Evidence map›Paper›PMID 38536542›Full record

ArticleInsights into imaging2024

Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma.

Hai-Feng Liu, Min Wang, Qing Wang, Yang Lu, Yu-Jie Lu, Ye Sheng, Fei Xing, Ji-Lei Zhang, Sheng-Nan Yu, Wei Xing

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

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31citing papers in PubMed
–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

31 citing papers in PubMed.

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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.

Hai-Feng LiuDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China.
Min WangDepartment of Anesthesiology, The Second People's Hospital of Changzhou, Affiliated Hospital of Nanjing Medical University, Changzhou, China.
Qing WangDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China.
Yang LuDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China.
Yu-Jie LuDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China.
Ye ShengDepartment of Interventional Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China.
Fei XingDepartment of Radiology, Nantong Third People's Hospital, Nantong, Jiangsu, China.
Ji-Lei ZhangBayer Healthcare, Shanghai, China.
Sheng-Nan YuDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China. xzyysn8@126.com.
Wei XingDepartment of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, China. suzhxingwei@suda.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo explore the predictive potential of intratumoral and multiregion peritumoral radiomics features extracted from multiparametric MRI for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients.

methodsA total of 265 patients with 277 HCCs (training cohort n = 193, validation cohort n = 84) who underwent preoperative MRI were retrospectively analyzed. The risk factors identified through stepwise regression analysis were utilized to construct a clinical model. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (entire tumor, Peri_5mm, Peri_10mm, Peri_20mm) were developed using the LASSO approach. The features obtained from the intratumoral region and the optimal peritumoral region were combined to design the IntraPeri fusion model. Model performance was assessed using the area under the curve (AUC).

resultsLarger size, non-smooth margins, and mosaic architecture were risk factors for poorly differentiated HCC (pHCC). The clinical model achieved AUCs of 0.77 and 0.73 in the training and validation cohorts, respectively, while the intratumoral model achieved corresponding AUC values of 0.92 and 0.82. The Peri_10mm model demonstrated superior performance to the Peri_5mm and Peri_20mm models, with AUC values of 0.87 vs. 0.84 vs. 0.73 in the training cohort and 0.80 vs. 0.77 vs. 0.68 in the validation cohort, respectively. The IntraPeri model exhibited remarkable AUC values of 0.95 and 0.86 in predicting pHCC in the training and validation cohorts, respectively.

conclusionsOur study highlights the potential of a multiparametric MRI-based radiomic model that integrates intratumoral and peritumoral features as a tool for predicting HCC differentiation. CRITICAL RELEVANCE STATEMENT: Both clinical and multiparametric MRI-based radiomic models, particularly the intratumoral radiomic model, are non-invasive tools for predicting HCC differentiation. Importantly, the IntraPeri fusion model exhibited remarkable predictiveness for individualized HCC differentiation. KEY POINTS: • Both the intratumoral radiomics model and clinical features were useful for predicting HCC differentiation. • The Peri_10mm radiomics model demonstrated better diagnostic ability than other peritumoral region-based models. • The IntraPeri radiomics fusion model outperformed the other models for predicting HCC differentiation.

Indexed as

Carcinoma (hepatocellular)DifferentiationMultiparametric MRIRadiomics

Identifiers

PMID38536542
PMCPMC10973314

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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.