A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer

Simple Summary In this study, we explored the potential of using F-18 fluorodeoxyglucose positron emission tomography (FDG PET), to predict the genetic characteristics and prognosis of patients with stage IV colorectal cancer. We used a machine learning approach to analyze association between image patterns and the tumor mutational burden (TMB), which can provide information for selecting treatment options and predicting prognosis. Our results showed that several radiomic features from the PET images were significantly associated with TMB. Furthermore, we developed a scoring system based on these features, which was found to be a significant predictor of patient survival. This research suggests that FDG PET could be a surrogate marker of TMB in advanced colorectal cancer. It offers a non-invasive method to assess a tumor's genetic characteristics and predict patient outcomes, potentially leading to more personalized and effective treatment strategies. Introduction: We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC). Methods: Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx. For feature selection, PET-based radiomics associated with TMB were selected by logistic regression analysis. The performances of seven ML algorithms to predict high TMB were compared by the area under the receiver's operating characteristic curves (AUCs) and validated by five-fold cross-validation. A PET radiomic score was calculated by averaging the z-score of each radiomic feature. The prognostic power of the PET radiomic score was assessed using Cox proportional hazards regression analysis. Results: Ten significant radiomic features associated with TMB were selected: surface-to-volume ratio, total lesion glycolysis, tumor volume, area, compacity, complexity, entropy, correlation, coarseness, and zone size non-uniformity. The k-nearest neighbors model obtained the good performance for prediction of high TMB (AUC: 0.791, accuracy: 0.814, sensitivity: 0.619, specificity: 0.871). On multivariable Cox regression analysis, the PET radiomic score (Hazard ratio = 4.498, 95% confidential interval = 1.024-19.759; p = 0.046) was a significant independent prognostic factor for OS. Conclusions: This study demonstrates that PET-based radiomics are useful image biomarkers for the prediction of TMB status in stage IV CRC. PET radiomic score, which integrates significant radiomic features, has the potential to predict survival in stage IV CRC patients.