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

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World Journal of Gastroenterology 2020年第40期26卷 6207-6223页

作者： Hyun-Jong Jang ahwon lee J Kang In Hye Song Sung Hak lee Department of Physiology Department of Biomedicine and Health SciencesCatholic Neuroscience InstituteThe Catholic University of KoreaSeoul 06591South Korea Department of Hospital Pathology Seoul St.Mary’s HospitalCollege of MedicineThe Catholic University of KoreaSeoul 06591South Korea

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 *** To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images(WSIs) with deep learning-based *** 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 *** 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 *** 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 *** 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.

关键词： Colorectal cancer Mutation Deep learning Computational pathology Computer-aided diagnosis Digital pathology

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

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World Journal of Gastroenterology 2021年第44期27卷 7687-7704页

作者： Hyun-Jong Jang ahwon lee Jun Kang In Hye Song Sung Hak lee Catholic Big Data Integration Center Department of PhysiologyCollege of MedicineThe Catholic University of KoreaSeoul 06591South Korea Department of Hospital Pathology Seoul St.Mary’s HospitalCollege of MedicineThe Catholic University of KoreaSeoul 06591South Korea

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 *** learning(DL)has been successfully applied to analyze hematoxylin and eosin(H and E)-stained tissue slide *** 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 *** 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)*** performance of the classifier can be improved by adding new FFPE WSI training dataset from our *** 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 *** This study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.

关键词： Gastric cancer Mutation Deep learning Digital pathology Formalin-fixed paraffin-embedded

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Bilateral primary breast lymphoma

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Chinese Medical Journal 2010年第11期123卷 1482-1484页

作者： Jung Im Yi Byung Joo Chae Ja Seong Bae Bong Joo Kang ahwon lee Byung Joo Song Sang Seol Jung Departments of Surgery Catholic University of Korea Seoul Korea Department of Radiology Catholic University of Korea Seoul Korea Department of Pathology Catholic University of Korea Seoul Korea

Pprimary breast lymphoma （PBL） is rare, accounting for 0.04%-0.50% of breast malignancies and 1.7% of extranodal lymphoma The originally descnibect diagnostic criteria for PBL2 remains the standard definition for this disease. These criteria are breast location as the clinical site of presentation, absence of history of previous lymphoma or evidence of widespread disease at diagnosis, close association of lymphoma with breast tissue in pathologic specimens, and involvement of ipsilateral lymph nodes if they develop simultaneously with PBL.

关键词： breast lymphoma treatment

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