Objectives: Approximately 30% of non-metastatic anal squamous cell carcinoma (ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and currently available clinical variables are poor predictors of treatment response. We aimed to develop a model leveraging information extracted from radiation pretreatment planning CT to predict recurrence-free survival (RFS) in ASCC patients after CRT. Methods: Radiomics features were extracted from planning CT images of 96 ASCC patients. Following pre-feature selection, the optimal feature set was selected via step-forward feature selection with a multivariate Cox proportional hazard model. The RFS prediction was generated from a radiomics-clinical combined model based on an optimal feature set with five repeats of five-fold cross validation. The risk stratification ability of the proposed model was evaluated with Kaplan-Meier analysis. Results: Shape- and texture-based radiomics features significantly predicted RFS. Compared to a clinical-only model, radiomics-clinical combined model achieves better performance in the testing cohort with higher C-index (0.80 vs 0.73) and AUC (0.84 vs 0.79 for 1-year RFS, 0.84 vs 0.78 for 2-year RFS, and 0.86 vs 0.83 for 3-year RFS), leading to distinctive high- and low-risk of recurrence groups (p<0.001). Conclusions: A treatment planning CT based radiomics and clinical combined model had improved prognostic performance in predicting RFS for ASCC patients treated with CRT as compared to a model using clinical features only.

翻译：目的：约30%的非转移性肛管鳞癌（ASCC）患者在放化疗（CRT）后会出现复发，而目前可用的临床变量对治疗反应的预测能力较差。本研究旨在利用放疗前计划CT提取的信息，构建预测ASCC患者CRT后无复发生存（RFS）的模型。方法：从96例ASCC患者的计划CT图像中提取影像组学特征。经预特征筛选后，采用逐步前向特征选择结合多变量Cox比例风险模型确定最优特征集。基于最优特征集构建影像组学-临床联合模型，通过五轮五折交叉验证生成RFS预测结果。采用Kaplan-Meier分析评估所提模型的风险分层能力。结果：基于形状和纹理的影像组学特征可显著预测RFS。与纯临床模型相比，影像组学-临床联合模型在测试队列中取得了更优性能，其C指数（0.80 vs 0.73）和AUC值更高（1年RFS为0.84 vs 0.79，2年RFS为0.84 vs 0.78，3年RFS为0.86 vs 0.83），成功区分出高复发风险组和低复发风险组（p<0.001）。结论：与仅使用临床特征的模型相比，基于治疗计划CT的影像组学与临床联合模型在预测接受CRT治疗的ASCC患者RFS方面展现了更优的预后性能。