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26.03.2019 | Hepatobiliary-Pancreas | Ausgabe 7/2019

European Radiology 7/2019

A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma

Zeitschrift:
European Radiology > Ausgabe 7/2019
Autoren:
Gu-Wei Ji, Fei-Peng Zhu, Yu-Dong Zhang, Xi-Sheng Liu, Fei-Yun Wu, Ke Wang, Yong-Xiang Xia, Yao-Dong Zhang, Wang-Jie Jiang, Xiang-Cheng Li, Xue-Hao Wang
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s00330-019-06142-7) contains supplementary material, which is available to authorized users.
Gu-Wei Ji and Fei-Peng Zhu contributed equally to this work.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Objectives

This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.

Methods

For this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.

Results

The radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.

Conclusions

Our radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.

Key Points

• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.
• Prognosis of high-risk patients remains dismal after curative-intent resection.
• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

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