Objectives To investigate the prognostic value of artificial intelligence (AI) quantified emphysema and interstitial lung abnormality (ILA) in patients with non-small cell lung cancer (NSCLC). Materials and methods This retrospective study used AI to quantify emphysema and ILA in patients diagnosed with NSCLC between January 2015 and December 2017. Associations between AI-quantified emphysema and ILA severity and overall survival (OS) were evaluated using Cox proportional hazards models. The ability of AI-quantified emphysema and ILA severity to predict OS was explored via concordance index (C-index) and area under the time-dependent receiver operating characteristic curve (AUC). Furthermore, exploratory OS analyses were performed on subgroups stratified by chronic obstructive pulmonary disease status, treatment type, and tumor-node-metastasis (TNM) staging. Results Of 1675 patients, 830 (49.6%) survived, and 845 (50.4%) died. Whole emphysema (mild: HR, 1.66 [95% CI: 1.26, 2.18]; p < 0.001; more than mild: HR, 2.55 [95% CI: 1.88, 3.48]; p < 0.001) and ILA (equivocal ILA: HR, 1.63 [95% CI: 1.15, 2.32]; p = 0.006; definite ILA: HR, 2.33 [95% CI: 1.61, 3.35]; p < 0.001) severity were independent prognostic factors for NSCLC, while regional emphysema and regional ILA severity were not. The model combining AI-quantified whole emphysema severity and ILA severity outperformed the TNM staging-only model in predicting NSCLC patient outcome (C-index, 0.80 vs. 0.75; AUC, 0.90 vs. 0.85). Conclusions Increased AI-quantified whole emphysema and ILA severity were associated with worse OS in NSCLC. The model combining AI-quantified emphysema and ILA showed improved performance for predicting patient survival versus TNM staging alone. Critical relevance statement AI-quantified emphysema and ILA severity are associated with NSCLC patient outcome and can provide information complementary to TNM staging for predicting NSCLC patient survival and promoting the development of individualized management strategies. Key Points <UnorderedList Mark="Bullet"> <ItemContent> The study explores artificial intelligence (AI) quantified emphysema and interstitial lung abnormality (ILA) severity in non-small cell lung cancer (NSCLC) prognosis. </ItemContent> <ItemContent> The AI-quantified whole emphysema severity and ILA severity were independent prognostic factors for NSCLC patient outcome, while regional emphysema and regional ILA severity were not. </ItemContent> <ItemContent> AI-quantified emphysema and ILA severity may help predict the survival of NSCLC patients and help clinicians make informed treatment decisions. </ItemContent> </UnorderedList> Graphical Abstract

Association of automated quantified emphysema and interstitial lung abnormality with survival in non-small cell lung cancer

期刊论文
出版模式：

开放获取
发表日期：

2026年1月5日

查看全文

Guangjing Weng,
Junli Tao,
Yu Pu,
Changyu Liang,
Bohui Chen,
Zhenyu Wang,
Chengzhan Qi,
Jiuquan Zhang

摘要

Objectives

To investigate the prognostic value of artificial intelligence (AI) quantified emphysema and interstitial lung abnormality (ILA) in patients with non-small cell lung cancer (NSCLC).

Materials and methods

This retrospective study used AI to quantify emphysema and ILA in patients diagnosed with NSCLC between January 2015 and December 2017. Associations between AI-quantified emphysema and ILA severity and overall survival (OS) were evaluated using Cox proportional hazards models. The ability of AI-quantified emphysema and ILA severity to predict OS was explored via concordance index (C-index) and area under the time-dependent receiver operating characteristic curve (AUC). Furthermore, exploratory OS analyses were performed on subgroups stratified by chronic obstructive pulmonary disease status, treatment type, and tumor-node-metastasis (TNM) staging.

Results

Of 1675 patients, 830 (49.6%) survived, and 845 (50.4%) died. Whole emphysema (mild: HR, 1.66 [95% CI: 1.26, 2.18]; p < 0.001; more than mild: HR, 2.55 [95% CI: 1.88, 3.48]; p < 0.001) and ILA (equivocal ILA: HR, 1.63 [95% CI: 1.15, 2.32]; p = 0.006; definite ILA: HR, 2.33 [95% CI: 1.61, 3.35]; p < 0.001) severity were independent prognostic factors for NSCLC, while regional emphysema and regional ILA severity were not. The model combining AI-quantified whole emphysema severity and ILA severity outperformed the TNM staging-only model in predicting NSCLC patient outcome (C-index, 0.80 vs. 0.75; AUC, 0.90 vs. 0.85).

Conclusions

Increased AI-quantified whole emphysema and ILA severity were associated with worse OS in NSCLC. The model combining AI-quantified emphysema and ILA showed improved performance for predicting patient survival versus TNM staging alone.

Critical relevance statement

AI-quantified emphysema and ILA severity are associated with NSCLC patient outcome and can provide information complementary to TNM staging for predicting NSCLC patient survival and promoting the development of individualized management strategies.

Key Points

The study explores artificial intelligence (AI) quantified emphysema and interstitial lung abnormality (ILA) severity in non-small cell lung cancer (NSCLC) prognosis.

The AI-quantified whole emphysema severity and ILA severity were independent prognostic factors for NSCLC patient outcome, while regional emphysema and regional ILA severity were not.

AI-quantified emphysema and ILA severity may help predict the survival of NSCLC patients and help clinicians make informed treatment decisions.

Graphical Abstract