Mao Yitao, Pei Qian, Fu Yan, Liu Haipeng, Chen Changyong, Li Haiping, Gong Guanghui, Yin Hongling, Pang Peipei, Lin Huashan, Xu Biaoxiang, Zai Hongyan, Yi Xiaoping, Chen Bihong T
Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China.
Front Oncol. 2022 May 10;12:850774. doi: 10.3389/fonc.2022.850774. eCollection 2022.
Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT).
Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort.
The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone.
Our combined predictive model was robust in differentiating patients with and without response to nCRT.
计算机断层扫描（CT）常用于辅助局部晚期直肠癌（LARC）的诊断和治疗。本研究评估基于预处理CT的放射组学对预测LARC新辅助放化疗（nCRT）病理完全缓解（pCR）的有效性。
本回顾性研究纳入了2010年7月至2018年12月期间接受nCRT后行全直肠系膜切除术的LARC患者。从预处理对比增强CT图像中提取了总共340个放射组学特征。使用最小绝对收缩和选择算子（LASSO）方法选择与pCR最相关的特征，并生成放射组学特征。利用放射组学特征和临床病理变量建立预测模型。通过决策曲线分析评估模型性能，并在独立队列中进行验证。
本研究中216例连续患者中有44例（20.4%）实现了pCR。性能最佳的模型同时使用了放射组学和临床变量，包括放射组学特征、距肛缘距离、淋巴细胞与单核细胞比值和癌胚抗原。该联合模型在训练队列和验证队列中区分有和无pCR患者的曲线下面积分别为0.926和0.872。联合模型的性能也优于仅使用放射组学或临床变量建立的模型。
我们的联合预测模型在区分对nCRT有反应和无反应的患者方面具有稳健性。
