ArticleWorld journal of gastrointestinal oncology2022
Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma.
Article in World journal of gastrointestinal oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- AI-Driven Predictive Models of Early Recurrence of HCC After Surgical Resection: A Systematic Review.Cancers · 2026Review
- Multimodal data-driven prediction of postoperative recurrence and survival in hepatocellular carcinoma: a narrative review.Journal of gastrointestinal oncology · 2026Review
- Evaluation of therapeutic efficacy in hepatocellular carcinoma ablation based on CT radiomics and deep learning multimodal models.Frontiers in oncology · 2026Article
- Deep Learning with Contrast-Enhanced Ultrasound for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma After Hepatectomy.Journal of multidisciplinary healthcare · 2025Article
- Artificial intelligence in liver cancer - new tools for research and patient management.Nature reviews. Gastroenterology & hepatology · 2024Review
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Radiological findings in non-surgical recurrent hepatocellular carcinoma: From locoregional treatments to immunotherapy.World journal of gastroenterology · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC) is the most common primary liver malignancy.
aimTo predict early recurrence (ER) and overall survival (OS) in patients with HCC after radical resection using deep learning-based radiomics (DLR).
methodsA total of 414 consecutive patients with HCC who underwent surgical resection with available preoperative grayscale and contrast-enhanced ultrasound images were enrolled. The clinical, DLR, and clinical + DLR models were then designed to predict ER and OS.
resultsThe DLR model for predicting ER showed satisfactory clinical benefits [area under the curve (AUC)] = 0.819 and 0.568 in the training and testing cohorts, respectively), similar to the clinical model (AUC = 0.580 and 0.520 in the training and testing cohorts, respectively;
conclusionCompared to the clinical model, the clinical + DLR model significantly improves the accuracy of predicting OS in HCC patients after radical resection.
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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.