A Population-Based Study: How to Identify High-Risk T1–2 Esophageal Cancer Patients?

Frontiers in Oncology

Jing Li¹  Chao Lu²  Shuangshuang Wu³  Yiming Qi⁴  Guoshu Xu⁴  Zhengquan Feng⁴  Jiabin Chen⁴  Yanli Wan⁵  Linghui Tao⁶ 

[1] Cancer Institute of Integrated Tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China;Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China;Department of Geriatrics, Tongde Hospital of Zhejiang Province, Hangzhou, China;Department of Oncology, Tongde Hospital of Zhejiang Province, Hangzhou, China;National Medicine Clinical Trial Organization Office, Tongde Hospital of Zhejiang Province, Hangzhou, China;The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China;

关键词: lymph node metastasis;    distant metastasis;    overall survival;    nomogram;    SEER database;    T1–2 esophageal cancer;   

DOI  :  10.3389/fonc.2021.766181

来源: DOAJ

【 摘 要 】

BackgroundDue to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1–2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1–2 ESCA patients is of great significance to clinical practice.MethodsA total of 1,747 T1–2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively.ResultsThe age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1–2 ESCA patients (p < 0.05). The age, T-stage, tumor grade, and tumor size were significant factors for predicting DM of T1–2 ESCA patients (p < 0.05). The age, race, sex, histology, primary tumor site, tumor size, N-stage, M-stage, and surgery were significant factors for predicting OS of T1–2 ESCA patients (p < 0.05). The C-indexes of the three nomograms constructed by these factors were 0.737, 0.764, and 0.740, respectively, suggesting that they were clinically effective.ConclusionsThe newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1–2 ESCA patients, which contribute to the individualized decision making before clinical management.

【 授权许可】

Unknown