Evidence map›Paper›PMID 36854872›Full record

ArticleInsights into imaging2023

Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI.

Mingzhen Chen, Chunli Kong, Enqi Qiao, Yaning Chen, Weiyue Chen, Xiaole Jiang, Shiji Fang, Dengke Zhang, Minjiang Chen, Weiqian Chen and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
5.5field-weighted citation impact, top 4% of its field
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
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  11. 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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  16. [Research progress of radiomics in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024
    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

11 authors at 4 institutions in 1 country.

Mingzhen Chen *Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Chunli Kong *Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Enqi QiaoKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Yaning ChenKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Weiyue ChenKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Xiaole JiangKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Shiji FangKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Dengke ZhangKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Minjiang ChenKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China.
Weiqian ChenKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. ls2119088@126.com.
Jiansong JiKey Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. jijiansong@zju.edu.cn.
Lishui University · CNWenzhou Medical University · CNLishui Central Hospital · CNZhejiang University · CN

Funding

National Key Research and Development projects intergovernmental cooperation in science and technology of China 2018YFE0126900National Natural Science Foundation of China 82072026National Natural Science Foundation of China 82102162Natural Science Foundation of Zhejiang Province LGF21H180002Zhejiang Provincial Key Laboratory of Wood Science and Technology 2022RC087
6 · The paper itself

Abstract

objectivesThis study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance.

methodsThe subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN).

resultsDNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735).

conclusionsDNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.

Indexed as

Deep learningHepatocellular carcinomaRadiomicsTransarterial chemoembolization

Identifiers

PMID36854872
PMCPMC9975141
OpenAlexW4322625284

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.