Advancements and innovations in metal-based nanomedicines provide novel avenues for addressing these challenges.Metal-based nanomedicines possess unique physicochemical properties that enable their interaction with living organisms,thereby inducing complex biological responses.These nanomaterials have been extensively used to enhance the contrast and sensitivity of cancer imaging and to amplify the distinction between cancerous and healthy tissues.Moreover,these nanomaterials can effectively combat a wide spectrum of cancers through various methods,including drug delivery,radiotherapy,photothermal therapy(PTT),photodynamic therapy(PDT),sonodynamic therapy(SDT),biocatalytic therapy,ion interference therapy(IIT),and immunotherapy.Currently,there is still a need for a comprehensive summary on the metal-based nanomaterials for cancer diagnosis and treatment.Herein,we present a systematic and complete overview of action mechanisms and the applications of metal-based nanomaterials in cancer theranostics.A summary of common strategies for synthesizing and modifying metal-based nanomedicines is presented,and their biosafety is analyzed.Then,the latest developments in their applications for cancer imaging and anticancer treatment are provided.Finally,the key technical challenges and reasonable perspectives of metal-based nanomedicines for cancer theranostics in clinical applications are discussed.展开更多
Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our ...Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme.展开更多
OBJECTIVE:Since the coronavirus disease 2019(COVID-19)outbreak in Wuhan in 2019,the virus has spread rapidly.We investigated the clinical and computed tomography(CT)characteristics of different clinical types of COVID...OBJECTIVE:Since the coronavirus disease 2019(COVID-19)outbreak in Wuhan in 2019,the virus has spread rapidly.We investigated the clinical and computed tomography(CT)characteristics of different clinical types of COVID-19.MATERIALS AND METHODS:We retrospectively analyzed clinical and chest CT findings of 89 reverse transcription polymerase chain reaction confirmed cases from five medical centers in China.All the patients were classified into the common(n=65),severe(n=18),or fatal(n=6)type.CT features included lesion distribution,location,size,shape,edge,density,and the ratio of lung lesions to extra-pulmonary lesions.A COVID-19 chest CT analysis tool(uAI-discover-COVID-19)was used to calculate the number of infections from the chest CT images.RESULTS:Fatal type COVID-19 is more common in older men,with a median age of 65 years.Fever was more common in the severe and fatal type COVID-19 patients than in the common type patients.Patients with fatal type COVID-19 were more likely to have underlying diseases.On CT examination,common type COVID-19 showed bilateral(68%),patchy(83%),ground-glass opacity(48%),or mixed(46%)lesions.Severe and fatal type COVID-19 showed bilateral multiple mixed density lesions(56%).The infection ratio(IR)increased in the common type(2.4[4.3]),severe type(15.7[14.3]),and fatal type(36.9[14.2]).The IR in the inferior lobe of both lungs was statistically different from that of other lobes in common and severe type patients(P<0.05).However,in the fatal type group,only the IR in the right inferior lung(RIL)was statistically different from that in the right superior lung(RUL),right middle lung(RML),and the left superior lung(LSL)(P<0.05).CONCLUSION:The CT findings and clinical features of the various clinical types of COVID-19 pneumonia are different.Chest CT findings have unique characteristics in the different clinical types,which can facilitate an early diagnosis and evaluate the clinical course and severity of COVID-19.展开更多
基金Project supported by the National Basic Research Program of China(Grant No.2006CB7057005)the National High Technology Research and Development Program of China(Grant No.2009AA012200)the National Natural Science Foundation of China (Grant No.60672104)
文摘With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.
基金the Sichuan Science and Technology Department Research and Development Key Project(No.21ZDYF3607)the Weining Cloud Hospital Based AI Medical Software System Service and Demo Project(No.2019K0JTS0159)the China Postdoctoral Science Foundation(No.2020T130137ZX)。
文摘Deep learning based analyses of computed tomography(CT)images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19.Two ensemble strategies are considered:the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation;voting strategy.A database containing 8347 CT slices of COVID-19,common pneumonia and normal subjects was used as training and testing sets.Results show that the novel method can reach a high accuracy of 99.37%(recall:0.9981;precision:0.9893),with an increase of about 7% in comparison to single-component models.And the average test accuracy is 95.62%(recall:0.9587;precision:0.9559),with a corresponding increase of 5.2%.Compared with several latest deep learning models on the identical test set,our method made an accuracy improvement up to 10.88%.The proposed method may be a promising solution for the diagnosis of COVID-19.
