Evidence map›Paper›PMID 38713432›Full record

ArticleAbdominal radiology (New York)2024

Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study.

Huilin Chen, Hui Dong, Ruilin He, Mengting Gu, Xingyu Zhao, Kairong Song, Wenjie Zou, Ningyang Jia, Wanmin Liu

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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.

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

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

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4 · The record

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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

9 authors.

Huilin Chen *School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Hui Dong *Department of Pathology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.
Ruilin He *School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Mengting GuDepartment of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China.
Xingyu ZhaoDepartment of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China.
Kairong SongDepartment of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.
Wenjie ZouDepartment of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China.
Ningyang JiaDepartment of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China. ningyangjia@163.com.
Wanmin LiuDepartment of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China. 18389376537@163.com.

Funding

Shanghai Municipal Health Commission 2022LJ024
6 · The paper itself

Abstract

backgroundVessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI.

methodsThis study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI).

resultsCA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2.

conclusionHBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.

Indexed as

Carcinoma, HepatocellularContrast MediaLiver NeoplasmsMagnetic Resonance ImagingMeglumineOrganometallic CompoundsAdultAgedFemaleHumansImage EnhancementMaleMiddle AgedPredictive Value of TestsPreoperative CareRetrospective StudiesContrast Mediagadobenic acidMeglumineOrganometallic CompoundsGadobenate dimeglumineHepatocellular carcinomaMagnetic resonance imagingPredictive performanceVessels encapsulating tumor clusters

Identifiers

PMID38713432

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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.