Evidence map›Paper›PMID 39881024›Full record

ArticleInsights into imaging2025

Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features.

Ye Yu, Tianshu Yang, Pengfei Ma, Yan Zeng, Yongming Dai, Yicheng Fu, Aie Liu, Ying Zhang, Guanglei Zhuang, Yan Zhou and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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.

  1. Trial
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  6. Review
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

11 authors.

Ye Yu *Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Tianshu Yang *Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Pengfei MaState Key Laboratory of Oncogenes and Related Genes, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yan ZengDepartment of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Yongming DaiSchool of Biomedical Engineering, Shanghai Tech University, Shanghai, China.
Yicheng FuDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Aie LiuDepartment of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Ying ZhangDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Guanglei ZhuangShanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhuangguanglei@gmail.com.
Yan ZhouDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. clare1475@hotmail.com.
Huawei WuDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. huaweiwu26@163.com.ORCID http://orcid.org/0000-0003-4846-6451

Funding

This study has received funding from Horizontal project of Renji Hospital No.RJKY22-001, No.RJKY22-002
6 · The paper itself

Abstract

objectivesThe aim of this study was to determine the status of tertiary lymphoid structures (TLSs) using radiomic features in patients with invasive pulmonary adenocarcinoma (IA).

methodsIn this retrospective study, patients with IA from November 2015 to March 2024 were recruited from two independent centers (center 1, training and internal test data set; center 2, external test data set). TLS was divided into two groups according to hematoxylin-eosin staining. Radiomic features were extracted, and support vector machine (SVM) were implemented to predict the status of TLSs. Receiver operating characteristic (ROC) curves were used to analyze diagnostic performance. Furthermore, visual assessments of the test set were also conducted by two thoracic radiologists and compared with the radiomics results.

resultsA total of 456 patients were included (training data set, n = 278; internal test data set, n = 115; external test data set, n = 63). The area under the curve (AUC) of the radiomics model on the validation set, the internal test set, and the external test set were 0.781 (95% confidence interval (CI): 0.659-0.905;), 0.804 (95% CI: 0.723-0.884;) and 0.747 (95% CI: 0.621-0.874;), respectively. In the visual assessments, the mean CT value and air bronchogram were important indicators of TLS, the AUC was 0.683. In the external test set, the AUC of the clinical model was 0.632.

conclusionsThe radiomics model has a higher AUC than the clinical model and effectively discriminates TLSs in patients with IA. CRITICAL RELEVANCE STATEMENT: This study demonstrates that the radiomics-based model can differentiate TLSs in patients with IA. As a non-invasive biomarker, it enhances our understanding of tumor prognosis and management. KEY POINTS: TLSs are closely related to favorable clinical outcomes in non-small cell lung cancer. Radiomics from Chest CT predicted TLSs in patients with IA. This study supports individualized clinical decision-making for patients with IA.

Indexed as

Adenocarcinoma of lungRadiomicsTertiary lymphoid structures

Identifiers

PMID39881024
PMCPMC11780022

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