Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

• Published in: Journal of magnetic resonance imaging 2022-03, Vol.55 (3), p.787-802
• Main Authors: Zhao, Jian, Zhang, Wei, Zhu, Yuan‐Yi, Zheng, Hao‐Yu, Xu, Li, Zhang, Jun, Liu, Si‐Yun, Li, Fu‐Yu, Song, Bin
• Format: Article
• Language: English
• Subjects: Bile Duct Neoplasms - diagnostic imaging; Bile Duct Neoplasms - surgery; Chi-square test; Cholangiocarcinoma; Correlation analysis; Early experience; early recurrence; Feature extraction; Female; Field strength; Humans; Image contrast; Image enhancement; Klatskin Tumor - diagnostic imaging; Klatskin Tumor - surgery; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mathematical models; Metastases; Middle Aged; Modelling; Multivariate analysis; Nomograms; Patients; perihilar cholangiocarcinoma; Population studies; Portal vein; Predictions; Radiomics; Retrospective Studies; Statistical analysis; Statistical tests; Training; Tumors
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.27846

Background Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. Purpose To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. Study Type Retrospective. Population One hundred eighty‐four patients (median age, 61.0 years; interquartile range: 53.0–66.8 years) including 115 men and 69 women. Field Strength/Sequence A 1.5 T; volumetric interpolated breath‐hold examination (VIBE) sequence. Assessment The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast‐enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic‐radiomics nomogram (Modelcombine). Statistical Tests Chi‐squared (χ2) test or Fisher's exact test, Mann–Whitney U‐test or t‐test, Delong test. Two tailed P