B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma

Dan Lu; Cheng Qin; Li-Fan Wang; Ling-Ling Li; Yu Li; Li-Ping Sun; Hui Shi; Bo-Yang Zhou; Xin Guan; Yao Miao; Hong Han; Jian-Hua Zhou; Hui-Xiong Xu; Chong-Ke Zhao

doi:10.1186/s13089-025-00452-2

B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma

Ultrasound J. 2025 Oct 17;17(1):53. doi: 10.1186/s13089-025-00452-2.

Authors

Dan Lu^#¹, Cheng Qin^#², Li-Fan Wang¹, Ling-Ling Li³, Yu Li³, Li-Ping Sun⁴, Hui Shi⁴, Bo-Yang Zhou¹, Xin Guan¹, Yao Miao¹, Hong Han¹, Jian-Hua Zhou³, Hui-Xiong Xu⁵, Chong-Ke Zhao⁶

Affiliations

¹ Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
² School of Future Technology, Shanghai University, Shanghai, China.
³ 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.
⁴ Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
⁵ Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. xuhuixiong@126.com.
⁶ Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. zhaochongke123@163.com.

^# Contributed equally.

Abstract

Background: 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).

Methods: 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_R model (based on BMUS radiomics features), BM + CEUS_R model (based on BMUS and CEUS radiomics features) and hybrid_R+C model (integrated clinical indicators and radiomic features) were established. These radiomics models' performance was compared with conventional clinic-radiological (C_C+R) 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.

Results: Among ten distinct machine learning classifiers evaluated, the AdaBoost algorithm demonstrated the highest classification performance. The AUCs of the BM + CEUS_R model for identifying MTM-HCC were higher than the BMUS_R 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_R model and the hybrid_R+C model in either set (p > 0.05). Additionally, the AdaBoost-based BM + CEUS_R model showed promising in stratifying early recurrence-free survival, with p < 0.001.

Conclusion: The AdaBoost-based BM + CEUS_R model shows promise as a tool for preoperatively identifying MTM-HCC and may also be beneficial in predicting prognosis.

Keywords: Contrast enhanced ultrasound; Macrotrabecular-massive hepatocellular carcinoma; Prognosis; Radiomics; SHapley additive explanations.

Abstract

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