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A Multi-Parametric Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion Status in Intrahepatic Cholangiocarcinoma.

Authors :
Qian, Xianling
Lu, Xin
Ma, Xijuan
Zhang, Ying
Zhou, Changwu
Wang, Fang
Shi, Yibing
Zeng, Mengsu
Source :
Frontiers in Oncology; 2/24/2022, Vol. 12, p1-12, 12p
Publication Year :
2022

Abstract

Background: Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer with increasing incidence in the last decades. Microvascular invasion (MVI) is a poor prognostic factor for patients with ICC, which correlates early recurrence and poor prognosis, and it can affect the selection of personalized therapeutic regime. Purpose: This study aimed to develop and validate a radiomics-based nomogram for predicting MVI in ICC patients preoperatively. Methods: A total of 163 pathologically confirmed ICC patients (training cohort: n = 130; validation cohort: n = 33) with postoperative Ga-DTPA-enhanced MR examination were enrolled, and a time-independent test cohort (n = 24) was collected for external validation. Univariate and multivariate analyses were used to determine the independent predictors of MVI status, which were then incorporated into the MVI prediction nomogram. Least absolute shrinkage and selection operator logistic regression was performed to select optimal features and construct radiomics models. The prediction performances of models were assessed by receiver operating characteristic (ROC) curve analysis. The performance of the MVI prediction nomogram was evaluated by its calibration, discrimination, and clinical utility. Results: Larger tumor size (p = 0.003) and intrahepatic duct dilatation (p = 0.002) are independent predictors of MVI. The final radiomics model shows desirable and stable prediction performance in the training cohort (AUC = 0.950), validation cohort (AUC = 0.883), and test cohort (AUC = 0.812). The MVI prediction nomogram incorporates tumor size, intrahepatic duct dilatation, and the final radiomics model and achieves excellent predictive efficacy in training cohort (AUC = 0.953), validation cohort (AUC = 0.861), and test cohort (AUC = 0.819), fitting well in calibration curves (p > 0.05). Decision curve and clinical impact curve further confirm the clinical usefulness of the nomogram. Conclusion: The nomogram incorporating tumor size, intrahepatic duct dilatation, and the final radiomics model is a potential biomarker for preoperative prediction of the MVI status in ICC patients. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2234943X
Volume :
12
Database :
Complementary Index
Journal :
Frontiers in Oncology
Publication Type :
Academic Journal
Accession number :
155430355
Full Text :
https://doi.org/10.3389/fonc.2022.838701