Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

BACKGROUND Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin(H&E) sections in diverse tumors including colorectal cancers(CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time-and cost-effective complementary method for personalized treatment.AIM To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images(WSIs) with deep learning-based classifiers.METHODS A total of 629 CRC patients from The Cancer Genome Atlas(TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital(SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC, KRAS, PIK3CA, SMAD4, and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic(ROC) curves and area under the curves(AUCs) for all the classifiers were presented.RESULTS The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs.The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved.CONCLUSION APC, KRAS, PIK3CA, SMAD4, and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

BACKGROUND Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer(GC).To facilitate this research,a cost-and time-effective method to analyze the mutational status is necessary.Deep learning(DL)has been successfully applied to analyze hematoxylin and eosin(H and E)-stained tissue slide images.AIM To test the feasibility of DL-based classifiers for the frequently occurring mutations from the H and E-stained GC tissue whole slide images(WSIs).METHODS From the GC dataset of The Cancer Genome Atlas(TCGA-STAD),wildtype/mutation classifiers for CDH1,ERBB2,KRAS,PIK3CA,and TP53 genes were trained on 360×360-pixel patches of tissue images.RESULTS The area under the curve(AUC)for the receiver operating characteristic(ROC)curves ranged from 0.727 to 0.862 for the TCGA frozen WSIs and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded(FFPE)WSIs.The performance of the classifier can be improved by adding new FFPE WSI training dataset from our institute.The classifiers trained for mutation prediction in colorectal cancer completely failed to predict the mutational status in GC,indicating that DL-based mutation classifiers are incompatible between different cancers.CONCLUSION This study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.