Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions.

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Accurate multi-classification is the prerequisite for reasonable management of focal liver lesions (FLLs). Ultrasound is the common image examination, but lacks accuracy. Contrast enhanced ultrasound (CEUS) offers better performance, but highly relies on experience. Therefore, we aimed to develop a CEUS-based artificial intelligence (AI) model for FLL multi-classification and evaluate its performance in multicenter clinical tests. Since January 2017 to December 2023, CEUS videos, immunohistochemical biomarkers and clinical information of solid FLLs>1cm in adults were collected from 52 centers to build and test the model. It aimed to classify FLLs into six types: hepatocellular carcinoma, hepatic metastasis, intrahepatic cholangiocarcinoma, hepatic hemangioma, hepatic abscess and others. First, Module-Disease, Module-Biomarker and Module-Clinic were built in training set A and validation set. Then, three modules were aggregated as Model-DCB in training set B and internal test set. Model-DCB performance was compared with CEUS and MRI radiologists in three external test sets. In total 3725 FLLs from 52 centers were divided into training set A (n=2088), validation set (n=592), training set B (n=234), internal test set (n=110), external test set A (n=113), B (n=276) and C (n=312). In external test sets A, B and C, Model-DCB all achieved significantly better performance (Accuracy from 0.85 to 0.86) than junior CEUS-radiologists (0.59-0.73), and comparable to senior CEUS-radiologists (0.79-0.85) and senior MRI-radiologists (0.82-0.86). In multiple subgroup analyses on demographic characteristics, tumor characteristics and ultrasound devices, its accuracy ranged from 0.79 to 0.92. CEUS-based Model-DCB provides accurate multi-classification of FLLs. It holds promise to benefit a wide range of population, especially for patients in remote suburban areas who have difficulty accessing MRI. Ultrasound is the most common image examination for screening focal liver lesions (FLLs), but it lacks accuracy for multi-classification, which is the prerequisite for reasonable management. Contrast enhanced ultrasound (CEUS) offers better diagnostic performance, but highly relies on work experience of radiologists. We develop a CEUS-based model (Model-DCB) can assist junior CEUS radiologists to achieve comparable diagnostic ability to senior CEUS radiologists and senior MRI radiologists. The combination of ultrasound device, CEUS examination and Model-DCB enables even patients in remote areas to obtain excellent diagnostic performance through examination by junior radiologists. NCT04682886.