Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for i...Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for improving predictive performance,while explainable machine learning techniques support transparency and biomarker discovery.Methods:To propose a hybrid explainable machine learning framework that combines stability-guided multi-source(SGMS)feature selection with classification models for gene expression-based cancer prediction and biomarker identification.SGMS integrates Mutual Information,F-statistic,and random forest(RF)importance to select informative genes.These features are used to train classifiers,including novel elasticnet logistic regression(NEN-LR),RF,and Support Vector Machine(SVM).Performance is evaluated using accuracy,precision,recall,F1-score,and Matthews correlation coefficient(MCC).SHapley Additive exPlanations(SHAP)values are used to interpret gene-level contributions,and co-expression networks help identify functional gene modules.Results:The proposed NEN-LR classifier achieved the highest performance with 99.8%accuracy,99.9%precision,and 0.997 MCC using the top 200 SGMS-selected genes.Biomarker discovery identified both class-specific and shared genes across five cancer types,with top genes like gene_230,gene_5380,and gene_18570 consistently appearing across multiple classes.Visualization tools,including heatmaps,Venn diagrams,and co-expression networks,were used to interpret expression dynamics and regulatory patterns,enhancing the biological relevance of findings.SHAP analysis revealed top biomarkers with strong predictive influence,while co-expression clustering uncovered biologically meaningful gene modules.Other models also showed marked improvement using SGMS-selected features.Conclusion:The proposed framework successfully integrates feature selection,interpretable classification,and biomarker discovery,providing a powerful tool for precision oncology and molecular diagnostics.展开更多
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classificatio...Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection ...Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.展开更多
The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousa...The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.展开更多
Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying...Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.展开更多
Context/Objectives: Hepatocellular carcinoma occurs mainly and increasingly in developing countries, where the prognosis is particularly poor. The Barcelona Clinic Liver Cancer classification is used to guide the trea...Context/Objectives: Hepatocellular carcinoma occurs mainly and increasingly in developing countries, where the prognosis is particularly poor. The Barcelona Clinic Liver Cancer classification is used to guide the treatment of hepatocellular carcinoma. The aim of this retrospective study was to describe the Barcelona Clinic Liver Cancer classification and the treatment of hepatocellular carcinoma in a University Hospital in Côte d’Ivoire. Methods: Patients with hepatocellular carcinoma hospitalized in the hepato-gastroenterology unit of the University Hospital of Yopougon from 01 January 2012 to 30 June 2017 were included. The diagnosis of hepatocellular carcinoma was based on the presence of hepatic nodules on the abdominal ultrasound scan, typical images with the helical scanner associated or not with an increase of the α-fetoprotein higher than 200 ng/ml or with histology. Demographic, clinical, biological and radiological data were determined at the time of diagnosis. Patients were classified according to the Barcelona Clinic Liver Cancer classification. Their treatment was specified. Results: There were 258 patients whose median age was 48.1 years. Viral hepatitis B virus was the primary cause of hepatocellular carcinoma in 64.7% of cases. The severity of the underlying cirrhosis was Child-Pugh A in 12.1%, B in 63.6% and C in 24.3% of cases. The median size of the tumor was 63 mm. The α-fetoprotein level was higher than 200 mg/ml in 56.03% of cases. The Eastern Cooperative Oncology Group (ECOG)/World Health Organization (WHO) system was ≥2 in 82.9%. The Barcelona Clinic Liver Cancer classification was A in 1.3%, B in 0%, C in 55.2% and D in 43.5% of patients. There was no transplantation or hepatic resection. Very few patients (1.9%) received radio-frequency curative therapy. The treatment was predominantly symptomatic in 97.8% of patients. During hospitalization 43.7% of patients died. Conclusion: Hepatocellular carcinoma occurs on a liver with severe cirrhosis at a late stage. This does not allow cure treatment and explains a high mortality rate during hospitalization. Hepatitis B virus is the main risk factor and immunization at birth will reduce the incidence of this cancer in Africa.展开更多
Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distan...Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.展开更多
Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine lea...Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.展开更多
