Open AccessTransfer learning based classification of MSI and MSS gastrointestinal cancer
Author(s) -
Zabiha Khan,
R Loganathan
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns1.4952
Subject(s) - microsatellite instability , dna mismatch repair , colorectal cancer , pembrolizumab , gastrointestinal cancer , cancer , cancer research , immunotherapy , medicine , oncology , chemotherapy , microsatellite , biology , gene , genetics , allele
Gastrointestinal and Colorectal cancers are treated with chemotherapy and its other forms which are not able to provide higher survival rates [1]. Immunotherapy is increasingly becoming popular due to its promising response especially to mutated tumors such as MicroSatellite Instability (MSI) cancers with deficient DNA Mismatch-Repair system (dMMR). Generally, 85% of all the cases related to gastrointestinal and colorectal cancers have proficient DNA Mismatch-Repair system (pMMR) which are also called MicroSatellite Stability (MSS). Only about 15% of the gastrointestinal and colorectal cancer patients have deficient DNA Mismatch-Repair system (dMMR) causing MicroSatellite Instability (MSI) in their tumors. While Immunotherapy responds well to patients with MSI tumors, it is resistant to MSS tumors [2]. Hence, it’s important to classify MSI vs. MSS tumors so that appropriate treatment can be given to the patients. Clinically MSI cancers are difficult to be detected after stage III due to their sensitivity to pembrolizumab inhibitors [3] [4]. In this work, deep learning based transfer learning approach is detailed that can accurately classify MSI vs. MSS cancers using histological images which are derived from formalin-fixed paraffin-embedded (FFPE).