Chen Han, Yang Ruoyun, Yu Xin, Jiang Xingzhou, Jiang Liuqin, Zhang Guoxin, Zhou Xiaoying
Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
The First Clinical Medical College of Nanjing Medical University, Nanjing 210029, China.
J Cancer. 2022 Apr 11;13(7):2238-2245. doi: 10.7150/jca.71114. eCollection 2022.
In superficial esophageal squamous cell carcinoma (SESCC), the lymph node status is considered as one of the essential factors to determine the primary treatment strategy. Nevertheless, current noninvasive staging methods before surgical intervention have limited accuracy. This study aimed to establish a simple and noninvasive serum-testing panel that facilitates the preoperative prediction of pathological nodal status in SESCC patients. Data for preoperative hematological parameters were retrospectively collected from 256 SESCC patients who underwent esophagectomy from December 2017 to May 2020. The random forest classification and decision tree algorithms were applied to identify the optimal combination of serum parameters for accurately identifying positive nodal metastasis. Twelve candidate parameters were identified for statistical significance in predicting positive nodal metastasis. A multi-analyte panel was established by using a random forest classification method, incorporating four optimal parameters: Hematocrit (HCT), Activated Partial Thromboplastin Time (APTT), Retinol-Binding Proteins (RBP), and Mean Platelet Volume (MPV). A schematic decision tree was yielded from the above panel with an 89.1% accuracy of classification capability. This study established a simple laboratory panel in discerning the preoperative lymph nodal status of SESCC patients. With further validation, this panel may serve as a simple tool for clinicians to choose appropriate intervention (surgery versus endoscopic resection) for SESCC patients.
在浅表性食管鳞状细胞癌（SESCC）中，淋巴结状态被视为决定主要治疗策略的关键因素之一。然而，目前手术干预前的非侵入性分期方法准确性有限。本研究旨在建立一个简单的非侵入性血清检测指标体系，以利于术前预测SESCC患者的病理淋巴结状态。回顾性收集了2017年12月至2020年5月期间接受食管切除术的256例SESCC患者的术前血液学参数数据。应用随机森林分类和决策树算法来确定血清参数的最佳组合，以准确识别阳性淋巴结转移。确定了12个候选参数在预测阳性淋巴结转移方面具有统计学意义。通过随机森林分类方法建立了一个多分析物指标体系，纳入了四个最佳参数：血细胞比容（HCT）、活化部分凝血活酶时间（APTT）、视黄醇结合蛋白（RBP）和平均血小板体积（MPV）。从上述指标体系得出了一个示意性决策树，其分类能力的准确率为89.1%。本研究建立了一个简单的实验室指标体系来辨别SESCC患者的术前淋巴结状态。经过进一步验证，该指标体系可能成为临床医生为SESCC患者选择合适干预措施（手术与内镜切除）的一个简单工具。