The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Altho...The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.展开更多
One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer cla...One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories： direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis arc also discussed.展开更多
This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images into four categori...This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images into four categories: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma (LULC), and normal. Although CNNs have made significant advancements in medical imaging, their limited capacity to capture long-range dependencies has led to the exploration of ViTs, which leverage self-attention mechanisms for a more comprehensive global understanding of images. The study utilized a dataset of 748 lung CT images to train both models with standardized input sizes, assessing their performance through conventional metrics—accuracy, precision, recall, F1 score, specificity, and AUC—as well as cross entropy, a novel metric for evaluating prediction uncertainty. Both models achieved similar accuracy rates (95%), with ViT demonstrating a slight edge over ResNet50 in precision and F1 scores for specific classes. However, ResNet50 exhibited higher recall for LULC, indicating fewer missed cases. Cross entropy analysis showed that the ViT model had lower average uncertainty, particularly in the LUAD, Normal, and LUSC classes, compared to ResNet50. This finding suggests that ViT predictions are generally more reliable, though ResNet50 performed better for LULC. The study underscores that accuracy alone is insufficient for model comparison, as cross entropy offers deeper insights into the reliability and confidence of model predictions. The results highlight the importance of incorporating cross entropy alongside traditional metrics for a more comprehensive evaluation of deep learning models in medical image classification, providing a nuanced understanding of their performance and reliability. While the ViT outperformed the CNN-based ResNet50 in lung cancer classification based on cross-entropy values, the performance differences were minor and may not hold clinical significance. Therefore, it may be premature to consider replacing CNNs with ViTs in this specific application.展开更多
Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradi...Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradientinformation, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimizationalgorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets.Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks bybalancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitationability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete featureselection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subsetvariance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches.This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the globalexploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. Theobjective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. Theproposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC),Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), andProstate Tumor (PT).The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%,96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively.These results validate the effectiveness of the hybrid approachin addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier.展开更多
This study aimed to assess the role of the National Comprehensive Cancer Network （NCCN） risk classification in predicting biochemical recurrence （BCR） after radical prostatectomy （RP） in Chinese prostate cancer ...This study aimed to assess the role of the National Comprehensive Cancer Network （NCCN） risk classification in predicting biochemical recurrence （BCR） after radical prostatectomy （RP） in Chinese prostate cancer patients. We included a consecutive cohort of 385 patients with prostate cancer who underwent RP at Fudan University Shanghai Cancer Center （Shanghai, China） from March 2011 to December 2014. Gleason grade groups were applied at analysis according to the 2014 International Society of Urological Pathology Consensus. Risk groups were stratified according to the NCCN Clinical Practice Guidelines in Oncology： Prostate Cancer version 1, 2017. All 385 patients were divided into BCR and non-BCR groups. The clinicopathological characteristics were compared using an independent sample t-test, Chi-squared test, and Fisher＇s exact test. BCR-free survival was compared using the log-rank test and multivariable Cox proportional hazard analysis. During median follow-up of 48 months （range： 1-78 months）, 31 （8.05%） patients experienced BCR. The BCR group had higher prostate-specific antigen level at diagnosis （46.54 ± 39.58 ng m1-1 vs 21.02 ± 21.06 ng ml-1, P= 0.001）, more advanced pT stage （P= 0.002）, and higher pN1 rate （P〈 0.001）. NCCN risk classification was a significant predictor of BCR {P = 0.0006） and BCR-free survival （P = 0.003） after RP. As NCCN risk level increased, there was a significant decreasing trend in BCR-free survival rate （Ptrend = 0.0002）. This study confirmed and validated that NCCN risk classification was a significant predictor of BCR and BCR-free survival after RP.展开更多
