Ultrasound and clinical data improve liver cancer invasion prediction
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Integrating Multimodal Ultrasound and Clinical Data: A Multicenter Study
Computer Vision and Pattern Recognition
Summary
Microvascular invasion in liver cancer is important to know before surgery because it affects recovery and survival, but it is usually only found after surgery. The authors created a computer model that looks at different types of ultrasound images and clinical information to predict this invasion before surgery. They tested their model using data from multiple hospitals and found it made more accurate predictions than using any single type of information. This tool could help doctors better decide how to treat liver cancer patients before surgery.
What this means in practice
- •For radiology teams: Deploy a multimodal ultrasound and clinical data fusion model to improve pre-surgery assessment of microvascular invasion in liver cancer patients.
- •For clinical decision support developers: Incorporate dynamic contrast-enhanced ultrasound data with clinical features into software to better stratify liver cancer surgery risks.$Commercial implications: Enables development of preoperative risk prediction tools for hospitals and clinics to improve treatment planning.
Authors
Jun Cheng, Yuanyuan Kong, Qing Huang, Xiaotong Tan, Licong Dong, Yulong Han, Wufeng Xue, Ruobing Huang, Dong Ni, Qi Yang, Jie Yu, Ping Liang
Abstract
Background: Microvascular invasion (MVI) predicts recurrence and survival in hepatocellular carcinoma (HCC) but requires postoperative histopathology for diagnosis. We developed and validated a model integrating multimodal ultrasound and clinical data for preoperative MVI prediction. Methods: This multicenter study included 489 patients with HCC from eight centers. All patients had B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), dynamic contrast-enhanced ultrasound (DCE-US), and clinical information. Data from seven centers (n = 421) were used for model development with five-fold cross-validation; data from the remaining center (n = 68) formed an independent external validation cohort. The proposed multimodal information fusion network used modality-specific encoders, a hemodynamic temporal change module for bidirectional DCE-US perfusion changes, and a representation consistency learning module to align heterogeneous ultrasound representations before Transformer-based fusion. Results: In external validation, DCE-US achieved the highest single-modality area under the receiver operating characteristic curve (AUC; 0.8545+/-0.0198), versus clinical information (0.6715+/-0.0156), CDFI (0.6435+/-0.0344), and BUS (0.6087+/-0.0417). Pixel-difference sampling and the proposed temporal module outperformed alternative sampling and video representation methods. The full model achieved the best performance, with an AUC of 0.8953+/-0.0180, accuracy of 81.18%+/-2.83%, sensitivity of 86.40%+/-6.69%, and specificity of 78.14%+/-6.28. Conclusions: Integrating multimodal ultrasound and clinical information enabled promising preoperative MVI prediction in HCC. DCE-US was the main source of predictive information, while BUS, CDFI, and clinical information provided complementary value. The proposed framework may support preoperative risk stratification and individualized clinical decision-making.