  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.