BACKGROUND Rectal cancer is characterized by more local recurrence(LR)and lung metastasis than colon cancer.However,the diagnosis of rectal cancer is not standardized as there is no global consensus on its definition ...BACKGROUND Rectal cancer is characterized by more local recurrence(LR)and lung metastasis than colon cancer.However,the diagnosis of rectal cancer is not standardized as there is no global consensus on its definition and classification.The classification of rectal cancer differs between Japanese and Western guidelines.AIM To clarify the characteristics of rectal cancer by comparing the tumor location and characteristics of rectal cancer with those of colon cancer according to each set of guidelines.METHODS A total of 958 patients with Stage II and III colorectal cancer were included in the analysis:607 with colon cancer and 351 with rectal cancer.Localization of rectal cancers was assessed by enema examination and rigid endoscopy.According to Japan guidelines,rectal cancer is classified as Rb(below the peritoneal inversion),Ra(between the inferior margin of second sacral vertebrae and Rb)or RS(between Ra and sacral promontory).RESULTS There were no significant differences between RS rectal cancer and colon cancer in the rates of liver and lung metastasis or LR.Lung metastasis and LR were significantly more common among Rb rectal cancer(in Japan)than in colon cancer(P=0.0043 and P=0.0002,respectively).Lung metastases and LR occurred at significantly higher rates in rectal cancer measuring≤12 cm and≤10 cm than in colon cancers(P=0.0117,P=0.0467,P=0.0036,P=0.0010).Finally,the rates of liver metastasis,lung metastasis,and LR in rectal cancers measuring 11 cm to 15 cm were 6.9%,2.8%,and 5.7%,respectively.These were equivalent to the rates in colon cancer.CONCLUSION High rectal cancer may be treated with the same treatment strategies as colon cancer.There was no difference in the classification of colorectal cancer between Japan and Western countries.展开更多
Dermatologists typically require extensive experience to accurately classify skin cancer.In recent years,the development of computer vision and machine learning has provided new methods for assisted diagnosis.Existing...Dermatologists typically require extensive experience to accurately classify skin cancer.In recent years,the development of computer vision and machine learning has provided new methods for assisted diagnosis.Existing skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin images.This paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for classification.To achieve this,a new function set and a new terminal set have been developed.The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature extraction.The performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP methods.The experimental results show that the new approach achieves significantly better or similar performance in most cases.Further analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.展开更多
This study presents an efficient feature selection method based on the Gower distance to enhance the accuracy and efficiency of standard classifiers on high-dimensional medical datasets.High-dimensional data poses sig...This study presents an efficient feature selection method based on the Gower distance to enhance the accuracy and efficiency of standard classifiers on high-dimensional medical datasets.High-dimensional data poses significant challenges for traditional classifiers due to feature redundancy or being irrelevant.The proposed method addresses these challenges by partitioning the dataset into blocks,calculating the Gower distance within each block,and selecting features based on their average similarity.Technically,the Gower distance normalizes the absolute difference between numerical features,ensuring that each feature contributes equally to the distance calculation.This normalization prevents features with larger scales from overshadowing those with smaller scales.This process facilitates the identification of features that exhibit high harmony and are the most relevant for classification.The proposed feature selection strategy significantly reduces dimensionality,retains the most relevant features,and improves model performance.Experimental results show that the accuracy for the classifiers including k-nearest neighbors(KNN),naive Bayes(NB),decision tree(DT),random forest(RF),support vector machine(SVM),and logistic regression(LR)was increased by 4.38%-7.02%.Besides,the reduction in the feature set size contributes to a considerable decrease in computational complexity and thus faster diagnosis speed.The execution time was averagely reduced by 77.82%for all samples and 76.45%for one sample.These results demonstrate that the proposed feature selection method shows enhanced performance on both prediction accuracy and diagnostic speed,making it a promising tool for real-time clinical decision-making and improving patient care outcomes.展开更多
