ReviewCancers2023
Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS.BMC medical imaging · 2026Article
- Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.Abdominal radiology (New York) · 2026Article
- SMC-LUD:Large-Scale B-Mode Liver Ultrasound Dataset for Hepatocellular Carcinoma and Hemangioma Classification.Scientific data · 2026Article
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Recent Advances in Magnetic Resonance Imaging for the Diagnosis of Liver Cancer: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Current Advances in Classification, Prediction and Management of Microvascular Invasion in Hepatocellular Carcinoma.Journal of cellular and molecular medicine · 2025Review
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model.Journal of bone oncology · 2025Article
- Multiple machine learning algorithms identified SLC6A8 as a diagnostic biomarker of the late stage of Hepatocellular carcinoma.Discover oncology · 2025Article
- Mixture of Expert-Based SoftMax-Weighted Box Fusion for Robust Lesion Detection in Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Diagnostic Performance of Deep Learning Applications in Hepatocellular Carcinoma Detection Using Computed Tomography Imaging.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Article
- Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning.BMC medical imaging · 2024Article
- [Hepatocellular Carcinoma-Derived Exosomes: Key Players in Intercellular Communication Within the Tumor Microenvironment].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87-91), the specificity was 90% (95% CI: 87-92), and the AUC was 0.95 (95% CI: 0.93-0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.
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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.