PT  - JOURNAL ARTICLE
AU  - BUM-SUP JANG
AU  - CHANGHOON SONG
AU  - SUNG-BUM KANG
AU  - JAE-SUNG KIM
TI  - Radiogenomic and Deep Learning Network Approaches to Predict <em>KRAS</em> Mutation from Radiotherapy Plan CT
AID  - 10.21873/anticanres.15193
DP  - 2021 Aug 01
TA  - Anticancer Research
PG  - 3969--3976
VI  - 41
IP  - 8
4099  - http://ar.iiarjournals.org/content/41/8/3969.short
4100  - http://ar.iiarjournals.org/content/41/8/3969.full
SO  - Anticancer Res2021 Aug 01; 41
AB  - 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.