文摘It is not easy to reduce the metal artifacts of computed tomography images.However,the pixel values inside the metal artifact regions vary smoothly,while those on the borders of the metal and the bone regions vary sharply.When the Canny operation by adaptive thresholding is conducted on the raw image,the almost continuous edges can be formed obviously on the borders of the metal and the bone regions,but this kind of information cannot be formed for the metal artifact regions.In this paper,by searching the closed areas formed by the border edges of the bone regions in the Canny image,the metal artifact regions,which are very difficult to discriminate only by intensity thresholding,can be excluded effectively.A novel prior image-based method is thus developed for metal artifact reduction.The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image.The method is suitable for the clinical application.
基金Project supported by the National Natural Science Foundation of China(Grant No.61372172)
文摘The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.
基金Supported by Taiwan National Science and Technology Council,No.NSTC114-2221-E-035-036Taichung Veterans General Hospital/Feng Chia University Joint Research Program,No.TCVGH-FCU1148207.
文摘BACKGROUND Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options.The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in esophageal cancer,but most follow-up computed tomography(CT)scans do not extend to L3 and limiting its utility.Radiomics has emerged as a powerful tool for extracting prognostic information from medical images.AIM To evaluate the influential features for esophageal cancer prognosis by integrating radiomic and body compositionbased indices of skeletal muscle and adipose tissue at the T12 level from both pretreatment and follow-up CT images,in order to assess their value in predicting overall survival(OS).METHODS This retrospective study included 212 esophageal cancer patients who underwent concurrent chemoradiotherapy,with both pretreatment and follow-up chest CT scans available.Body organ analysis(BOA)and radiomic features were extracted from skeletal muscle and adipose tissue at the T12 level using automated tools.Four feature subsets(no-radiomics,pretreatment only,follow-up only,and combined inputs)were developed using logistic regression(LR)with least absolute shrinkage and selection operator for feature selection,followed by Cox regression.Prognostic models-including nomogram,support vector classifier,LR,and extra trees classifier-were constructed to predict 1-,2-,and 3-year OS.RESULTS The model integrating both BOA and radiomics from pretreatment and follow-up CT,combined with clinical data,achieved the best performance for 2-year OS prediction,with an area under the time-dependent receiver operating characteristic curve of 0.91,sensitivity of 0.81,and specificity of 0.88 using the LR model.The most predictive features included both clinical variables,body composition indices,and radiomic features,particularly from follow-up VAT.Follow-up imaging contributed significantly to model performance,reinforcing its value in treatment response evaluation.CONCLUSION This is the first study to demonstrate that BOA indices and their corresponding radiomics at the T12-level from both pretreatment and follow-up CT scans-combined with clinical data-can provide accurate prognostic information for esophageal cancer.This approach offers a practical alternative when L3-level imaging is unavailable and supports the clinical integration of automated T12-based imaging biomarkers.The integration of these imaging features with clinical parameters enhances the prediction of survival outcomes and contributes to non-invasive,personalized treatment planning.
文摘BACKGROUND Reliable preoperative detection of lymph node metastasis(LNM)in pancreatic cancer remains elusive:Conventional computed tomography(CT)underestimates micrometastases,and carbohydrate antigen 19-9 is hampered by low specificity.The neutrophil-albumin ratio(NAR)simultaneously reflects systemic inflammation and nutritional depletion,but its contribution to LNM prediction in pancreatic cancer is unexplored.We hypothesised that integrating NAR with multiphase CT findings would significantly improve the accuracy of preoperative LNM assessment in patients undergoing curative-intent resection.AIM To determine whether preoperative NAR plus multi-phase CT reliably predicts nodal metastasis in pancreatic cancer.METHODS In this single-centre retrospective cohort study(February 2022 to February 2025,Ordos Central Hospital,China),129 consecutive patients undergoing curative pancreatic resection were histologically classified as LNM+(n=61)and LNM-(n=68).Preoperative NAR and platelet-albumin ratio(PAR)were calculated;optimal cut-offs were determined with X-tile.Multi-phase CT images were re-reviewed by two blinded radiologists.Independent predictors of nodal metastasis were identified by multivariate logistic regression,and model performance was evaluated with receiver operating characteristic(ROC)analysis.RESULTS Between the two cohorts,univariate comparison revealed significant divergence in age,tumour diameter,concomitant hemangioma thrombosis,PAR,NAR,and CT-detected nodal status(P<0.05).Subsequent multivariate modelling identified hemangioma thrombosis,PAR above 6.35,NAR exceeding 0.13,and radiologically positive lymph nodes as independent predictors of nodal metastasis(P<0.05).ROC evaluation indicated that the NAR-plus-CT-nodes model(model 1)reached an area under the curve(AUC)of 0.758,whereas the fourvariable composite(model 3)achieved the best performance with an AUC of 0.830(95%CI:0.753-0.890),sensitivity 83.61%,and specificity 67.65%.CONCLUSION The model 3(NAR>0.13,PAR>6.35,CT nodal positivity,hemangioma thrombosis)provides robust,clinically actionable preoperative identification of pancreatic cancer patients at high risk of LNM.
