The Application Value of MRI T 2 ∗ WI Radiomics Nomogram in Discriminating Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma

Objective. To establish and validate an MRI T 2 ∗ WI-based radiomics nomogram model and to discriminate hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICCA). Methods. 174 patients were retrospectively collected, who were diagnosed with primary hepatic carcinoma by surgery or pu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computational and mathematical methods in medicine 2022-09, Vol.2022, p.1-13
Hauptverfasser: Huang, Feng, Liu, Xiaoyun, Liu, Peng, Xu, Dan, Li, Zeda, Lin, Huashan, Xie, An
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Objective. To establish and validate an MRI T 2 ∗ WI-based radiomics nomogram model and to discriminate hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICCA). Methods. 174 patients were retrospectively collected, who were diagnosed with primary hepatic carcinoma by surgery or puncture pathology and received preoperative MRI scans including T 2 ∗ WI scans. There were 113 cases of HCC and 61 cases of mass-type ICCA. T 2 ∗ WI was used for feature extraction, the extent of the lesions was manually outlined at the largest lesions layer of the T 2 ∗ WI, and the feature dimension reduction was performed by the mRMR and LASSO to obtain the optimal feature set. The radiomics features and clinical risk factors were combined to establish the radiomics nomogram model. In both training and validation groups, calibration curves and ROC curves were applied to validate the efficacy of the established model. Finally, calibration curves were applied to assess the degree of fitting and DCA to assess the clinical utility of the established model. Results. The radiomics model had the AUC of 0.90 (95% CI, 0.85–0.96) and 0.91 (95% CI, 0.83–0.99) in the training and validation groups, respectively; the AUC of the radiomics nomogram was 0.97 (95% CI, 0.94–0.99) in the training group and 0.95 (95% CI, 0.95–0.99) in the validation group. DCA suggested the clinical application value of the nomogram model. Conclusion. Radiomics nomogram model based on MRI T 2 ∗ WI scan without enhancement can be used to discriminate HCC from ICCA.
ISSN:1748-670X
1748-6718
DOI:10.1155/2022/7099476