Evidence map›Paper›PMID 31088553›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019

Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction.

Zhen Zhang, Hanyu Jiang, Jie Chen, Yi Wei, Likun Cao, Zheng Ye, Xin Li, Ling Ma, Bin Song

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PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 4 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

78 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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18 more citing papers are in PubMed but not listed here.

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

9 authors.

Zhen ZhangDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
Hanyu JiangDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
Jie ChenDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
Yi WeiDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
Likun CaoDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
Zheng YeDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China.
Xin LiGE Healthcare China, Beijing, China.
Ling MaGE Healthcare China, Beijing, China.
Bin SongDepartment of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, China. songb_radiology@163.com.

Funding

National Natural Science Foundation of China NFSC 81471658Science and Technology Support Program of Sichuan Province 2017SZ0003
6 · The paper itself

Abstract

backgroundThis study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images.

methodsIn total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves.

resultsIn total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis.

conclusionsIncorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery.

Indexed as

Magnetic Resonance ImagingNomogramsAdultAgedCarcinoma, HepatocellularContrast MediaFemaleGadolinium DTPAHumansLiver NeoplasmsMaleMiddle AgedContrast MediaGadolinium DTPAgadolinium ethoxybenzyl DTPAGadoxetic acid-enhanced MRIHepatocellular carcinomaNomogramRadiomicsRecurrence

Identifiers

PMID31088553
PMCPMC6518803

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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.