Evidence map›Paper›PMID 36039307›Full record

ArticleInternational journal of general medicine2022

Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images.

Linhua Wu, Jian Li, Xiaowei Ruan, Jialiang Ren, Xuejun Ping, Bing Chen

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

9 citing papers in PubMed, 10 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

6 authors at 3 institutions in 1 country.

Linhua WuDepartment of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China.
Jian LiDepartment of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China.
Xiaowei RuanDepartment of Radiology, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, People's Republic of China.
Jialiang RenDepartment of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, People's Republic of China.
Xuejun PingDepartment of Clinical Medical Faculty, Medical University of Ningxia, Yinchuan, Ningxia Hui Autonomous Region, People's Republic of China.
Bing ChenDepartment of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China.
Ningxia Medical University · CNNingxia Medical University General Hospital · CNThe Fourth People's Hospital of Ningxia Hui Autonomous Region · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Energy spectrum CT is an effective method to evaluate the biological behavior of lung cancer. Radiomics is a non-invasive technology to obtain histological information related to lung cancer. Purpose: To investigate the value of the radiomics models on the bases of enhanced spectral CT images of peripheral lung cancer to predict the expression of the vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR). Material and Methods: This study retrospectively analyzed 73 patients with peripheral lung cancer confirmed by postoperative pathology. All patients underwent dual-phase enhanced spectral CT scans before surgery. Regions of interest (ROI) were delineated in the arterial phase and venous phase. Key radiomics features were extracted and models were established to predict the expression of VEGF and EGFR, respectively. All models were established based on the expression levels of VEGF and EGFR in tissues detected by immunohistochemical staining as reference standards. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of each model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the models. Results: In predicting the expression level of VEGF, the combined (COMB) model composed of one spectral feature and two radiomics features achieved the best performance with area under ROC (AUC) 0.867 (95% CI: 0.767-0.966), accuracy of 0.812, sensitivity of 0.879, and specificity of 0.667. According to the expression level of EGFR, three importance radiomics features were retained in the arterial and venous phases to establish the multiphase phase model which has the best performance with AUC of 0.950 (95% confidence interval: 0.89-1.00), accuracy of 0.896, sensitivity of 0.868, and specificity of 1. Conclusion: The radiomics model of enhanced spectral CT images of peripheral lung cancer can predict the expression of EGFR and VEGF.

Indexed as

EGFRenergy spectrum CTperipheral lung cancerradiomicsVEGF

Identifiers

PMID36039307
PMCPMC9419990
OpenAlexW4292673849

What OpenQuestion holds

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LicenceCC BY-NC
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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.