AUTHOR=Liu Peijun , Li Weiqiu , Qiu Ganbin , Chen Jincan , Liu Yonghui , Wen Zhongyan , Liang Mei , Zhao Yue 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1142916

DOI=10.3389/fonc.2023.1142916

ISSN=2234-943X

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 ×10 3 mm 2 /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. Moreover, the prediction model constructed based on these significant variables demonstrated that the best predictive performance was obtained with an area under the receiver operator characteristic curve of 0.909, an area under the precision-recall curve of 0.963, and an F1 score of 0.887; for the validation set, these values were 0.853, 0.936, and 0.843, respectively.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.