Dataset Information

Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype.

ABSTRACT:

Background

Macrotrabecular hepatocellular carcinoma (MTHCC) has a poor prognosis and is difficult to diagnose preoperatively. The purpose is to build and validate MRI-based models to predict the MTHCC subtype.

Methods

Two hundred eight patients with confirmed HCC were enrolled. Three models (model 1: clinicoradiologic model; model 2: fusion radiomics signature; model 3: combined model 1 and model 2) were built based on their clinical data and MR images to predict MTHCC in training and validation cohorts. The performance of the models was assessed using the area under the curve (AUC). The clinical utility of the models was estimated by decision curve analysis (DCA). A nomogram was constructed, and its calibration was evaluated.

Results

Model 1 is easier to build than models 2 and 3, with a good AUC of 0.773 (95% CI 0.696-0.838) and 0.801 (95% CI 0.681-0.891) in predicting MTHCC in training and validation cohorts, respectively. It performed slightly superior to model 2 in both training (AUC 0.747; 95% CI 0.689-0.806; p = 0.548) and validation (AUC 0.718; 95% CI 0.618-0.810; p = 0.089) cohorts and was similar to model 3 in the validation (AUC 0.866; 95% CI 0.801-0.928; p = 0.321) but inferior in the training (AUC 0.889; 95% CI 0.851-0.926; p = 0.001) cohorts. The DCA of model 1 had a higher net benefit than the treat-all and treat-none strategy at a threshold probability of 10%. The calibration curves of model 1 closely aligned with the true MTHCC rates in the training (p = 0.355) and validation sets (p = 0.364).

Conclusion

The clinicoradiologic model has a good performance in diagnosing MTHCC, and it is simpler and easier to implement, making it a valuable tool for pretherapeutic decision-making in patients.

SUBMITTER: Bilal Masokano I

PROVIDER: S-EPMC9772375 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype.

Bilal Masokano Ismail I Pei Yigang Y Chen Juan J Liu Wenguang W Xie Simin S Liu Huaping H Feng Deyun D He Qiongqiong Q Li Wenzheng W

Insights into imaging 20221221 1

<h4>Background</h4>Macrotrabecular hepatocellular carcinoma (MTHCC) has a poor prognosis and is difficult to diagnose preoperatively. The purpose is to build and validate MRI-based models to predict the MTHCC subtype.<h4>Methods</h4>Two hundred eight patients with confirmed HCC were enrolled. Three models (model 1: clinicoradiologic model; model 2: fusion radiomics signature; model 3: combined model 1 and model 2) were built based on their clinical data and MR images to predict MTHCC in training ...[more]

PMID: 36544029

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Project description:Background The recently described pathological subtype of hepatocellular carcinoma (HCC), named macrotrabecular massive (MTM), is associated with an unfavorable prognosis. This study aimed to evaluate the potential for tumor metabolism obtained by β-2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) to be used as a preoperative imaging indicator for predicting MTM-HCCs. Methods This study was designed to be cross-sectional. Patients who underwent preoperative 18F-FDG PET/CT and who had surgically-diagnosed HCC between June 2015 and June 2021 were retrospectively included. Tumor metabolism was determined by the tumor-to-normal liver standardized uptake value ratio (TLR) of the primary tumor as shown on 18F-FDG PET/CT. Clinical, pathological, and PET/CT characteristics were compared between non-MTM-HCCs and MTM-HCCs. Univariate analyses were used to screen the predictive factors of MTM-HCCs, then multivariate binary logistic regression analyses were performed. A regression-based diagnostic model was then established. Substantial necrosis was assessed to compare the predictive performance between traditional imaging and TLR measured on 18F-FDG PET/CT. The receiver operating characteristic (ROC) curve analyses and the DeLong test were used to assess the predictive performance. Results A total of 93 patients (mean age, 52.6±11.3 years; 81 male) with 36 MTM-HCCs were included. Multivariate binary logistic regression analyses identified higher platelet count [PLT; ≥118.5×103/µL; odds ratio (OR), 3.63; 95% confidence interval (CI), 1.13–12.87; P=0.035], higher aspartate transaminase (AST; ≥52 IU/L; OR, 4.15; 95% CI: 1.34–14.33; P=0.017), and larger TLR (≥2.2; OR, 5.55; 95% CI: 1.90–17.56; P=0.002) as independent predictors of MTM-HCCs. A TLR ≥2.2 helped to identify 72.2% of the MTM-HCCs with a specificity of 75.4%. The AUC of the regression-based diagnostic model for predicting MTM-HCCs was 0.835 (95% CI: 0.746–0.923), with a sensitivity of 80.6% and a specificity of 78.9%. Substantial necrosis enabled the identification of MTM-HCCs with 52.8% sensitivity and 87.7% specificity, with an AUC of 0.702 (95% CI: 0.588–0.817). There was no statistical difference between TLR and substantial necrosis in predicting MTM-HCCs using the DeLong test (P>0.05). Conclusions Tumor metabolism determined by TLR on 18F-FDG PET/CT is a valuable imaging indicator for MTM-HCCs. Noninvasive prediction of this subtype can achieve good sensitivity and excellent predictive performance based on the regression model of AST, PLT, and TLR.

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Dataset Information

Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype.

Background

Methods

Results

Conclusion

Publications

Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype.

Similar Datasets

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