Wei Chaoyi, Xiang Xinli, Zhou Xiaobo, Ren Siyan, Zhou Qingyu, Dong Wenjun, Lin Haizhen, Wang Saijun, Zhang Yuyue, Lin Hai, He Qingzu, Lu Yuer, Jiang Xiaoming, Shuai Jianwei, Jin Xiance, Xie Congying
Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China.
The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Front Microbiol. 2023 Jan 12;13:1090770. doi: 10.3389/fmicb.2022.1090770. eCollection 2022.
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 ( = 124) and a validation set ( = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models.
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) method 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.
We successfully developed and validated an integrated radiomic model containing rectal information. 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.
放射性直肠炎是宫颈癌放疗后的常见并发症。与其他器官的单纯放射性损伤不同，放射性直肠炎是一种与微生物群密切相关的复杂疾病。然而，肠道微生物群分析耗时且昂贵。本研究旨在利用放射组学挖掘直肠信息，并将其纳入列线图模型，以廉价、快速地预测宫颈癌术后患者严重放射性直肠炎。
根据RTOG/EORTC标准对患者放射性直肠炎的严重程度进行分级。将放射性直肠炎毒性分级大于或等于2级设定为模型的目标。共178例宫颈癌患者被分为训练集（n = 124）和验证集（n = 54）。采用多因素逻辑回归建立放射组学和非放射组学模型。
在验证集中，放射组学模型[AUC = 0.6855(0.5174 - 0.8535)]比非放射组学模型[AUC = 0.6641(0.4904 - 0.8378)]表现更好，净效益更高。特别是，我们首次将SHapley Additive exPlanation（SHAP）方法应用于基于放射组学的逻辑回归模型，从病例和特征角度进一步解释放射组学特征。综合放射组学模型能够首次准确量化评估宫颈癌术后患者放射性直肠炎的发生概率，克服了目前仅通过剂量体积参数对计划进行定性评估的局限性。
我们成功开发并验证了一个包含直肠信息的综合放射组学模型。对该模型的SHAP分析表明，放射组学特征在定量评估宫颈癌术后患者放射性直肠炎发生概率方面具有辅助作用。
