Evidence map›Paper›PMID 37875815›Full record

ArticleBMC cancer2023

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma.

Ying Zhao, Jian Zhang, Nan Wang, Qihao Xu, Yuhui Liu, Jinghong Liu, Qinhe Zhang, Xinyuan Zhang, Anliang Chen, Lihua Chen and 5 more

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 2% 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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  14. 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

15 authors at 3 institutions in 1 country.

Ying Zhao *Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Jian Zhang *Department of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Nan WangDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Qihao XuDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Yuhui LiuCollege of Medical Imaging, Dalian Medical University, Dalian, China.
Jinghong LiuDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Qinhe ZhangDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Xinyuan ZhangCollege of Medical Imaging, Dalian Medical University, Dalian, China.
Anliang ChenDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Lihua ChenDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Liuji ShengCollege of Medical Imaging, Dalian Medical University, Dalian, China.
Qingwei SongDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
Feng WangDepartment of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Yan GuoGE Healthcare (China), Shanghai, China.
Ailian LiuDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China. liuailian@dmu.edu.cn.
Dalian Medical University · CNFirst Affiliated Hospital of Dalian Medical University · CNUnited Imaging Healthcare (China) · CN

Funding

National Natural Science Foundation of China 61971091
6 · The paper itself

Abstract

backgroundNoninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.

methodsA total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman's rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.

resultsThe T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821-0.936) and 0.911 (95%CI, 0.825-0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854-0.958) and 0.918 (95%CI, 0.831-0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709-0.863) and 0.782 (95%CI, 0.660-0.902)] in the both cohorts and achieved good calibration capability and clinical utility.

conclusionsCE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Indexed as

Carcinoma, HepatocellularChemoembolization, TherapeuticLiver NeoplasmsHumansMagnetic Resonance ImagingRetrospective StudiesHepatocellular carcinomaMagnetic resonance imagingRadiomicsTransarterial chemoembolizationTreatment response

Identifiers

PMID37875815
PMCPMC10594790
OpenAlexW4387906832

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.