1. Multiphase MRI-Based Radiomics for Predicting Histological Grade of Hepatocellular Carcinoma.
- Author
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Yan Y, Si Z, Chun C, Chao-Qun P, Ke M, Dong Z, and Li W
- Subjects
- Humans, Female, Male, Middle Aged, Retrospective Studies, Aged, Neoplasm Grading, Adult, Liver diagnostic imaging, Liver pathology, ROC Curve, Image Processing, Computer-Assisted methods, Reproducibility of Results, Radiomics, Carcinoma, Hepatocellular diagnostic imaging, Carcinoma, Hepatocellular pathology, Liver Neoplasms diagnostic imaging, Liver Neoplasms pathology, Magnetic Resonance Imaging methods, Gadolinium DTPA, Contrast Media
- Abstract
Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer. Accurate preoperative prediction of histological grade holds potential for improving clinical management and disease prognostication., Purpose: To evaluate the performance of a radiomics signature based on multiphase MRI in assessing histological grade in solitary HCC., Study Type: Retrospective., Subjects: A total of 405 patients with histopathologically confirmed solitary HCC and with liver gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI within 1 month of surgery., Field Strength/sequence: Contrast-enhanced T1-weighted spoiled gradient echo sequence (LAVA) at 1.5 or 3.0 T., Assessment: Tumors were graded (low/high) according to results of histopathology. Basic clinical characteristics (including age, gender, serum alpha-fetoprotein (AFP) level, history of hepatitis B, and cirrhosis) were collected and tumor size measured. Radiomics features were extracted from Gd-EOB-DTPA-enhanced MRI data. Three feature selection strategies were employed sequentially to identify the optimal features: SelectFromModel (SFM), SelectPercentile (SP), and recursive feature elimination with cross-validation (RFECV). Probabilities of five single-phase radiomics-based models were averaged to generate a radiomics signature. A combined model was built by combining the radiomics signature and clinical predictors., Statistical Tests: Pearson χ
2 test/Fisher exact test, Wilcoxon rank sum test, interclass correlation coefficient (ICC), univariable/multivariable logistic regression analysis, area under the receiver operating characteristic (ROC) curve (AUC), DeLong test, calibration curve, Brier score, decision curve, Kaplan-Meier curve, and log-rank test. A P-value <0.05 was considered statistically significant., Results: High-grade HCCs were present in 33.8% of cases. AFP levels (odds ratio [OR] 1.89) and tumor size (>5 cm; OR 2.33) were significantly associated with HCC grade. The combined model had excellent performance in assessing HCC grade in the test dataset (AUC: 0.801), and demonstrated satisfactory calibration and clinical utility., Data Conclusion: A model that combined a radiomics signature derived from preoperative multiphase Gd-EOB-DTPA-enhanced MRI and clinical predictors showed good performance in assessing HCC grade., Level of Evidence: 3 TECHNICAL EFFICACY: Stage 5., (© 2024 International Society for Magnetic Resonance in Medicine.)- Published
- 2024
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