Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients - Wei, Chaoyi; Xiang, Xinli; Zhou, Xiaobo; Ren, Siyan; Zhou, Qingyu; Dong, Wenjun; Lin, Haizhen; Wang, Saijun; Zhang, Yuyue; Lin, Hai; He, Qingzu; Lu, Yuer; et al. (Frontiers in Microbiology, 2023)
Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients. The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models. We successfully developed and validated an integrated radiomic model containing rectal information. The radiomics model (AUC=0.6855(0.5174-0.8535)) showed better performance and more net benefit in the validation set than the non-radiomic model (AUC=0.6641(0.4904-0.8378)). In particular, we applied Shapley Additive explanation (SHAP) values for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients. Based on the model output and SHAP values analysis, we suggest that clinicians can adjust the radiation dose to minimize the occurrence of severe radiation proctitis while not compromising the effectiveness of radiation therapy.