Evidence map›Paper›PMID 40235745›Full record

ArticleQuantitative imaging in medicine and surgery2025

Integrated radiomics and immune infiltration analysis to decipher immunotherapy efficacy in lung adenocarcinoma.

Yiyi Lei, Wenjin Fan, Beizhan Liu, Yuxuan Liao, Chenxi Liu, Shengjie Xue, Dawei Zhou, Hongyi Wang, Qiang Zhang

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Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yiyi Lei *Department of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Wenjin Fan *Department of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Beizhan Liu *Department of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Yuxuan LiaoXiangya School of Medicine, Central South University, Changsha, China.
Chenxi LiuXiangya School of Medicine, Central South University, Changsha, China.
Shengjie XueDepartment of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Dawei ZhouDepartment of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Hongyi WangDepartment of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Qiang ZhangDepartment of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Research in recent years has witnessed unprecedented improvements in immunotherapy, especially immune checkpoint blockade (ICB) for the treatment of lung adenocarcinoma (LUAD) patients. Nevertheless, due to the heterogeneity of immunotherapy response, reliable biomarkers are urgently needed to guide precision cancer therapy. In this study, we aimed to identify immune subtypes in LUAD and develop a radiogenomic model to improve immunotherapy predictive accuracy. Methods: In this study, clinical data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) databases, and immune subtypes were identified using the ConsensusClusterPlus package in R. Biological, genomic, and epigenomic distinctions were compared. The TCGA cohort and clinical cohort from the Third Xiangya Hospital were utilized to demonstrate no significant differences of survival probability between sexes. Feature extraction and definition were conducted from 103 computed tomography (CT) images from The Cancer Imaging Archive (TCIA) dataset via the "PyRadiomics" embedded in Python. A series of machine learning techniques were applied to build a radiogenomic model. Results: Two LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in biological, genomic, and epigenomic distinctions among the two subtypes were observed (P<0.05). The immune subtype A participated in pathways related to immune activation and displayed a higher tumor microenvironment (TME) score (P<0.001) with a better prognosis of LUAD [overall survival (OS), P=0.037; disease-specific survival (DSS), P=0.034]. Besides, the model appears to show better fit for females (P=0.015) than for males (P=0.641). Our constructed radiogenomic model incorporating 12 radiomics features displayed satisfactory potential to facilitate the predictive accuracy of immunotherapy in LUAD [test area under the curve (AUC) =0.89; train AUC =0.95]. Conclusions: Our study presented a promising avenue to harness the rich radiomics data to identify the specific immune subtype and integrate it into the existing clinical decision-making system to facilitate the predictive accuracy of immunotherapy in LUAD.

Indexed as

immune subtypeimmunotherapyLung adenocarcinoma (LUAD)radiomicstumor microenvironment (TME)

Identifiers

PMID40235745
PMCPMC11994542

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