B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma.
Authors
Affiliations (6)
Affiliations (6)
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
- School of Future Technology, Shanghai University, Shanghai, China.
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China.
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. [email protected].
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. [email protected].
Abstract
This study aimed to develop and validate an interpretable radiomics model using quantitative features from B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) for predicting macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC). From October 2020 to September 2023, 344 patients (mean age: 58.20 ± 10.70 years; 275 men) with surgically resected HCC were retrospectively enrolled from three medical centers. Radiomics features were extracted from BMUS and CEUS, followed by a multiple-step feature selection process. BMUS<sub>R</sub> model (based on BMUS radiomics features), BM + CEUS<sub>R</sub> model (based on BMUS and CEUS radiomics features) and hybrid<sub>R+C</sub> model (integrated clinical indicators and radiomic features) were established. These radiomics models' performance was compared with conventional clinic-radiological (C<sub>C+R</sub>) model using area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) method was used to interpret model performance. The model's potential for predicting recurrence-free survival (RFS) was further analyzed. Among ten distinct machine learning classifiers evaluated, the AdaBoost algorithm demonstrated the highest classification performance. The AUCs of the BM + CEUS<sub>R</sub> model for identifying MTM-HCC were higher than the BMUS<sub>R</sub> model and the conventional clinic-radiological model in both validation (0.880 vs. 0.720 and 0.658, both p < 0.05) and test sets (0.878 vs. 0.605 and 0.594, both p < 0.05). No statistical differences were observed between the BM + CEUS<sub>R</sub> model and the hybrid<sub>R+C</sub> model in either set (p > 0.05). Additionally, the AdaBoost-based BM + CEUS<sub>R</sub> model showed promising in stratifying early recurrence-free survival, with p < 0.001. The AdaBoost-based BM + CEUS<sub>R</sub> model shows promise as a tool for preoperatively identifying MTM-HCC and may also be beneficial in predicting prognosis.