A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer

Background Recent studies showed the potential of MRI‐based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI‐based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear. Purpose To develop a DL method based on T2‐weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer. Study Type Retrospective. Subjects A total of 376 patients (108 women [28.7%]) with histopathology‐confirmed rectal adenocarcinoma and KRAS mutation status. Field Strength/Sequence A 3 T, turbo spin echo T2WI and single‐shot echo‐planar diffusion‐weighted imaging (b = 0, 1000 sec/mm 2 ). Assessment A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA). Statistical Tests Mann–Whitney U test, Chi‐squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer–Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients ( P < 0.05 was considered statistically significant). Results All the nine clinical‐MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85. Data Conclusion The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer. Evidence Level 3 Technical Efficacy Stage 2