J Cancer 2022; 13(7):2238-2245. doi:10.7150/jca.71114 This issue
1. Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
2. The First Clinical Medical College of Nanjing Medical University, Nanjing 210029, China.
*These authors contributed equally to this work.
Background and Aims: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
Keywords: Superficial Esophageal Cancer, Squamous Cell Carcinoma, Lymph Node Metastasis, Laboratory Panel, Decision tree