Department of Radiology (Xiangya Hospital), Central South University, Changsha, Hunan, P.R. China.
Department of Gastroenterology (The Third Xiangya Hospital), Central South University, Changsha, Hunan, P.R. China.
Cancer Med. 2023 Feb;12(3):2463-2473. doi: 10.1002/cam4.5086. Epub 2022 Aug 1.
Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.
Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.
This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.
Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.
局部晚期结直肠癌（LARC）新辅助放化疗（nCRT）后无应答或降期不良的早期检测仍然具有挑战性。我们旨在评估 nCRT 前放疗计划 CT（RT-PCT）的放射组学是否可以区分 nCRT 后无应答或无降期的患者与 nCRT 后有反应和降期的患者。
回顾性纳入 2009 年 3 月至 2019 年 3 月期间接受 nCRT 治疗的 LARC 患者。通过视觉检查分析传统影像学特征，通过计算方法从 nCRT 前放疗计划 CT 图像中分析放射组学特征。使用放射组学方法和临床病理特征构建区分模型，用于预测 nCRT 无应答。评估模型的分类效率、校准、判别和临床应用。
本研究共纳入 215 例患者，其中训练队列 151 例（50 例无应答者和 101 例应答者），验证队列 64 例（21 例无应答者和 43 例应答者）。对于预测无应答，与逻辑回归方法构建的模型（训练和验证队列 AUC 值分别为 0.72 和 0.71）相比，基于集成机器学习方法构建的模型具有更高的性能（AUC 值分别为 0.92 和 0.89）。决策曲线和校准曲线分析均证实了集成机器学习模型具有更高的预测性能。
nCRT 前 CT 放射组学在预测 nCRT 无应答方面具有良好的性能，可能有助于协助 LARC 患者的治疗计划。
