Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma

Peijun Liu; Weiqiu Li; Ganbin Qiu; Jincan Chen; Yonghui Liu; Zhongyan Wen; Mei Liang; Yue Zhao

doi:10.3389/fonc.2023.1142916

Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma

Front Oncol. 2023 Nov 9:13:1142916. doi: 10.3389/fonc.2023.1142916. eCollection 2023.

Authors

Peijun Liu^#¹, Weiqiu Li^#², Ganbin Qiu², Jincan Chen², Yonghui Liu², Zhongyan Wen², Mei Liang², Yue Zhao¹

Affiliations

¹ Department of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, China.
² Department of Radiology, The First People's Hospital of Zhaoqing, Zhaoqing, China.

^# Contributed equally.

Abstract

Objectives: The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators.

Methods: As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility.

Results: Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75^th percentile ADC value < 1.48 ×10³ mm²/s and R2* value ≥ 38.6 sec^-1 were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness.

Conclusions: The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.

Keywords: glypican-3; hepatocellular carcinoma; immunotherapy; magnetic resonance imaging; nomogram.

Grants and funding

This study was funded by the Science and Technology innovation of Zhaoqing City, Guangdong Province, China under Grant (No. 2019N008). This work was supported by Medical Scientific Research Foundation of Guangdong Province, China under Grant (No. B2020225).