Zhang Yiyi, Huang Ying, Xu Meifang, Zhuang Jiazheng, Zhou Zhibo, Zheng Shaoqing, Zhu Bingwang, Guan Guoxian, Chen Hong, Liu Xing
Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
BMC Cancer. 2024 Dec 26;24(1):1580. doi: 10.1186/s12885-024-13328-w.
Accurate prediction of pathological complete response (pCR) and disease-free survival (DFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (NCRT) is essential for formulating effective treatment plans. This study aimed to construct and validate the machine learning (ML) models to predict pCR and DFS using pathomics.
A retrospective analysis was conducted on 294 patients who received NCRT from two independent institutions. Pathomics from pre-NCRT H&E stains were extracted, and five ML models were developed and validated across two centers using ROC, Kaplan-Meier, time-dependent ROC, and nomogram analyses.
Among the five ML models, the Xgboost (XGB) model demonstrated superior performance in predicting pCR, achieving an AUC of 1.000 (p < 0.001) on the internal data-set and an AUC of 0.950 (p = 0.001) on the external data-set.The XGB model effectively differentiated between high-risk and low-risk prognosis patients across all five centers: internal dataset (DFS, p = 0.002; OS, p = 0.004) and external dataset (DFS, p = 0.074; OS, p = 0.224).Furthermore, the COX regression demonstrated that the tumor length (HR = 1.230, 95%CI: 1.050-1.440, p = 0.010), post-NCRT CEA (HR = 1.716, 95%CI: 1.031- 2.858, p = 0.038), and XGB model score (HR = 0.128, 95%CI: 0.026-0.636, p = 0.012) were independent predictors of DFS after NCRT in the internal data-set.Using COX regression, the nomogram model and time-dependent AUC analysis demonstrated strong predictive discrimination for DFS in LARC patients across two independent institutions.
The ML model based on pathomics demonstrated effective prediction of pCR and prognosis in LARC patients. Further validation in larger cohorts is warranted to confirm the findings of this study.
准确预测接受新辅助放化疗（NCRT）的局部晚期直肠癌（LARC）患者的病理完全缓解（pCR）和无病生存期（DFS）对于制定有效的治疗方案至关重要。本研究旨在构建和验证使用病理组学预测pCR和DFS的机器学习（ML）模型。
对来自两个独立机构的294例接受NCRT的患者进行回顾性分析。提取NCRT前苏木精-伊红（H&E）染色的病理组学特征，并使用ROC、Kaplan-Meier、时间依赖性ROC和列线图分析在两个中心开发和验证五个ML模型。
在五个ML模型中，Xgboost（XGB）模型在预测pCR方面表现出卓越性能，在内部数据集上的AUC为1.000（p<0.001），在外部数据集上的AUC为0.950（p=0.001）。XGB模型在所有五个中心有效区分了高风险和低风险预后患者：内部数据集（DFS，p=0.002；OS，p=0.004）和外部数据集（DFS，p=0.074；OS，p=0.224）。此外，COX回归表明，肿瘤长度（HR=1.230，95%CI：1.050-1.440，p=0.010）、NCRT后癌胚抗原（CEA）（HR=1.716，95%CI：1.031-2.858，p=0.038）和XGB模型评分（HR=0.128，95%CI：0.026-0.636，p=0.012）是内部数据集中NCRT后DFS的独立预测因素。使用COX回归、列线图模型和时间依赖性AUC分析对两个独立机构的LARC患者的DFS表现出强大的预测辨别力。
基于病理组学的ML模型在LARC患者中对pCR和预后表现出有效的预测。需要在更大的队列中进行进一步验证以证实本研究的结果。