The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization o...The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.展开更多
Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep lea...Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.展开更多
文摘Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for improving predictive performance,while explainable machine learning techniques support transparency and biomarker discovery.Methods:To propose a hybrid explainable machine learning framework that combines stability-guided multi-source(SGMS)feature selection with classification models for gene expression-based cancer prediction and biomarker identification.SGMS integrates Mutual Information,F-statistic,and random forest(RF)importance to select informative genes.These features are used to train classifiers,including novel elasticnet logistic regression(NEN-LR),RF,and Support Vector Machine(SVM).Performance is evaluated using accuracy,precision,recall,F1-score,and Matthews correlation coefficient(MCC).SHapley Additive exPlanations(SHAP)values are used to interpret gene-level contributions,and co-expression networks help identify functional gene modules.Results:The proposed NEN-LR classifier achieved the highest performance with 99.8%accuracy,99.9%precision,and 0.997 MCC using the top 200 SGMS-selected genes.Biomarker discovery identified both class-specific and shared genes across five cancer types,with top genes like gene_230,gene_5380,and gene_18570 consistently appearing across multiple classes.Visualization tools,including heatmaps,Venn diagrams,and co-expression networks,were used to interpret expression dynamics and regulatory patterns,enhancing the biological relevance of findings.SHAP analysis revealed top biomarkers with strong predictive influence,while co-expression clustering uncovered biologically meaningful gene modules.Other models also showed marked improvement using SGMS-selected features.Conclusion:The proposed framework successfully integrates feature selection,interpretable classification,and biomarker discovery,providing a powerful tool for precision oncology and molecular diagnostics.
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
基金supported by a grant from the National High-tech R&D Program (863 Program, No. 2006AA02Z331) to Liangbiao Chen
文摘Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
基金supported by Research Fund from University of Johannes-burg,Johannesburg City,South Africa.
文摘Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.
文摘The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.
基金This project was supported by the Deanship of Scientific Research at Prince SattamBin Abdulaziz University under research Project#(PSAU-2022/01/20287).
文摘Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.
文摘Context/Objectives: Hepatocellular carcinoma occurs mainly and increasingly in developing countries, where the prognosis is particularly poor. The Barcelona Clinic Liver Cancer classification is used to guide the treatment of hepatocellular carcinoma. The aim of this retrospective study was to describe the Barcelona Clinic Liver Cancer classification and the treatment of hepatocellular carcinoma in a University Hospital in Côte d’Ivoire. Methods: Patients with hepatocellular carcinoma hospitalized in the hepato-gastroenterology unit of the University Hospital of Yopougon from 01 January 2012 to 30 June 2017 were included. The diagnosis of hepatocellular carcinoma was based on the presence of hepatic nodules on the abdominal ultrasound scan, typical images with the helical scanner associated or not with an increase of the α-fetoprotein higher than 200 ng/ml or with histology. Demographic, clinical, biological and radiological data were determined at the time of diagnosis. Patients were classified according to the Barcelona Clinic Liver Cancer classification. Their treatment was specified. Results: There were 258 patients whose median age was 48.1 years. Viral hepatitis B virus was the primary cause of hepatocellular carcinoma in 64.7% of cases. The severity of the underlying cirrhosis was Child-Pugh A in 12.1%, B in 63.6% and C in 24.3% of cases. The median size of the tumor was 63 mm. The α-fetoprotein level was higher than 200 mg/ml in 56.03% of cases. The Eastern Cooperative Oncology Group (ECOG)/World Health Organization (WHO) system was ≥2 in 82.9%. The Barcelona Clinic Liver Cancer classification was A in 1.3%, B in 0%, C in 55.2% and D in 43.5% of patients. There was no transplantation or hepatic resection. Very few patients (1.9%) received radio-frequency curative therapy. The treatment was predominantly symptomatic in 97.8% of patients. During hospitalization 43.7% of patients died. Conclusion: Hepatocellular carcinoma occurs on a liver with severe cirrhosis at a late stage. This does not allow cure treatment and explains a high mortality rate during hospitalization. Hepatitis B virus is the main risk factor and immunization at birth will reduce the incidence of this cancer in Africa.
