Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma After Chemoradiotherapy using Planning CT-based Radiomics Model.

Autor:	Tang S; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.; Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX., Wang K; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.; Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX., Hein D; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX., Lin G; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX., Sanford NN; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX., Wang J; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.; Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX.
Jazyk:	angličtina
Zdroj:	The British journal of radiology [Br J Radiol] 2024 Nov 13. Date of Electronic Publication: 2024 Nov 13.
DOI:	10.1093/bjr/tqae235
Abstrakt:	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 nested 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 0.78 for 1-year RFS, 0.84 vs 0.79 for 2-year RFS, and 0.85 vs 0.81 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. Advances in Knowledge: The use of radiomics from planning CT is promising in assisting in personalized management in ASCC. The study outcomes support the role of planning CT-based radiomics as potential imaging biomarker. (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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