Evidence map›Paper›PMID 39582540›Full record

ArticleFrontiers in oncology2024

Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection.

Ying Zhao, Sen Wang, Yue Wang, Jun Li, Jinghong Liu, Yuhui Liu, Haitong Ji, Wenhan Su, Qinhe Zhang, Qingwei Song and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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  8. Article
  9. MRI Imaging Biomarkers for Prognostication of Hepatocellular Carcinoma.Journal of the Korean Society of Radiology · 2025
    Review
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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

12 authors.

Ying Zhao *Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Sen Wang *Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China.
Yue WangDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Jun LiDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Jinghong LiuDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Yuhui LiuCollege of Medical Imaging, Dalian Medical University, Dalian, China.
Haitong JiCollege of Medical Imaging, Dalian Medical University, Dalian, China.
Wenhan SuCollege of Medical Imaging, Dalian Medical University, Dalian, China.
Qinhe ZhangDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Qingwei SongDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Yu YaoChengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China.
Ailian LiuDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection. Methods: Total 165 HCC patients (ER, Results: The RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes. Conclusion: Both radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.

Indexed as

deep learningearly recurrencehepatocellular carcinomamagnetic resonance imagingradiomics

Identifiers

PMID39582540
PMCPMC11581961

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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.