ArticleFrontiers in oncology2021
US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 17 citations in OpenAlex.
- Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Deep learning model integrating contrast-enhanced ultrasound spatiotemporal imaging with clinical data for the differential diagnosis between hepatocellular carcinoma and intrahepatic cholangiocarcinoma.La Radiologia medica · 2026Article
- Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Abdominal radiology (New York) · 2024Article
- Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- Review
- Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study.Frontiers in oncology · 2022Article
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Authors and funding
12 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6%
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