Evidence map›Paper›PMID 42322484›Full record

ArticleInsights into imaging2026

Lobar-level radiomic clustering reveals background lung changes associated with lung cancer risk: a new perspective for early screening.

Yihuan Wang, Chen Zhu, Zhenzhen Lu, Xianglan Zhou, Chengting Lin, Yuwei Li, Liting Shi, Lili Wu, Hongxia Ma, Meng Zhu and 8 more

Abstract read
In one paragraph

Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Yihuan Wang *Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Chen Zhu *Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Zhenzhen LuDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Xianglan ZhouDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Chengting LinDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Yuwei LiDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Liting ShiDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Lili WuDepartment of Radiology Imaging, Taizhou Cancer Hospital, Taizhou, China.
Hongxia MaDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Meng ZhuDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Jia ChenFaculty of Health Sciences, University of Macau, Macao, China.
Junwei LvShanghai Juillet AI Lab, Shanghai, China.
Lingying ZhuDepartment of Radiology Imaging, Taizhou Cancer Hospital, Taizhou, China.
Lingbin DuDepartment of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Chen JiDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Honglun RenBEIJING DEEPWISE&LEAGUE OF PHD TECHNOLOGY CO.LTD, Beijing, China.
Enyu WangDepartment of Radiology Imaging, Taizhou Cancer Hospital, Taizhou, China. fishman811@sohu.com.
Lei ShiDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. shilei@zjcc.org.cn.ORCID http://orcid.org/0000-0003-0031-2808

Funding

The Zhejiang Provincial Medical and Health Science and Technology Plan WKJ-ZJ-2330Wenling Science and Technology Bureau 2021S00080
6 · The paper itself

Abstract

backgroundThe concept of field cancerization highlights spatially diffuse, pre-malignant changes in carcinogen-exposed lung tissue, yet current screening rarely captures such effects regionally. This exploratory study aims to quantify field cancerization via lobar-level radiomic clustering and assess its association with lung cancer risk in high-risk smokers. MATERIALS AND

methodsA total of 10,280 male current or former smokers (mean age, 62.1 ± 6.34 years) were enrolled from a high-risk population undergoing lung cancer screening. Unsupervised clustering of CT-derived radiomic features was performed for each lobe. A logistic regression-derived weighted spatial risk score was developed to quantify cumulative lobar risk. Cancer incidence associations were assessed after adjusting for polygenic risk and epidemiological factors, with stratified analyses by genetic risk and smoking duration.

resultsDistinct radiomic clusters were observed across all lobes, with the right upper lobe demonstrating a significantly higher lung cancer incidence in the high-risk cluster (0.997% vs 0.593%, p = 0.020). The weighted spatial risk score was independently associated with cancer risk (OR = 1.09, 95% CI: 1.02-1.16, p = 0.010). There was no significant interaction between the score and PRS (p = 0.938), suggesting a genetic background-independent effect. In stratified analysis, the score was significantly associated with lung cancer among long-term smokers (OR = 1.08, 95% CI: 1.00-1.17, p = 0.049), with a similar but nonsignificant trend in short-term smokers.

conclusionUnsupervised clustering of lobar radiomics reveals background pulmonary alterations, supporting field cancerization and the "seed-and-soil" hypothesis, and offers an exploratory imaging-based framework for cancer risk stratification in screening. CRITICAL RELEVANCE STATEMENT: Unsupervised clustering of radiomic features at the pulmonary lobe level identified distinct background patterns associated with nodule and cancer incidence. A derived weighted lobe score was independently associated with lung cancer risk, especially among individuals with prolonged smoking histories. KEY POINTS: Lobar radiomic clustering identifies background lung changes related to cancer risk. Weighted lobe spatial risk score predicts lung cancer risk independently of genetic background. Longer smoking duration strengthens the link between lung damage and cancer risk.

Indexed as

Field cancerizationLung cancerRadiomicsRisk stratificationUnsupervised clustering

Identifiers

PMID42322484
PMCPMC13283237

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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.