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

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Journal of hepatology 2025 Vol.83(2) p. 426-439
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Ding W, Meng Y, Ma J, Pang C, Wu J, Tian J, Yu J, Liang P, Wang K

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[BACKGROUND & AIMS] Accurate multi-classification is a prerequisite for appropriate management of focal liver lesions (FLLs).

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • 표본수 (n) 2,088

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BibTeX ↓ RIS ↓
APA Ding W, Meng Y, et al. (2025). Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions.. Journal of hepatology, 83(2), 426-439. https://doi.org/10.1016/j.jhep.2025.01.011
MLA Ding W, et al.. "Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions.." Journal of hepatology, vol. 83, no. 2, 2025, pp. 426-439.
PMID 39848548

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 in training set B and an internal test set. Model 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 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 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) 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 means that even patients in remote areas can be accurately diagnosed through examination by junior radiologists.

MeSH Terms

Humans; Ultrasonography; Contrast Media; Liver Neoplasms; Female; Male; Artificial Intelligence; Middle Aged; Adult; Cholangiocarcinoma; Liver; Aged; Magnetic Resonance Imaging; Carcinoma, Hepatocellular

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