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Abstract
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.

Affiliations

Bum-Sup Jang  1 , Changhoon Song  1 , Sung-Bum Kang  2 , Jae-Sung Kim  3   4
1 Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
2 Department of Surgery, College of Medicine, Seoul National University, Seoul, Republic of Korea.
3 Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea jskim@snubh.org.
4 Department of Radiation Oncology, College of Medicine, Seoul National University, Seoul, Republic of Korea.