Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.

ABSTRACT:

Background

The detection of Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network (ResNet) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging.

Methods

We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40).

SUBMITTER: He K 

PROVIDER: S-EPMC7268438 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature