All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Thank you for your thorough revision and for addressing all reviewer comments so carefully. The reviewer has now recommended acceptance, and after editorial assessment, I am satisfied that the concerns raised in the previous round have been fully resolved.
Your clarifications on the study period, inclusion/exclusion criteria, ethics, and nomogram construction have strengthened the manuscript, and the overall presentation is now clear, coherent, and methodologically sound.
I am pleased to confirm that your manuscript is accepted in its current form.
Congratulations, and thank you for your careful work.
Title:
The title of the manuscript is clear, detailed and capture the content and focus of the manuscript which is to evaluate the “Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation”
Abstract:
The abstract of the study is structured. The Abstract adequately captures the content and focus of the manuscript. The authors provided adequate background information highlighting that Traditional biphasic CT scans are limited by radiation exposure, while single-inspiratory CT-based deep learning lacks interpretability especially in the diagnosis of preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD). Also, the study has very clear study objective which is “to develop a single-inspiratory quantitative CT (QCT) nomogram integrating parenchymal, airway, and vascular parameters to redefine imaging definition boundaries”
The Methods section is detailed, succinct and well-written. The authors provided a detailed description of the study participants i.e. cohort of 1,265 patients screened, yielding 658 eligible participants (Normal: 135, PRISm: 328, COPD: 195) and the study design. However, the section did not indicate the study period and setting as well as the inclusion and exclusion criteria of the study.
The Results are detailed and fairly well-written capturing the key objectives of the study – highlighting that the diagnostic models achieved AUCs up to 0.984 (PRISm vs. severe COPD) and 0.853 (PRISm vs. all COPD).
Also, the Conclusion section are detailed summarizing key findings and its implications for clinical care including that radiation-efficient approach enables early COPD stratification via interpretable structural-functional metrics.
In the revised manuscript, the authors have highlighted that the study received ethic approval in the Methods section as recommended. Also, the authors have clarified the study period and setting as well as the inclusion and exclusion criteria of the study in the Methods section of the Abstract as recommended. Overall, the authors have satisfactorily addressed all the issues raised in this section during the previous review of the manuscript.
Introduction
The authors provided a detailed review of the literature on the interests in the limitations of traditional biphasic CT scans such as radiation exposure and single-inspiratory CT-based deep learning lacks interpretability especially in the diagnosis of preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD). The authors did highlight the epidemiology of COPD and that the disease is highly complex and heterogeneous, involving multiple inflammatory mechanisms that collectively drive disease progression. However, previous studies indicate that COPD diagnosis based on spirometry post-bronchodilator, defining airflow obstruction as a ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) <0.70 has many limitations including detection after substantial structural lung damage and inability to differentiate COPD phenotypes including PRISm. Therefore, the authors did adequately justify the need for the present study as there are limited studies that have employed quantitative CT-based model for the early diagnosis of PRISm; and the study has well-written study objectives which is: “to develop a single-inspiratory quantitative CT (QCT) nomogram for the early diagnosis of PRISm”.In the revised manuscript, the authors have further highlighted the advantages of quantitative CT (QCT) nomogram for the early diagnosis of PRISm such as early COPD detection, differentiation of COPD from PRISm, risk stratification, personalized treatment and lung densitometry as recommended using the references suggested. Overall, the authors have satisfactorily addressed all the issues raised in this section during the previous review of the manuscript.
Figures & Tables
All the Tables and Figures are complete, self-explanatory and cited in the text
Material and Methods
The authors provide adequate description of the Methods including the study design and participants including all the necessary ethical approval. Also, the authors provided adequate description of the inclusions and exclusion criteria as well as study period. In addition, the authors provided adequate details of the CT and PFT measurements as well as CT image quality meeting the requirements for radiomics. Overall, the authors showed that Pulmonary Function and Imaging Data Acquisition were partly clear as PFTs were conducted using the GANSHORN PowerCube Diffusion+ system. Quantitative analysis of pulmonary CT images was performed using the Aview® system (Coreline Soft Inc., Seoul, South Korea). Also, lung parenchymal features including mean lung density (MLD), emphysema index (EI), and pixel indices PI-1/PI-15were determined through histogram thresholding. Therefore, the Methods described are fairly repeatable and adequate level of detail has been provided to enable replication of the analysis. Also, details of the statistical methods were fairly well-written. There are no recommended changes in this section of the manuscript.