文摘Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.
文摘Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.
基金supported by the National Natural Science Foundation of China (20835002)International Science and Technology Cooperation Program of the Ministry of Science and Technology (MOST) of China (2008DFA32250)
文摘The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.
文摘One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories： direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis arc also discussed.
文摘This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images into four categories: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma (LULC), and normal. Although CNNs have made significant advancements in medical imaging, their limited capacity to capture long-range dependencies has led to the exploration of ViTs, which leverage self-attention mechanisms for a more comprehensive global understanding of images. The study utilized a dataset of 748 lung CT images to train both models with standardized input sizes, assessing their performance through conventional metrics—accuracy, precision, recall, F1 score, specificity, and AUC—as well as cross entropy, a novel metric for evaluating prediction uncertainty. Both models achieved similar accuracy rates (95%), with ViT demonstrating a slight edge over ResNet50 in precision and F1 scores for specific classes. However, ResNet50 exhibited higher recall for LULC, indicating fewer missed cases. Cross entropy analysis showed that the ViT model had lower average uncertainty, particularly in the LUAD, Normal, and LUSC classes, compared to ResNet50. This finding suggests that ViT predictions are generally more reliable, though ResNet50 performed better for LULC. The study underscores that accuracy alone is insufficient for model comparison, as cross entropy offers deeper insights into the reliability and confidence of model predictions. The results highlight the importance of incorporating cross entropy alongside traditional metrics for a more comprehensive evaluation of deep learning models in medical image classification, providing a nuanced understanding of their performance and reliability. While the ViT outperformed the CNN-based ResNet50 in lung cancer classification based on cross-entropy values, the performance differences were minor and may not hold clinical significance. Therefore, it may be premature to consider replacing CNNs with ViTs in this specific application.
基金funded by the Deanship of Research andGraduate Studies at King Khalid University through the Large Research Project under grant number RGP2/417/46.
文摘Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradientinformation, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimizationalgorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets.Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks bybalancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitationability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete featureselection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subsetvariance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches.This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the globalexploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. Theobjective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. Theproposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC),Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), andProstate Tumor (PT).The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%,96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively.These results validate the effectiveness of the hybrid approachin addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier.
基金This study was sponsored by the National Natural Science Foundation of China （No. 81472377） and the Natural Science Foundation of Shanghai （No. 16ZR1406500）. The authors also thank Wei-Yi Yang, Cui-Zhu Zhang, and Ying Shen for helping with follow-up of patients.
文摘This study aimed to assess the role of the National Comprehensive Cancer Network （NCCN） risk classification in predicting biochemical recurrence （BCR） after radical prostatectomy （RP） in Chinese prostate cancer patients. We included a consecutive cohort of 385 patients with prostate cancer who underwent RP at Fudan University Shanghai Cancer Center （Shanghai, China） from March 2011 to December 2014. Gleason grade groups were applied at analysis according to the 2014 International Society of Urological Pathology Consensus. Risk groups were stratified according to the NCCN Clinical Practice Guidelines in Oncology： Prostate Cancer version 1, 2017. All 385 patients were divided into BCR and non-BCR groups. The clinicopathological characteristics were compared using an independent sample t-test, Chi-squared test, and Fisher＇s exact test. BCR-free survival was compared using the log-rank test and multivariable Cox proportional hazard analysis. During median follow-up of 48 months （range： 1-78 months）, 31 （8.05%） patients experienced BCR. The BCR group had higher prostate-specific antigen level at diagnosis （46.54 ± 39.58 ng m1-1 vs 21.02 ± 21.06 ng ml-1, P= 0.001）, more advanced pT stage （P= 0.002）, and higher pN1 rate （P〈 0.001）. NCCN risk classification was a significant predictor of BCR {P = 0.0006） and BCR-free survival （P = 0.003） after RP. As NCCN risk level increased, there was a significant decreasing trend in BCR-free survival rate （Ptrend = 0.0002）. This study confirmed and validated that NCCN risk classification was a significant predictor of BCR and BCR-free survival after RP.
