Evidence map›Paper›PMID 39085788›Full record

ArticleBMC medical imaging2024

CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.

Ting Xu, Xiaowen Liu, Yaxi Chen, Shuxing Wang, Changsi Jiang, Jingshan Gong

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In one paragraph

Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 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

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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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

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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.

Ting XuThe Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
Xiaowen LiuThe Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
Yaxi ChenThe Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
Shuxing WangThe Second Clinical Medical College of Jinan University, Shenzhen, 518020, China.
Changsi JiangDepartment of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), 1F, Building 4, No. 1017 Dongmen North Road, Shenzhen, 518020, China.
Jingshan GongDepartment of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), 1F, Building 4, No. 1017 Dongmen North Road, Shenzhen, 518020, China. jshgong@sina.com.

Funding

National Natural Science Foundation of China 82172026
6 · The paper itself

Abstract

backgroundProgrammed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expression in non-small cell lung cancers(NSCLCs).

methods259 consecutive patients with pathological confirmed NSCLCs were retrospectively collected and divided into the training cohort and validation cohort according to the chronological order. The univariate and multivariate analyses were used to build the clinical model. Radiomics and deep learning features were extracted from preoperative non-contrast CT images. After feature selection, Radiomics score (Rad-score) and deep learning radiomics score (DLR-score) were calculated through a linear combination of the selected features and their coefficients. Predictive performance for PD-L1 expression was evaluated via the area under the curve (AUC) of receiver operating characteristic, the calibration curves, and the decision curve analysis.

resultsThe clinical model based on Cytokeratin 19 fragment and lobulated shape obtained an AUC of 0.767(95% CI: 0.673-0.860) in the training cohort and 0.604 (95% CI:0.477-0.731) in the validation cohort. 11 radiomics features and 15 deep learning features were selected by LASSO regression. AUCs of the Rad-score were 0.849 (95%CI: 0.783-0.914) and 0.717 (95%CI: 0.607-0.826) in the training cohort and validation cohort, respectively. AUCs of DLR-score were 0.938 (95%CI: 0.899-0.977) and 0.818(95%CI:0.727-0.910) in the training cohort and validation cohort, respectively. AUCs of the DLR-score were significantly higher than those of the Rad-score and the clinical model.

conclusionThe CT-based deep learning radiomics signature could achieve clinically acceptable predictive performance for PD-L1 expression, which showed potential to be a surrogate imaging biomarker or a complement of immunohistochemistry assessment.

Indexed as

B7-H1 AntigenBiomarkers, TumorCarcinoma, Non-Small-Cell LungDeep LearningLung NeoplasmsTomography, X-Ray ComputedAgedArea Under CurveFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesROC CurveB7-H1 AntigenBiomarkers, TumorCD274 protein, humanDeep learningImmune checkpoint inhibitorsNon-small cell lung cancerProgrammed cell death ligand 1Radiomics

Identifiers

PMID39085788
PMCPMC11292915

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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.