Results
The paper makes a meaningful contribution to the advancement of the field by describing the interplay between PRISm and quantitative CT. Overall, the authors reported the Pulmonary Function and Whole-Lung Quantitative CT Parameters showing that there were significant gradient differences among the normal group 192 (n=135), PRISm group (n=328), and COPD group (n=195) regarding demographic characteristics, pulmonary function, and whole-lung CT parameters. Overall, the authors demonstrated Regional Specific Pulmonary Vascular Parameters including that quantitative chest CT parameters in 658 participants (Normal: 135, PRISm: 328 COPD: 195), revealing heterogeneous vascular patterns at different distances from the pleural surface. Also, the Spearman correlation analysis revealed a multi-level association between quantitative CT parameters and pulmonary function indices. In addition, the authors demonstrated Validation of Diagnostic Model Performance. The diagnostic models constructed using stepwise logistic regression across four groups which demonstrated a gradient of discriminative performance. The findings presented appears plausible and reasonable. All the Tables and Figures are complete, self-explanatory and cited in the text. Although, some of the Tables require additional clarifications.
In the revised manuscript, the authors have further clarified and provided details of how they created the CT nomogram and how they used it or it can be used following validation including any cut off values for each item of the nomogram as recommended. Overall, the authors have satisfactorily addressed all the issues raised in this section during the previous review of the manuscript. The authors need to clarify
Discussion
The Discussion is well written as important studies in this area were reviewed and cited in the field by describing the single-inspiratory CT-based deep learning lacks interpretability especially in the diagnosis of preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD). The authors did adequately discussed what is known in the literature and adequately compared their findings with other studies that suggest the importance of quantitative CT in diagnosing and stratifying PRISm, particularly concerning parenchymal and small airway alterations. Also, the authors discussed their findings highlighting that quantitative uses a single inspiratory-phase CT scan combined with a logistic regression model to efficiently differentiate between PRISm and individuals with normal spirometry or mild-to-moderate COPD (AUC = 0.836 for PRISm vs. normal spirometry; AUC = 0.815 for PRISm vs. mild-to-moderate COPD), while maintaining parameter interpretability, and discussed this finding in relation to other studies. In addition, the authors did provide a detailed paragraph highlighting the limitations of the study.
In the revised manuscript, the authors have strengthened the limitations of the study by highlighting that having both radiologists independently assess all cases would further strengthen measurement validity and enable comprehensive agreement analysis. Future multicenter studies should implement dual independent readings for all quantitative CT parameters to minimize measurement bias and establish robust quality control standards. Overall, the authors have satisfactorily addressed all the issues raised in this section during the previous review of the manuscript.
Conclusion
The Conclusion paragraph is fairly well-written does align with the key findings obtained in the study. In addition, the authors did provide clear implications from their findings to improve clinical practice especially that finding highlights the imaging boundaries between PRISm and COPD through single-inspiratory quantitative CT parameters, highlighting the critical role of small airway remodeling and vessel diameter at 9 mm from the pleural surface as early biomarkers. In addition, the study offers novel insights into the pathophysiological mechanisms of COPD progression and the potential for precision in pRISm detection and management. There are no recommended changes in this section of the manuscript.
None
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**PeerJ Staff Note:** PeerJ's policy is that any additional references suggested during peer review should only be included if the authors find them relevant and useful.
**Language Note:** When preparing your next revision, please ensure that your manuscript is reviewed either by a colleague who is proficient in English and familiar with the subject matter, or by a professional editing service. PeerJ offers language editing services; if you are interested, you may contact us at [email protected] for pricing details. Kindly include your manuscript number and title in your inquiry. – PeerJ Staff
Title & Abstract
Title:
The title of the manuscript is clear, detailed, and captures the content and focus of the manuscript, which is to evaluate the “Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation.”
Abstract:
The abstract of the study is structured. The Abstract adequately captures the content and focus of the manuscript. The authors provided adequate background information highlighting that Traditional biphasic CT scans are limited by radiation exposure, while single-inspiratory CT-based deep learning lacks interpretability, especially in the diagnosis of preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD). Also, the study has a very clear study objective, which is “to develop a single-inspiratory quantitative CT (QCT) nomogram integrating parenchymal, airway, and vascular parameters to redefine imaging definition boundaries.”
The Methods section is detailed, succinct, and well-written. The authors provided a detailed description of the study participants, i.e. cohort of 1,265 patients screened, yielding 658 eligible participants (Normal: 135, PRISm: 328, COPD: 195), and the study design. However, the section did not indicate the study period and setting, as well as the inclusion and exclusion criteria of the study.
