ArticleIEEE journal of biomedical and health informatics2023
Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.
Article in IEEE journal of biomedical and health informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Multidimensional Dual Encoding Network For Liver Lesion Classification From Multi-Phase Magnetic Resonance Imaging.Journal of imaging informatics in medicine · 2026Article
- LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.Abdominal radiology (New York) · 2026Review
- Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- User-preference alignment with uncertainty-aware interactive rectification for liver organ and tumor segmentation and analysis from CT images.NPJ digital medicine · 2026Article
- Radiomics for Detection and Differentiation of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.Cancers · 2026Review
- Transformer-enhanced deep ensemble for multi-class liver disease classification using computed tomography images.Scientific reports · 2026Article
- Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification.JCO clinical cancer informatics · 2026Article
- Comparative multiphase CT radiomics for differentiating isolated hepatic metastasis from intrahepatic cholangiocarcinoma: a two-center study of native, subtraction, peritumoral, and fusion models.Frontiers in oncology · 2026Article
- Interpretable ADC-based radiomics models for differentiating hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Frontiers in oncology · 2026Article
- Foundation model-guided multi-view semi-supervised CT segmentation of liver tumors in resource-constrained settings.NPJ digital medicine · 2025Article
- A computer vision approach for the classification of multi liver tumor using computed tomography scan.BMC medical imaging · 2025Article
- Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges.World journal of gastrointestinal oncology · 2025Review
- Serum levels of AFP and CA19-9 after intraoperative radiotherapy combined with drug therapy on liver and pancreatic tumors.Journal of medical biochemistry · 2025Article
- Fully Automatic Artificial Intelligence Liver Anatomy Segmentation in the Management of Colorectal Liver Metastases.Cureus · 2025Article
- Review
- Artificial intelligence for detection and characterization of focal hepatic lesions: a review.Abdominal radiology (New York) · 2025Review
- Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.The Journal of international medical research · 2024Review
- Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
The liver is a frequent site of benign and malignant, primary and metastatic tumors. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancers, and colorectal liver metastasis (CRLM) is the most common secondary liver cancer. Although the imaging characteristic of these tumors is central to optimal clinical management, it relies on imaging features that are often non-specific, overlap, and are subject to inter-observer variability. Thus, in this study, we aimed to categorize liver tumors automatically from CT scans using a deep learning approach that objectively extracts discriminating features not visible to the naked eye. Specifically, we used a modified Inception v3 network-based classification model to classify HCC, ICC, CRLM, and benign tumors from pretreatment portal venous phase computed tomography (CT) scans. Using a multi-institutional dataset of 814 patients, this method achieved an overall accuracy rate of 96%, with sensitivity rates of 96%, 94%, 99%, and 86% for HCC, ICC, CRLM, and benign tumors, respectively, using an independent dataset. These results demonstrate the feasibility of the proposed computer-assisted system as a novel non-invasive diagnostic tool to classify the most common liver tumors objectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.