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Peer-reviewedCC BY 4.0The Clinical Respiratory Journal

Combined Pulmonary Fibrosis and Emphysema: Current Status and Future Perspective

  • Qinyu Deng1
  • Chen Chen1
  • Qinghe Zhang1
  • Zilin Wei2
  • Jianqi Sun2
  • Feng Li3
  1. 1Department of Basic Medical Sciences, School of Medicine Shanghai Jiao Tong University Shanghai People's Republic of China
  2. 2School of Biomedical Engineering Shanghai Jiao Tong University Shanghai People's Republic of China
  3. 3Department of Pulmonary and Critical Care Medicine, Shanghai Chest Hospital, School of Medicine Shanghai Jiao Tong University Shanghai People's Republic of China
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Abstract

Background: Combined pulmonary fibrosis and emphysema (CPFE) is a clinical syndrome defined by the coexistence of upper‐lobe emphysema and lower‐lobe pulmonary fibrosis. It is characterized by clinical, functional, and radiologic features that differ significantly from isolated idiopathic pulmonary fibrosis or chronic obstructive pulmonary disease.

Summary: This review summarizes recent advances in CPFE, focusing on etiologic classification, clinical and radiologic features, diagnosis and treatment, prognostic evaluation, and emerging perspectives in artificial intelligence (AI). Based on current research, we summarize the etiologic classification of CPFE into four types: smoking‐related, systemic autoimmune rheumatic disease‐associated, occupational‐ and environmental‐related, and idiopathic. These types differ in epidemiologic characteristics, imaging patterns, and disease progression. Consequently, future research is expected to focus on mechanism‐driven phenotyping and quantitative imaging analysis, where AI may provide additional value in disease identification, risk stratification, and dynamic prognostic assessment.

Key Message: This review proposes a novel framework for CPFE, integrating etiologic subtyping and a diagnostic algorithm to guide precise management, and highlights AI‐powered strategies for future personalized interventions.

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Cite

BibTeXRIS
BibTeX
@article{deng2026combined,
  title = {{Combined Pulmonary Fibrosis and Emphysema: Current Status and Future Perspective}},
  author = {Qinyu Deng and Chen Chen and Qinghe Zhang and Zilin Wei and Jianqi Sun and Feng Li},
  journal = {The Clinical Respiratory Journal},
  year = {2026},
  month = nov,
  doi = {10.1111/crj.70236},
  url = {https://doi.org/10.1111/crj.70236},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Combined Pulmonary Fibrosis and Emphysema: Current Status and Future Perspective
AU  - Qinyu Deng
AU  - Chen Chen
AU  - Qinghe Zhang
AU  - Zilin Wei
AU  - Jianqi Sun
AU  - Feng Li
PY  - 2026
DA  - 2026/11/03
JO  - The Clinical Respiratory Journal
DO  - 10.1111/crj.70236
UR  - https://doi.org/10.1111/crj.70236
AB  - Background: Combined pulmonary fibrosis and emphysema (CPFE) is a clinical syndrome defined by the coexistence of upper‐lobe emphysema and lower‐lobe pulmonary fibrosis. It is characterized by clinical, functional, and radiologic features that differ significantly from isolated idiopathic pulmonary fibrosis or chronic obstructive pulmonary disease.  Summary: This review summarizes recent advances in CPFE, focusing on etiologic classification, clinical and radiologic features, diagnosis and treatment, prognostic evaluation, and emerging perspectives in artificial intelligence (AI). Based on current research, we summarize the etiologic classification of CPFE into four types: smoking‐related, systemic autoimmune rheumatic disease‐associated, occupational‐ and environmental‐related, and idiopathic. These types differ in epidemiologic characteristics, imaging patterns, and disease progression. Consequently, future research is expected to focus on mechanism‐driven phenotyping and quantitative imaging analysis, where AI may provide additional value in disease identification, risk stratification, and dynamic prognostic assessment.  Key Message: This review proposes a novel framework for CPFE, integrating etiologic subtyping and a diagnostic algorithm to guide precise management, and highlights AI‐powered strategies for future personalized interventions.
SN  - 1752-6981
SN  - 1752-699X
ER  -