基金Supported by the National Natural Science Foundation of China,No.81860047the Natural Science Foundation of Gansu Province,No.22JR5RA650+1 种基金Key Science and Technology Program in Gansu Province,No.21YF5FA016Gansu Provincial Hospital Scientific Research Foundation,No.23GSSYD-12.
文摘BACKGROUND Colorectal cancer(CRC)is a leading cause of cancer-related death globally,with the tumor immune microenvironment(TIME)influencing prognosis and immunotherapy response.Current TIME evaluation relies on invasive biopsies,limiting its clinical application.This study hypothesized that computed tomography(CT)-based deep learning(DL)radiomics models can non-invasively predict key TIME biomarkers:Tumor-stroma ratio(TSR),tumor-infiltrating lymphocytes(TILs),and immune score(IS).AIM To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.METHODS In this retrospective study,preoperative CT images of 315 pathologically confirmed CRC patients(220 in training cohort and 95 in validation cohort)were analyzed.Manually delineated regions of interest were used to extract DL features.Predictive models(DenseNet-121/169)for TSR,TILs,IS,and TIME classification were constructed.Performance was evaluated via receiver operating characteristic curves,calibration curves,and decision curve analysis(DCA).RESULTS The DL-DenseNet-169 model achieved area under the curve(AUC)values of 0.892[95%confidence interval(CI):0.828-0.957]for TSR and 0.772(95%CI:0.674-0.870)for TIME score.The DenseNet-121 model yielded AUC values of 0.851(95%CI:0.768-0.933)for TILs and 0.852(95%CI:0.775-0.928)for IS.Calibration curves demonstrated strong prediction-observation agreement,and DCA confirmed clinical utility across threshold probabilities(P<0.05 for all models).CONCLUSION CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation,enabling personalized immunotherapy strategies in CRC management.
文摘BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomography imaging(GSI)to improve diagnostic accuracy in lymph node metastasis(LNM)assessment.AIM To extensively investigate the clinical utility of GSI in the preoperative assessment of CRC.METHODS The subject population included 200 patients with CRC who were admitted to Zibo Central Hospital from January 2022 to December 2023.All patients underwent dual-phase contrast-enhanced scans in the arterial and venous phases using GSI before surgical intervention.During the research,meticulous quantification was conducted regarding the number of patients with CRC with LNM as well as the exact count of metastatic lymph nodes.Moreover,for both metastatic and non-metastatic lymph nodes,the short diameter at the maximum crosssectional area(covering the axial,sagittal,and coronal planes),morphological features(including manifestations such as margin blurring,aggregation,and enhancement),and spectral parameters in the arterial and venous phases[specifically iodine concentration(IC),normalized IC(NIC),and the slope of the spectral curve(λHU)]were measured and recorded,and a comparative analysis was conducted.The diagnostic efficacy of each index with differences was systematically assessed using the receiver operating characteristic(ROC)curve.Concurrently,receiver operating characteristic curves were constructed for LNM screening based on the short diameter at the maximum cross-sectional area of lymph nodes and each spectral parameter in the arterial and venous phases.RESULTS The area under the curve of GSI for diagnosing LNM in patients with CRC can reach 0.897,with sensitivity,specificity,and accuracy of 92.59%,85.87%,and 89.50%,respectively.A total of 265 lymph nodes were analyzed from the 200 participants with CRC,with metastatic lymph nodes accounting for 56.60%.Compared with nonmetastatic lymph nodes,the short diameters of metastatic lymph nodes in the axial,sagittal,and coronal planes were significantly increased,whereas the IC values in the arterial and venous phases,the NIC value in the arterial phase,and theλHU values in the arterial and venous phases were significantly decreased.The short axial,sagittal,and coronal diameters,arterial-phase IC,venous-phase IC,arterial-phase NIC,arterial-phaseλHU,and venousphaseλHU for diagnosing metastatic lymph nodes demonstrated area under the curve values of 0.631,0.681,0.659,0.862,0.808,0.831,0.801,and 0.706,respectively.CONCLUSION GSI exhibits substantial clinical significance in the preoperative assessment of CRC.Among the parameters assessed,the arterial-phase IC demonstrates the most outstanding diagnostic performance,effectively improving the diagnostic efficacy for preoperative LNM in CRC.
