Evidence map›Paper›PMID 41022600›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University

[Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer].

Qizhi Huang, Daipeng Xie, Lintong Yao, Qiaxuan Li, Shaowei Wu, Haiyu Zhou

Abstract readEnglish Abstract
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qizhi HuangGuangdong Provincial Institute of Cardiovascular Diseases, Guangzhou 510080, China.
Daipeng XieDepartment of Biochemistry, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China.
Lintong YaoGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China.
Qiaxuan LiDepartment of Lung Transplantation, Second Affiliated Hospital of Zhejiang University, Hangzhou 310000, China.
Shaowei WuGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China.
Haiyu ZhouGuangdong Provincial Institute of Cardiovascular Diseases, Guangzhou 510080, China.

Funding

National Natural Science Foundation of China 82472064
6 · The paper itself

Abstract

objectivesTo construct a nomogram for predicting the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (aNSCLC) by integrating chest CT radiomics signature that reflects the tumor microenvironment (TME) and clinical parameters of the patients.

methodsTranscriptomic and CT imaging data from TCGA, GEO and TCIA databases were integrated for weighted gene co-expression network analysis (WGCNA) of the GEO cohort to identify the immunotherapy-related genes (IRGs) associated with ICIs response. A prognostic model was built using these IRGs in the TCGA cohort to assess immune microenvironment features across different risk groups. Radiomics features were extracted from TCIA lung_3 cohort using

resultsWGCNA identified 84 IRGs enriched in immune activation pathways. The combined model outperformed individual models in both the training (AUC=0.725, 95%

conclusionsThe genomic-radiomic-clinical multidimensional predictive framework established in this study provides an interpretable biomarker combination and clinical decision-making tool for evaluating ICIs efficacy in aNSCLC, potentially facilitating personalized immunotherapy decision-making.

Indexed as

Carcinoma, Non-Small-Cell LungImmunotherapyLung NeoplasmsTomography, X-Ray ComputedTumor MicroenvironmentFemaleHumansImmune Checkpoint InhibitorsMaleNomogramsPrognosisRadiomicsRetrospective StudiesImmune Checkpoint Inhibitorsimmune checkpoint inhibitorsmachine learningnon-small cell lung cancerradiomicstumor microenvironment

Identifiers

PMID41022600
PMCPMC12479264

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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.