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

Abstract

Background & aims: Accurate multi-classification is a prerequisite for appropriate management of focal liver lesions (FLLs). Ultrasound is the most common imaging examination but lacks accuracy. Contrast-enhanced ultrasound (CEUS) offers better performance but is highly dependent on operator 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.

Methods: Since January 2017 to December 2023, CEUS videos, immunohistochemical biomarkers and clinical information on solid FLLs >1 cm in adults were collected from 52 centers to build and test the model. The model was developed 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 a validation set. Then, three modules were aggregated as Model^-DCB in training set B and an internal test set. Model^-DCB performance was compared with CEUS and MRI radiologists in three external test sets.

Results: In total 3,725 FLLs from 52 centers were divided into training set A (n = 2,088), the validation set (n = 592), training set B (n = 234), the internal test set (n = 110), and external test sets A (n = 113), B (n = 276) and C (n = 312). In external test sets A, B and C, Model^-DCB achieved significantly better performance (accuracy from 0.85 to 0.86) than junior CEUS radiologists (0.59-0.73), and comparable performance 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.

Conclusions: CEUS-based Model^-DCB provides accurate multi-classification of FLLs. It holds promise for a wide range of populations, especially those in remote areas who have difficulty accessing MRI.

Clinical trial: NCT04682886.

Impact and implications: Ultrasound is the most common imaging examination for screening focal liver lesions (FLLs), but it lacks accuracy for multi-classification, which is a prerequisite for appropriate clinical management. Contrast-enhanced ultrasound (CEUS) offers better diagnostic performance but relies on the experience of radiologists. We developed a CEUS-based model (Model^-DCB) that can help junior CEUS radiologists to achieve comparable diagnostic ability as senior CEUS radiologists and senior MRI radiologists. The combination of an ultrasound device, CEUS examination and Model^-DCB means that even patients in remote areas can be accurately diagnosed through examination by junior radiologists.

Keywords: Contrast-enhanced ultrasound; Deep learning; Focal liver lesion multi-classification; immunohistochemical biomarker; multi-center retrospective and prospective study.