基金Supported by the State Key Projects Specialized on Infectious Diseases,No.2017ZX10203202–004Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding,No.ZYLX201610+1 种基金Beijing Municipal Administration of Hospitals’Ascent Plan,No.DFL20151602Digestive Medical Coordinated Development Center of Beijing Hospitals Authority,No.XXT24.
文摘BACKGROUND Computed tomography(CT),liver stiffness measurement(LSM),and magnetic resonance imaging(MRI)are non-invasive diagnostic methods for esophageal varices(EV)and for the prediction of high-bleeding-risk EV(HREV)in cirrhotic patients.However,the clinical use of these methods is controversial.AIM To evaluate the accuracy of LSM,CT,and MRI in diagnosing EV and predicting HREV in cirrhotic patients.METHODS We performed literature searches in multiple databases,including Pub Med,Embase,Cochrane,CNKI,and Wanfang databases,for articles that evaluated the accuracy of LSM,CT,and MRI as candidates for the diagnosis of EV and prediction of HREV in cirrhotic patients.Summary sensitivity and specificity,positive likelihood ratio and negative likelihood ratio,diagnostic odds ratio,and the areas under the summary receiver operating characteristic curves were analyzed.The quality of the articles was assessed using the quality assessment of diagnostic accuracy studies-2 tool.Heterogeneity was examined by Q-statistic test and I2 index,and sources of heterogeneity were explored using metaregression and subgroup analysis.Publication bias was evaluated using Deek’s funnel plot.All statistical analyses were conducted using Stata12.0,Meta Disc1.4,and Rev Man5.3.RESULTS Overall,18,17,and 7 relevant articles on the accuracy of LSM,CT,and MRI in evaluating EV and HREV were retrieved.A significant heterogeneity was observed in all analyses(P<0.05).The areas under the summary receiver operating characteristic curves of LSM,CT,and MRI in diagnosing EV and predicting HREV were 0.86(95%confidence interval[CI]:0.83-0.89),0.91(95%CI:0.88-0.93),and 0.86(95%CI:0.83-0.89),and 0.85(95%CI:0.81-0.88),0.94(95%CI:0.91-0.96),and 0.83(95%CI:0.79-0.86),respectively,with sensitivities of 0.84(95%CI:0.78-0.89),0.91(95%CI:0.87-0.94),and 0.81(95%CI:0.76-0.86),and 0.81(95%CI:0.75-0.86),0.88(95%CI:0.82-0.92),and 0.80(95%CI:0.72-0.86),and specificities of 0.71(95%CI:0.60-0.80),0.75(95%CI:0.68-0.82),and 0.82(95%CI:0.70-0.89),and 0.73(95%CI:0.66-0.80),0.87(95%CI:0.81-0.92),and 0.72(95%CI:0.62-0.80),respectively.The corresponding positive likelihood ratios were 2.91,3.67,and 4.44,and 3.04,6.90,and2.83;the negative likelihood ratios were 0.22,0.12,and 0.23,and 0.26,0.14,and 0.28;the diagnostic odds ratios were 13.01,30.98,and 19.58,and 11.93,49.99,and 10.00.CT scanner is the source of heterogeneity.There was no significant difference in diagnostic threshold effects(P>0.05)or publication bias(P>0.05).CONCLUSION Based on the meta-analysis of observational studies,it is suggested that CT imaging,a non-invasive diagnostic method,is the best choice for the diagnosis of EV and prediction of HREV in cirrhotic patients compared with LSM and MRI.
