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In Anticancer research

BACKGROUND/AIM : We aimed to investigate the role of radiogenomic and deep learning approaches in predicting the KRAS mutation status of a tumor using radiotherapy planning computed tomography (CT) images in patients with locally advanced rectal cancer.

PATIENTS AND METHODS : After surgical resection, 30 (27.3%) of 110 patients were found to carry a KRAS mutation. For the radiogenomic model, a total of 378 texture features were extracted from the boost clinical target volume (CTV) in the radiotherapy planning CT images. For the deep learning model, we constructed a simple deep learning network that received a three-dimensional input from the CTV.

RESULTS : The predictive ability of the radiogenomic score model revealed an AUC of 0.73 for KRAS mutation, whereas the deep learning model demonstrated worse performance, with an AUC of 0.63.

CONCLUSION : The radiogenomic score model was a more feasible approach to predict KRAS status than the deep learning model.

Jang Bum-Sup, Song Changhoon, Kang Sung-Bum, Kim Jae-Sung


KRAS, Radiogenomics, chemoradiation, clinical target volume, deep learning, rectal cancer