Evidence map›Paper›PMID 39129938›Full record

ArticleGastro hep advances2022

Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation.

Masaya Sato, Ryosuke Tateishi, Makoto Moriyama, Tsuyoshi Fukumoto, Tomoharu Yamada, Ryo Nakagomi, Mizuki Nishibatake Kinoshita, Takuma Nakatsuka, Tatsuya Minami, Koji Uchino and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Gastro hep advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Technological Advancements in Interventional Oncology.Diagnostics (Basel, Switzerland) · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Masaya SatoDepartment of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Ryosuke TateishiDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Makoto MoriyamaDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Tsuyoshi FukumotoDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Tomoharu YamadaDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Ryo NakagomiDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Mizuki Nishibatake KinoshitaDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takuma NakatsukaDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Tatsuya MinamiDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Koji UchinoDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kenichiro EnookuDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hayato NakagawaDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Shuichiro ShiinaDepartment of Gastroenterology, Juntendo University, Tokyo, Japan.
Kota NinomiyaDepartment of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Satoshi KoderaDepartment of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yutaka YatomiDepartment of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kazuhiko KoikeDepartment of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods: We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models-including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. Results: The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups ( Conclusion: We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.

Indexed as

DeepSurvHepatocellular CarcinomaMachine LearningRadiofrequency Ablation

Identifiers

PMID39129938
PMCPMC11308827

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.