Tang Ze, Feng Shiyun, Liu Qing, Ban Yunze, Zhang Yan
Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
Int J Gen Med. 2024 Dec 6;17:5869-5882. doi: 10.2147/IJGM.S495296. eCollection 2024.
Esophageal squamous cell carcinoma (ESCC) has a high incidence and mortality rate. Postoperative positive surgical margins (PSM) often correlate with poor prognosis. This study aims to develop and validate a predictive model for PSM positivity in ESCC patients, with the potential to guide preoperative planning and improve patient outcomes.
We conducted a retrospective analysis of 1776 patients who underwent esophageal cancer surgery at the First Affiliated Hospital of Jilin University between January 2015 and December 2023. Patients with visible residual tumors (R2) or microscopic residual tumors (R1) at the surgical margins were classified as having PSM. High-dimensional pathological features were extracted from digital pathological sections using CellProfiler software. The selected features were used to develop a predictive model based on decision trees and generalized linear regression, and the model was validated in an independent cohort. Clinically significant pathological factors (P < 0.05) were included in multivariate logistic regression for further validation. The model's performance was assessed using calibration curves and receiver operating characteristic (ROC) curves, generated with the Bootstrap method. Decision curve analysis (DCA) was employed to evaluate the clinical utility of the predictive model.
A total of 229 patients (12.89%) were diagnosed with PSM. Logistic regression analysis identified multifocal lesions, vascular invasion, and pathomics-based features as independent predictors of PSM. The predictive model, represented by a decision tree, demonstrated good discrimination with an area under the ROC curve of 0.899 (95% CI: 0.842-0.956, P < 0.001), and a strong calibration curve between the predicted probability and the actual probability. Additionally, the nomogram demonstrated slightly inferior discrimination with an area under the ROC curve of 0.803 (95% CI: 0.734-0.872, P < 0.001) in the training cohort.
Our study successfully established and validated a pathology-based predictive model for PSM risk, which could enhance preoperative evaluation and inform treatment strategies for ESCC.
食管鳞状细胞癌（ESCC）的发病率和死亡率较高。术后手术切缘阳性（PSM）常与预后不良相关。本研究旨在建立并验证ESCC患者PSM阳性的预测模型，以指导术前规划并改善患者预后。
我们对2015年1月至2023年12月期间在吉林大学第一医院接受食管癌手术的1776例患者进行了回顾性分析。手术切缘有肉眼可见残留肿瘤（R2）或镜下残留肿瘤（R1）的患者被归类为PSM。使用CellProfiler软件从数字病理切片中提取高维病理特征。所选特征用于基于决策树和广义线性回归建立预测模型，并在独立队列中进行验证。具有临床意义的病理因素（P<0.05）纳入多因素逻辑回归进行进一步验证。使用Bootstrap方法生成校准曲线和受试者工作特征（ROC）曲线来评估模型性能。采用决策曲线分析（DCA）评估预测模型的临床实用性。
共有229例患者（12.89%）被诊断为PSM。逻辑回归分析确定多灶性病变、血管侵犯和基于病理组学的特征为PSM的独立预测因素。以决策树表示的预测模型显示出良好的区分度，ROC曲线下面积为0.899（95%CI：0.842-0.956，P<0.001），预测概率与实际概率之间有很强的校准曲线。此外，在训练队列中，列线图的区分度略低，ROC曲线下面积为0.803（95%CI：0.734-0.872，P<0.001）。
我们的研究成功建立并验证了基于病理的PSM风险预测模型，该模型可加强术前评估并为ESCC的治疗策略提供参考。
