ArticleAmerican journal of cancer research2022
5-year recurrence prediction after hepatocellular carcinoma resection: deep learning vs. Cox regression models.
Article in American journal of cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
6 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
- Current Immunotherapy Strategies and Emerging Biomarkers for the Treatment of Hepatocellular Carcinoma.Cancers · 2025Review
- Pre- and postoperative predictors of extrahepatic recurrence after curative resection for hepatocellular carcinoma.BMC cancer · 2025Article
- Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentation Mastopexy.Aesthetic plastic surgery · 2024Article
- Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.Frontiers in medicine · 2024Article
- Systemic Neoadjuvant and Adjuvant Therapies in the Management of Hepatocellular Carcinoma-A Narrative Review.Cancers · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning algorithms have yet to be used for predicting clinical prognosis after cancer surgery. Therefore, this study compared performance indices and permutation importance of potential confounders in three models for predicting 5-year recurrence after hepatocellular carcinoma (HCC) resection: a deep-learning deep neural network (DNN) model, a recurrent neural network (RNN) model, and a Cox proportional hazard (CPH) regression model. Data for 725 patients who had received HCC resection at three medical centers in southern Taiwan between April, 2011, and December, 2015, were randomly divided into three datasets: a training dataset containing data for 507 subjects was used for model development, a testing dataset containing data for 109 subjects was used for internal validation, and a validating dataset containing data for 109 subjects was used for external validation. Feature importance analysis was also performed to identify potential predictors of recurrence after HCC resection. Univariate Cox proportional hazards regression analyses were performed to identify potential significant predictors of 5-year recurrence after HCC resection, which were included in the forecasting models (
Indexed as
Identifiers
35812048PMC9251698W4285048611What OpenQuestion holds
Registered trials
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