文摘BACKGROUND Rectal cancer is characterized by more local recurrence(LR)and lung metastasis than colon cancer.However,the diagnosis of rectal cancer is not standardized as there is no global consensus on its definition and classification.The classification of rectal cancer differs between Japanese and Western guidelines.AIM To clarify the characteristics of rectal cancer by comparing the tumor location and characteristics of rectal cancer with those of colon cancer according to each set of guidelines.METHODS A total of 958 patients with Stage II and III colorectal cancer were included in the analysis:607 with colon cancer and 351 with rectal cancer.Localization of rectal cancers was assessed by enema examination and rigid endoscopy.According to Japan guidelines,rectal cancer is classified as Rb(below the peritoneal inversion),Ra(between the inferior margin of second sacral vertebrae and Rb)or RS(between Ra and sacral promontory).RESULTS There were no significant differences between RS rectal cancer and colon cancer in the rates of liver and lung metastasis or LR.Lung metastasis and LR were significantly more common among Rb rectal cancer(in Japan)than in colon cancer(P=0.0043 and P=0.0002,respectively).Lung metastases and LR occurred at significantly higher rates in rectal cancer measuring≤12 cm and≤10 cm than in colon cancers(P=0.0117,P=0.0467,P=0.0036,P=0.0010).Finally,the rates of liver metastasis,lung metastasis,and LR in rectal cancers measuring 11 cm to 15 cm were 6.9%,2.8%,and 5.7%,respectively.These were equivalent to the rates in colon cancer.CONCLUSION High rectal cancer may be treated with the same treatment strategies as colon cancer.There was no difference in the classification of colorectal cancer between Japan and Western countries.
基金supported in part by National Natural Science Foundation of China(U23A20340,62376253,62106230,62176238,62476254)China Postdoctoral Science Foundation,China(2023M743185)+2 种基金Natural Science Foundation of Henan Province,China(222300420088)Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,China Open Foundation(BDIC-2023-A-007)Frontier Exploration Projects of Longmen Laboratory,China(NO.LMQYTSKT031).
文摘Dermatologists typically require extensive experience to accurately classify skin cancer.In recent years,the development of computer vision and machine learning has provided new methods for assisted diagnosis.Existing skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin images.This paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for classification.To achieve this,a new function set and a new terminal set have been developed.The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature extraction.The performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP methods.The experimental results show that the new approach achieves significantly better or similar performance in most cases.Further analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.
文摘This study presents an efficient feature selection method based on the Gower distance to enhance the accuracy and efficiency of standard classifiers on high-dimensional medical datasets.High-dimensional data poses significant challenges for traditional classifiers due to feature redundancy or being irrelevant.The proposed method addresses these challenges by partitioning the dataset into blocks,calculating the Gower distance within each block,and selecting features based on their average similarity.Technically,the Gower distance normalizes the absolute difference between numerical features,ensuring that each feature contributes equally to the distance calculation.This normalization prevents features with larger scales from overshadowing those with smaller scales.This process facilitates the identification of features that exhibit high harmony and are the most relevant for classification.The proposed feature selection strategy significantly reduces dimensionality,retains the most relevant features,and improves model performance.Experimental results show that the accuracy for the classifiers including k-nearest neighbors(KNN),naive Bayes(NB),decision tree(DT),random forest(RF),support vector machine(SVM),and logistic regression(LR)was increased by 4.38%-7.02%.Besides,the reduction in the feature set size contributes to a considerable decrease in computational complexity and thus faster diagnosis speed.The execution time was averagely reduced by 77.82%for all samples and 76.45%for one sample.These results demonstrate that the proposed feature selection method shows enhanced performance on both prediction accuracy and diagnostic speed,making it a promising tool for real-time clinical decision-making and improving patient care outcomes.
文摘The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.
文摘Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.
