Objective·To analyze the differences and classify hypertrophic cardiomyopathy(HCM)patients and healthy controls(HC)using short-axis cine cardiac magnetic resonance(CMR)images-derived radiomics features.Methods·One hundred HCM subjects were included,and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology,Renji Hospital,Shanghai Jiao Tong University School of Medicine.The CMR examinations were performed by experienced radiologists on these subjects.CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements,including left ventricular ejection fraction(LVEF),left ventricular end-diastolic volume(LVEDV)and left ventricular end-diastolic mass(LVEDM).The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine.The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups.Results·One hundred and seven 3D radiomic features were selected and extracted.After exclusion of highly correlated features,least absolute shrinkage and selection operator(LASSO)was used,and a 5-fold cross-validation was performed.There were still 11 characteristics with non-zero coefficients.The K-best method was used to decide the top 8 features for subsequent analysis.Among them,four features were significantly different between the two groups(all P<0.05).Support vector machine(SVM)and random forest(RF)models were constructed to discriminate the two groups.The results showed that the maximum area under the curve(AUC)for the single-feature model(first order grayscale:entropy)was 0.833(95%CI 0.685?0.968)and the maximum accuracy for the multi-feature model was 83.3%with an AUC of 0.882(95%CI 0.705?0.980).Conclusion·There are significant differences in both left ventricular function and left ventricular morphology between HCM and HC.The 3D myocardial radiomic features of the two groups are also significantly different.Although single feature is able to distinguish the two groups,the combination of multi-features show better classification performance.

Objective·To develop a machine learning approach for early identification of metabolic syndromes associated with inflammatory metabolic state changes in breast cancer patients after neoadjuvant therapy,using common laboratory and transthoracic echocardiography indices.Methods·Female patients with primary invasive breast cancer diagnosed at the Department of Breast Surgery,Renji Hospital,Shanghai Jiao Tong University School of Medicine,between September 2020 and September 2022,were included.General patient information,laboratory test results,and transthoracic echocardiography data were collected.After feature extraction,five machine learning algorithms,including random forest(RF),gradient boosting(GB),support vector machine(SVM),K-nearest neighbor(KNN),and decision tree(DT),were applied to construct a prediction model for the changes of the patients' metabolic state after neoadjuvant therapy,and the prediction performances of the five models were compared.Results·A total of 232 cases with valid clinical data were included,comprising 135 cases before neoadjuvant therapy and 97 cases after completing 4 cycles of neoadjuvant therapy.Feature extraction identified five key features:white blood cell count,hemoglobin,high-density lipoprotein(HDL),interleukin-2 receptor,and interleukin-8.In the multi-feature analysis,the area under the receiver operating characferistic curve(AUC)was higher in the combination of white blood cell count,hemoglobin and HDL compared to the combination of interleukin-2 receptor and interleukin-8(RF:0.928 vs 0.772,GB:0.900 vs 0.792,SVM:0.941 vs 0.764,KNN:0.907 vs 0.762,DT:0.799 vs 0.714).The RF,SVM,and GB models showed higher AUC(0.928,0.941,0.900)and accuracy(0.914,0.897,0.776).The SVM model exhibited superior accuracy in the training data compared to the RF and GB models(P=0.394,0.122 and 0.097,respectively).Conclusion·The SVM model can be used to establish a prediction model for identifying breast cancer patients at high risk of developing inflammatory metabolic state-related metabolic syndrome after neoadjuvant therapy by incorporating five common clinical indicators,namely,white blood cell count,hemoglobin,high-density lipoprotein,interleukin-2 receptor,and interleukin-8.SVM modeling may be useful for clinicians to establish individualized screening protocols based on a patient's inflammatory metabolic state.