Evidence map›Paper›PMID 33713159›Full record

ArticleAbdominal radiology (New York)2021

MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm.

Yani Kuang, Renzhan Li, Peng Jia, Wenhai Ye, Rongzhen Zhou, Rui Zhu, Jian Wang, Shuangxiang Lin, Peipei Pang, Wenbin Ji

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In one paragraph

Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 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

34 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  8. Radiomic Analysis Applied to Pretreatment Gd-EOB-DTPA-enhanced MR Imaging Predicts Response to Selective Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
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  9. Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
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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

10 authors.

Yani Kuang *Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
Renzhan Li *Sanmen People's Hospital, Taizhou, China.
Peng JiaFirst People's Hospital of Taizhou city, Zhejiang, China.
Wenhai YeSanmen People's Hospital, Taizhou, China.
Rongzhen ZhouTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
Rui ZhuTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
Jian WangTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
Shuangxiang LinTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
Peipei PangGE Healthcare, Hangzhou, China.
Wenbin JiTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China. 15058301036@163.com.ORCID http://orcid.org/0000-0002-7667-9430

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo construct MRI radiomics nomograms that can predict short-term response after TACE in HCC patients with diameter less than 5 cm.

methodsMRI images and clinical data of 153 cases with tumor diameter less than 5 cm before TACE from 3 hospitals were collected retrospectively and divided into 1 internal training set and 1 external validation set. The T2-weighted imaging (T2WI) and dynamic contrast-enhanced MRI arterial phase (DCE-MR AP) images were studied. Multivariable logistic regression was used to construct Radiomics models, Clinics models, and Nomograms based on T2WI and DCE-MR AP, respectively. The receiver characteristic curve (ROC) was used to evaluate the predictive performance of each model.

resultsIn this study, 113 eligible cases in Hospital 1 were collected as the training set, and 40 eligible cases in other hospitals were used as the verification set. 11 T2WI features and 11 DCE-MRI AP features with the most predictive value were finally screened. 3 models based on T2WI and 3 models based on DCE-MRI AP were established, respectively. The area under curve (AUC) value of Nomogram based on T2WI of training set and validation set was 0.83 and 0.81, respectively. The AUC value of the models based on T2WI and models based on AP was almost equal, and Nomograms were the most effective models among all three types of models.

conclusionMRI-based Nomogram has greater predictive efficacy to predict the response after TACE than Radiomics and Clinics models alone, and the efficacy of T2WI-based models and DCE-MRI AP-based models was almost equal.

Indexed as

Carcinoma, HepatocellularChemoembolization, TherapeuticLiver NeoplasmsHumansMagnetic Resonance ImagingNomogramsRetrospective StudiesHCC progressionMRI-based nomogramPrediction abilityRadiomicsTACE

Identifiers

PMID33713159

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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.