ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2022
Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence for Contrast-Enhanced Ultrasound of the Liver: A Systematic Review.Digestion · 2025Pooled it
- Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Deep learning prediction of chemo-immunotherapy response using tumor perfusion ultrasound images.Frontiers in artificial intelligence · 2026Article
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Images Versus Videos in Contrast-Enhanced Ultrasound for Computer-Aided Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Deep-learning-based washout classification for decision support in contrast-enhanced ultrasound examinations of the liver.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Comparison of Radiomics and Deep Learning Using Intestinal Ultrasound Images for Differentiating Inflammatory and Fibrotic Intestinal Strictures in Crohn's Disease.Journal of inflammation research · 2025Article
- Weakly Supervised Deep Learning Can Analyze Focal Liver Lesions in Contrast-Enhanced Ultrasound.Digestion · 2025Article
- AI-assisted anatomical structure recognition and segmentation via mamba-transformer architecture in abdominal ultrasound images.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
- Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients.Frontiers in medicine · 2024Article
- Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians.Diagnostics (Basel, Switzerland) · 2023Article
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- Article
- An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
8 authors.
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Abstract
This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by texture analysis at different time points. Using the extracted features as input, several machine learning classifiers are optimized to achieve semiautomatic FLLs characterization, for which there is no need for motion compensation and the only manual input required is the location of a suspicious lesion. Clinical validation on 87 FLLs from 72 patients at risk for hepatocellular carcinoma (HCC) showed promising performance, achieving a balanced accuracy of 0.84 in the distinction between benign and malignant lesions. Analysis of feature relevance demonstrates that a combination of spatiotemporal and texture features is needed to achieve the best performance. Interpretation of the most relevant features suggests that aspects related to microvascular perfusion and the microvascular architecture, together with the spatial enhancement characteristics at wash-in and peak enhancement, are important to aid the accurate characterization of FLLs.
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Registered trials
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