文摘Inflammatory fibroid polyp(IFP) is a rare benign lesion of the gastrointestinal tract. We report a case of computed tomography(CT) imaging finding of a gastric IFP with massive fibrosis. CT scans showed thickening of submucosal layer with overlying mucosal hyperenhancement in the gastric antrum. The submucosal layer showed increased enhancement on delayed phase imaging. An antrectomy with gastroduodenostomy was performed because gastric cancer was suspected, particularly signet ring cell carcinoma. The histopathological diagnosis was an IFP with massive fibrosis. The authors suggest that when the submucosal layer of the gastric wall is markedly thickened with delayed enhancement and preservation of the mucosal layer, an IFP with massive fibrosis should be considered in the differential diagnosis.
基金Supported by the Hundred Talents Program of Chinese Academy of Sciencesthe National Basic Research Program of China under Grant No 2014CB339803+2 种基金the Major National Development Project of Scientific Instrument and Equipment under Grant No2011YQ150021the National Natural Science Foundation of China under Grant Nos 61575214,61574155,61404149 and 61404150the Shanghai Municipal Commission of Science and Technology under Grant Nos 14530711300,15560722000 and 15ZR1447500
文摘Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the Schottky barrier diode detector in the terahertz frequency band （90–140GHz）. Based on the computed tomography technique, the three-dimensional image of a ceramic sample is reconstructed successfully by stacking the slices at different heights. The imaging results not only indicate the ability of terahertz wave in the non-invasive sensing and non-destructive inspection applications, but also prove the effectiveness and superiority of the uni-traveling-carrier photodiode as a terahertz source in the imaging applications.
基金the Natural Science Foundation of Zhejiang Province(No.LQ20F020024)。
文摘Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.
基金supported by the National Natural and Science Foundation of China under Grant No.12301662Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ21F030019.
文摘Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morphology,and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task.To address these challenges,we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images.First,a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network,enhancing pancreatic feature extraction and improving localization accuracy.Second,a Multi-Scale Feature Extraction module is embedded into the network to expand the receptive field while preserving feature map resolution,mitigate feature degradation caused by network depth,and maintain awareness of pancreatic anatomical structures.Third,a Dimensional Squeeze Attention module is designed to reduce interference from adjacent organs and highlight useful pancreatic features through spatial-channel interaction,thereby enhancing sensitivity to small targets.Finally,a hybrid loss function combining Dice loss and Focal loss is employed to alleviate class imbalance issues.Extensive evaluation on two public datasets(NIH and MSD)shows that the proposed RPMS-DSAUnet achieves Dice Similarity Coefficients of 85.51%and 80.91%,with corresponding Intersection over Union(IoU)scores of 74.93%and 67.94%on each dataset,respectively.Experimental results demonstrate superior performance of the proposed model over baseline methods and state-of-the-art approaches,validating its effectiveness for CT-based pancreas segmentation.
基金This study was supported by National Educational Science Plan Foundation“in 13th Five-Year”(DIA170375),ChinaGuangxi Key Laboratory of Trusted Software(kx201901)British Heart Foundation Accelerator Award,UK.
文摘Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of urinary system,some problems remain unsolved completely A nine-layer Convolutional Neural Network(CNN)is proposed in this paper to classify the renal Computed Tomography(CT)images.Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07±1.67%.Although our renal CT data is not very large,we do augment the training data by affine,translating,rotating and scaling geometric transformation and gamma,noise transformation in color space.Experimental results validate the Data Augmentation(DA)on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%.This proposed algorithm gives a promising solution to help clinical doctors automatically recognize the abnormal images faster than manual judgment and more accurately than previous methods.
基金This work was supported by the National Natural Science Foundation of China(Nos.61772242,61572239,61402204)Research Fund for Advanced Talents of Jiangsu University(14JDG141)+2 种基金Qing Lan ProjectChina Postdoctoral Science Foundation(No.2017M611737)Zhenjiang social development project(SH2016029).
文摘Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing.We propose a novel approach of automatically identifying region of interest in Computed Tomography Image(CT)images based on temporal and spatial data.Our method is a 3 stages approach,1)We extract organ features from the CT images by adopting the Hounsfield filter.2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’area and automatically detect a seed point.3)We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest,given a seed point as our input.We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images.With the results presented in this research work,our proposed novel sequence approach method has been proven to be superior in terms of accuracy,automation and robustness.
