Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging

Frontiers in Oncology

Oncology

Cong Wang¹  Dandan Shi¹  Minghui Wu¹  Changjiang Yu¹  Yuanyuan Lv¹  Miaohui Gao¹  Yiran Zhou¹  Ning Zhang²  Shaocheng Zhu³ 

[1] Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China;Henan Provincial People’s Hospital, Xinxiang Medical University, Xinxiang, China;Henan Provincial People’s Hospital, Xinxiang Medical University, Xinxiang, China;Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China;

关键词: Gd-EOB-DTPA;    hepatocellular carcinoma;    glypican-3;    radiomics;    hepatobiliary phase;    magnetic resonance imaging;   

DOI  :  10.3389/fonc.2023.1209814

 received in 2023-04-21, accepted in 2023-09-12,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionThe hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature.MethodsWe retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC).ResultsIn the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively.ConclusionHBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

【 授权许可】

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Copyright © 2023 Zhang, Wu, Zhou, Yu, Shi, Wang, Gao, Lv and Zhu