Objective: To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer. Study design: One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCE sequential ) and DWI images changing over three b -values (DWI sequential ). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed. Results: For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWI sequential with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWI sequential alone, together with a linear SVM model, achieved an accuracy of 83.6%. Conclusions: Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different b -values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.

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Resveratrol, a natural polyphenolic phytoalexin, was reported to exert multiple anticancer effects as a traditional Chinese medicine. However, research regarding the anticancer mechanism of resveratrol for the treatment and prevention of gastric cancer has reported conflicting results. In the present study, it was determined that resveratrol inhibited cell viability in a dose-dependent manner in the human gastric cancer cell line BGC823. Cell migration and invasion were suppressed significantly following treatment with 200 µM resveratrol. Additionally, resveratrol inhibited metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) expression, which was overexpressed in gastric cancer cells. Further experiments revealed that MALAT1 knockdown suppressed cell viability, migration, invasion and epithelial-to-mesenchymal transition in BGC823 cells. The present study indicated that resveratrol inhibited migration and invasion in human gastric cancer cells via suppressing MALAT1-mediated epithelial-to-mesenchymal transition, providing novel evidence for understanding the anticancer mechanism of resveratrol.