The identification and severity staging of chronic obstructive pulmonary disease using quantitative CT parameters, radiomics features, and deep learning features

Q: What type of study is this?

This is a Cohort Study study (also classified as: Quantitative Study).

Shengchuan Feng; Wenxiu Zhang; Ran Zhang; Yuqiong Yang; Fengyan Wang; Chengyu Miao; Zizheng Chen; Kai Yang; Qing Yao; Qing Xuan Liang; Huijun Zhao; Yuting Chen; Cuixia Liang; Xiaoyun Liang; Rongchang Chen; Zhenyu Liang

doi:10.1159/000548595

September 25, 2025Respiration

The identification and severity staging of chronic obstructive pulmonary disease using quantitative CT parameters, radiomics features, and deep learning features

View at Publisher

Ask AI

Bookmark

Share

Key Points

Model 5-B1 achieved an AUC of 0.920 for identifying COPD in the testing cohort, indicating high predictive performance.
The combination of quantitative CT and deep learning features provides an effective method for COPD identification and staging.
The integration of multiple data features from CT scans enhances the precision of COPD diagnosis and severity assessment.
External validation using the Shenzhen cohort confirms the generalizability of the predictive models across different populations.

No abstract yet

We only have this paper's title and publication details.

Read it at the source

Implication

Retrospective analysis shows predictive models for COPD severity using quantitative CT and deep learning features, suggesting improved diagnostic accuracy.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Feng et al. (2025) studied this question.

synapsesocial.com/papers/68d7b3ddeebfec0fc523672c https://doi.org/10.1159/000548595

View at Publisher

Ask AI

Bookmark

Share

The identification and severity staging of chronic obstructive pulmonary disease using quantitative CT parameters, radiomics features, and deep learning features

Key Points

No abstract yet

Implication

Discussion

Cite This Study

The identification and severity staging of chronic obstructive pulmonary disease using quantitative CT parameters, radiomics features, and deep learning features

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Keep reading

Keep reading