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

Authors :
Zeng Y
Wang X
Wu J
Wang L
Shi F
Shu J
Source :
BMC medical imaging [BMC Med Imaging] 2024 Jan 02; Vol. 24 (1), pp. 7. Date of Electronic Publication: 2024 Jan 02.
Publication Year :
2024

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.<br />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.<br />Results: Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion's position, the signal intensity ratio of liver to paraspinal muscle (SIR <subscript>Liver/Muscle</subscript> ), and the signal intensity ratio of spleen to paraspinal muscle (SIR <subscript>Spleen/Muscle</subscript> ) 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.<br />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.<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
1471-2342
Volume :
24
Issue :
1
Database :
MEDLINE
Journal :
BMC medical imaging
Publication Type :
Academic Journal
Accession number :
38166729
Full Text :
https://doi.org/10.1186/s12880-023-01188-y