Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma

Zewen Han; Hanting Dai; Xiaolin Chen; Lanmei Gao; Xiaojie Chen; Chuan Yan; Rongping Ye; Yueming Li

doi:10.3389/fphys.2023.1138239

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma

Front Physiol. 2023 Aug 3:14:1138239. doi: 10.3389/fphys.2023.1138239. eCollection 2023.

Authors

Zewen Han^{1

2}, Hanting Dai^{1

2}, Xiaolin Chen¹, Lanmei Gao¹, Xiaojie Chen¹, Chuan Yan¹, Rongping Ye¹, Yueming Li^{1

3}

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
² School of Medical Imaging, Fujian Medical University, Fuzhou, China.
³ Key Laboratory of Radiation Biology (Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China.

Abstract

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors. Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram. Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment.

Keywords: glypican-3; hepatocellular carcinoma; machine learning; magnetic resonance imaging; radiomics.

Grants and funding

This study has been supported by the Natural Science Foundation of Fujian Province (CN) (Award Number: 2022J02033).