Survival analysis of patients with extrahepatic cholangiocarcinoma: A nomogram for clinical and MRI features

Author:

Zeng Yan Yan1,Wang Xiao Yong1,Wu Jiao Jiao2,Wang Li Min1,Shi Feng2,Shu Jian1

Affiliation:

1. Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China

2. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China

Abstract

Abstract Background This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features. Methods A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy. Results Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion’s position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions Conclusions Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.

Publisher

Research Square Platform LLC

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