The Value of LI-RADS and Radiomic Features from MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma within 5 cm

作者：Bing Feng, Leyao Wang, Yongjian Zhu, Xiaohong Ma, Rong Cong, Wei Cai, Siyun Liu, Jiesi Hu, Sicong Wang, Xinming Zhao · 发表于：Academic Radiology · 年份：2024 · DOI：10.1016/j.acra.2023.12.007 · 被引用次数：10 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

RATIONALE AND OBJECTIVES: To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm. METHODS: We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively. RESULTS: Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC＜5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination mo...