Evidence map›Paper›PMID 35444942›Full record

ArticleFrontiers in oncology2022

A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature.

Tingting Fan, Shijie Li, Kai Li, Jingxu Xu, Sheng Zhao, Jinping Li, Xinglu Zhou, Huijie Jiang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

12 citing papers in PubMed, 19 citations in OpenAlex.

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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

8 authors at 4 institutions in 1 country.

Tingting FanDepartment of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Shijie LiDepartment of Interventional Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
Kai LiDepartment of Interventional Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
Jingxu XuDepartment of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Doctor of Philosophy (PHD) Technology Co., Ltd, Beijing, China.
Sheng ZhaoDepartment of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Jinping LiDepartment of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Xinglu ZhouDepartment of Positron Emission Tomography/Computed Tomography (PET/CT) Center, Harbin Medical University Cancer Hospital, Harbin, China.
Huijie JiangDepartment of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Third Affiliated Hospital of Harbin Medical University · CNHarbin Medical University · CNSecond Affiliated Hospital of Harbin Medical University · CNCenter for High Pressure Science & Technology Advanced Research · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation. Methods: 202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant's MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC). Results: The entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors α-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets. Conclusions: The combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC.

Indexed as

diagnosishepatocellular carcinomamagnetic resonance imagingradiomicsVEGF

Identifiers

PMID35444942
PMCPMC9013965
OpenAlexW4226019026

What OpenQuestion holds

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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.