Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
School of Science, Jimei University, Xiamen, Fujian, People's Republic of China.
Ann Surg Oncol. 2021 Oct;28(11):6408-6421. doi: 10.1245/s10434-021-10218-4. Epub 2021 Jun 19.
The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.
In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.
The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.
The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.
肿瘤微环境中的胶原特征（CFs）与新辅助放化疗（nCRT）治疗反应之间的关系尚不清楚。本研究旨在建立和验证一种基于 CFs 和临床病理特征的预测模型，以预测局部晚期直肠癌（LARC）患者接受 nCRT 治疗的反应。
在这项多中心回顾性分析中，纳入了 428 名患者，并将其随机分为训练队列（299 名患者）和验证队列（129 名患者）[7:3 比例]。从预处理活检的多光子图像中提取了 11 个 CFs，并使用支持向量机（SVM）构建了 CFs-SVM 分类器。使用多变量分析建立并呈现了一个包含 CFs-SVM 分类器和临床病理特征的预测模型。在验证队列中对该列线图进行了进一步验证。
整合胶原面积、直线度和交联密度的 CFs-SVM 分类器与治疗反应显著相关。列线图中的预测因子包括多变量分析中包含的 CFs-SVM 分类器和临床病理特征。CFs 列线图具有良好的区分能力，在训练队列中的 AUC 为 0.834，在验证队列中的 AUC 为 0.854。决策曲线分析表明 CFs 列线图具有临床实用性。此外，与传统临床病理模型相比，CFs 列线图在确定 nCRT 反应方面具有更强的区分能力。
基于肿瘤微环境中 CFs 的 CFs-SVM 分类器与治疗反应相关，整合 CFs-SVM 分类器和临床病理特征的 CFs 列线图有助于对 LARC 患者接受 nCRT 治疗的反应进行个体化预测。