文摘This paper proposes a novel exemplar- based method for reducing noise in computed tomography （CT） images. In the proposed method, denoising is performed on each block with the help of a given database of standard image blocks. For each noisy block, its denoised version is the best sparse positive linear combination of the blocks in the database. We formulate the problem as a constrained optimization problem such that the solution is the denoised block. Experimental results demonstrate the good performance of the proposed method over current state-of-the-art denoising methods, in terms of both objective and subjective evaluations.
基金supported by the National Natural Science Foundation of China(82071981)the Program of Youth Science and Technology Innovation and Entrepreneurship Outstanding Talents(Team)of Jilin Province,China(20230508063RC)+3 种基金the Excellent Youth Training Foundation of Jilin University,China(419080520665)the Innovation and Entrepreneurship Talent Funding Program of Jilin Province,Chinathe Health Special Project of the Finance Department of Jilin Province,Chinathe Graduate Innovation Fund of Jilin University,China(2025CX297)。
文摘The heterogeneity and invasiveness of cancer cells pose serious challenges in cancer diagnosis and treatment.Advancements and innovations in metal-based nanomedicines provide novel avenues for addressing these challenges.Metal-based nanomedicines possess unique physicochemical properties that enable their interaction with living organisms,thereby inducing complex biological responses.These nanomaterials have been extensively used to enhance the contrast and sensitivity of cancer imaging and to amplify the distinction between cancerous and healthy tissues.Moreover,these nanomaterials can effectively combat a wide spectrum of cancers through various methods,including drug delivery,radiotherapy,photothermal therapy(PTT),photodynamic therapy(PDT),sonodynamic therapy(SDT),biocatalytic therapy,ion interference therapy(IIT),and immunotherapy.Currently,there is still a need for a comprehensive summary on the metal-based nanomaterials for cancer diagnosis and treatment.Herein,we present a systematic and complete overview of action mechanisms and the applications of metal-based nanomaterials in cancer theranostics.A summary of common strategies for synthesizing and modifying metal-based nanomedicines is presented,and their biosafety is analyzed.Then,the latest developments in their applications for cancer imaging and anticancer treatment are provided.Finally,the key technical challenges and reasonable perspectives of metal-based nanomedicines for cancer theranostics in clinical applications are discussed.
基金the National Natural Science Foundation of China (No. 30770589)
文摘Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme.
基金supported by Key Emergency Project of Pneumonia Epidemic of novel coronavirus infectionNational Natural Science Foundation of China(81671671)the Key R&D projects in Hunan Province(2019SK2131).
文摘OBJECTIVE:Since the coronavirus disease 2019(COVID-19)outbreak in Wuhan in 2019,the virus has spread rapidly.We investigated the clinical and computed tomography(CT)characteristics of different clinical types of COVID-19.MATERIALS AND METHODS:We retrospectively analyzed clinical and chest CT findings of 89 reverse transcription polymerase chain reaction confirmed cases from five medical centers in China.All the patients were classified into the common(n=65),severe(n=18),or fatal(n=6)type.CT features included lesion distribution,location,size,shape,edge,density,and the ratio of lung lesions to extra-pulmonary lesions.A COVID-19 chest CT analysis tool(uAI-discover-COVID-19)was used to calculate the number of infections from the chest CT images.RESULTS:Fatal type COVID-19 is more common in older men,with a median age of 65 years.Fever was more common in the severe and fatal type COVID-19 patients than in the common type patients.Patients with fatal type COVID-19 were more likely to have underlying diseases.On CT examination,common type COVID-19 showed bilateral(68%),patchy(83%),ground-glass opacity(48%),or mixed(46%)lesions.Severe and fatal type COVID-19 showed bilateral multiple mixed density lesions(56%).The infection ratio(IR)increased in the common type(2.4[4.3]),severe type(15.7[14.3]),and fatal type(36.9[14.2]).The IR in the inferior lobe of both lungs was statistically different from that of other lobes in common and severe type patients(P<0.05).However,in the fatal type group,only the IR in the right inferior lung(RIL)was statistically different from that in the right superior lung(RUL),right middle lung(RML),and the left superior lung(LSL)(P<0.05).CONCLUSION:The CT findings and clinical features of the various clinical types of COVID-19 pneumonia are different.Chest CT findings have unique characteristics in the different clinical types,which can facilitate an early diagnosis and evaluate the clinical course and severity of COVID-19.
