A Nomogram Model to Predict Post-Progression Survival in Esophageal Squamous Cell Carcinoma Patients With Recurrence After Radical Resection

Autor: Changsen Leng, Yingying Cui, Junying Chen, Kexi Wang, Hong Yang, Jing Wen, Jianhua Fu, Qianwen Liu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Frontiers in Oncology, Vol 12 (2022)
Druh dokumentu: article
ISSN: 2234-943X
DOI: 10.3389/fonc.2022.925685
Popis: BackgroundEsophageal squamous cell carcinoma (ESCC) is characterized clinically by frequent recurrence, leading to a poor prognosis after radical surgery. The aim of this study was to identify a prognostic nomogram to predict the post-progression survival (PPS) of ESCC patients based on the features of primary tumor and recurrence.MethodsA total of 234 ESCC patients who underwent recurrence after radical surgery were enrolled in this study. The independent prognostic factors screened by the univariate and multivariate Cox regression analysis were subsequently used to construct a nomogram. The predictive performance of the nomogram was evaluated with the concordance index (C-index), decision curve, and the area under the receiver operating characteristic curve (AUC) and validated in two validation cohorts. The Kaplan-Meier curves of different recurrence patterns were analyzed.ResultsThe prognostic nomogram of PPS was established by integrating independent prognostic factors, including age, body mass index, number of lymph node dissection, recurrence pattern, and recurrence treatment. The nomogram demonstrated good performance, with C-index values of 0.756, 0.817, and 0.730 for the training and two validation cohorts. The 1-year AUC values were 0.773, 0.798, and 0.735 and 3-year AUC values were 0.832, 0.871, and 0.791, respectively. Furthermore, we found that patients with bone metastasis displayed the worst PPS compared to other isolated recurrence patterns.ConclusionWe constructed a nomogram to reliably predict PPS, which would be valuable to provide individual managements for ESCC patients after radical surgery.
Databáze: Directory of Open Access Journals