¹⁸F-FDG PET based intratumoral and peritumoral explainable radiomics for predicting cervical cancer prognosis: A...

Prognosis evaluation in cervical cancer is crucial for treatment decisions. This study aims to develop and validate a combined model using positron emission tomography (PET)-derived intratumoral and peritumoral radiomic parameters to predict cervical cancer prognosis based on the Shapley additive explanations (SHAP) method.

A retrospective cohort of 114 patients with cervical cancer from two institutions was used, with one institution's data designated for training and the other for testing. Semi-automatic segmentation of fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) PET images was performed to delineate the primary intratumoral and peritumoral regions, defined by expanding the tumor boundary by 2mm, 4mm, 6mm, and 8mm. Radiomic features were extracted from each region. Six machine learning algorithms were employed to construct intratumoral and peritumoral radiomic models, with the optimal model selected based on performance evaluated through receiver operating characteristic (ROC) and calibration curves. Area under the curve (AUC) values were compared using the DeLong test. The SHAP method was used to identify the key features influencing prognosis.

Among the intratumoral and peritumoral radiomic models, the Gradient Boosting Machine (GBM) algorithm showed superior performance. The 4mm peritumoral model exhibited the best performance among the four peritumoral models, with a testing AUC of 0.762 (95% CI: 0.582-0.944). The integrated model combining the intratumoral and 4mm peritumoral regions emerged as the optimal radiomic model for predicting cervical cancer prognosis, achieving the highest AUC of 0.954 (95% CI: 0.882-1.000) in the testing set. At the patient level, SHAP force plots provided valuable insights into the combined model's predictive ability for prognosis.

The integrated radiomic model, particularly for the 4mm peritumoral region, was validated as the optimal approach for predicting overall survival in cervical cancer. The application of the SHAP method enhanced interpretability, allowing for the identification of key features influencing prognosis and offering transparent insights for guiding personalized treatment strategies.