Identification of Tissue Types and Gene Mutations From Histopathology Images for Advancing Colorectal Cancer Biology

Identification of Tissue Types and Gene Mutations From Histopathology Images for Advancing Colorectal Cancer Biology 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Author (s): Yuqi JiangCecilia K. W. ChanRonald C. K. ChanXin WangNathalie WongKa Fai To; Simon S. M. Ng; James Y. W. Lau; Carmen C. Y. Poon

Objective: Colorectal cancer (CRC) patients respond differently to treatments and are sub-classified by different approaches. We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features. Results: Data of 461 patients were collected from TCGA-COAD database. The proposed framework was able to 1) differentiate tumour from normal tissues with an Area Under Receiver Operating Characteristic curve (AUROC) of 0.97; 2) identify certain gene mutations (MYH9, TP53) with an AUROC > 0.75; 3) classify CMS2 and CMS4 better than the other subtypes; and 4) demonstrate the generalizability of predicting KRAS mutants in an external cohort. Conclusions: Artificial intelligent can be used for on-site patient classification. Although KRAS mutants were commonly associated with therapeutic resistance and poor prognosis, subjects with predicted KRAS mutants in this study have a higher survival rate in 30 months after diagnoses.

Access the Full Paper on IEEE Xplore®

Sign-in or become an IEEE member to discover the full contents of the paper.