The Results are detailed and fairly well-written, capturing the key objectives of the study – highlighting that the diagnostic models achieved AUCs up to 0.984 (PRISm vs. severe COPD) and 0.853 (PRISm vs. all COPD).
Also, the Conclusion section is a detailed summarization of key findings and their implications for clinical care, including that a radiation-efficient approach enables early COPD stratification via interpretable structural-functional metrics.
Recommended revisions
i) Lines 25 to 29: The authors need to clearly state the study period and setting, as well as the inclusion and exclusion criteria of the study, in the Methods section of the paper
ii) Lines 25 to 29: The authors need to indicate whether the study received ethical approval.
Introduction
The authors provided a detailed review of the literature on the interests in limitations of traditional biphasic CT scans, such as radiation exposure, and single-inspiratory CT-based deep learning lacks interpretability, especially in the diagnosis of preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD). The authors did highlight the epidemiology of COPD and that the disease is highly complex and heterogeneous, involving multiple inflammatory mechanisms that collectively drive disease progression. However, previous studies indicate that COPD diagnosis based on spirometry post-bronchodilator, defining airflow obstruction as a ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) <0.70, has many limitations, including detection after substantial structural lung damage and inability to differentiate COPD phenotypes, including PRISm. Therefore, the authors did adequately justify the need for the present study as there are limited studies that have employed a quantitative CT-based model for the early diagnosis of PRISm, and the study has well-written study objectives, which are: “to develop a single-inspiratory quantitative CT (QCT) nomogram for the early diagnosis of PRISm”.
Recommended revisions
i) The authors need to further highlight the advantages of quantitative CT (QCT) nomogram for the early diagnosis of PRISm, such as early COPD detection, differentiation of COPD from PRISm, risk stratification, personalized treatment, and lung densitometry. To this end, I recommend that the authors review the following studies to improve the manuscript:
• https://doi.org/10.1016/j.acra.2024.08.030
• https://doi.org/10.2147/COPD.S436803
Figures & Tables
All the Tables and Figures are complete, self-explanatory, and cited in the text.
Material and Methods
The authors provide an adequate description of the Methods, including the study design and participants, including all the necessary ethical approval. Also, the authors provided an adequate description of the inclusion and exclusion criteria as well as the study period. In addition, the authors provided adequate details of the CT and PFT measurements, as well as CT image quality, meeting the requirements for radiomics. Overall, the authors showed that Pulmonary Function and Imaging Data Acquisition were partly clear as PFTs were conducted using the GANSHORN PowerCube Diffusion+ system. Quantitative analysis of pulmonary CT images was performed using the Aview® system (Coreline Soft Inc., Seoul, South Korea). Also, lung parenchymal features, including mean lung density (MLD), emphysema index (EI), and pixel indices PI-1/PI-15 were determined through histogram thresholding.
Therefore, the Methods described are fairly repeatable, and an adequate level of detail has been provided to enable replication of the analysis. Also, details of the statistical methods were fairly well-written.
Results
The paper makes a meaningful contribution to the advancement of the field by describing the interplay between PRISm and quantitative CT. Overall, the authors reported the Pulmonary Function and Whole-Lung Quantitative CT Parameters showing that there were significant gradient differences among the normal group 192, n=135), PRISm group (n=328), and COPD group (n=195) regarding demographic characteristics, pulmonary function, and whole-lung CT parameters. Overall, the authors demonstrated Regional Specific Pulmonary Vascular Parameters, including that quantitative chest CT parameters in 658 participants (Normal: 135, PRISm: 328, COPD: 195), revealing heterogeneous vascular patterns at different distances from the pleural surface. Also, the Spearman correlation analysis revealed a multi-level association between quantitative CT parameters and pulmonary function indices. In addition, the authors demonstrated Validation of Diagnostic Model Performance. The diagnostic models constructed using stepwise logistic regression across four groups demonstrated a gradient of discriminative performance. The findings presented appear plausible and reasonable. All the Tables and Figures are complete, self-explanatory, and cited in the text. Although some of the Tables require additional clarification.
Recommended revisions
i) The authors need to clarify and provide details of how they created the CT nomogram and how they used it, or how it can be used following validation, including any cutoff values for each item of the nomogram.
Discussion
The Discussion is well written, as important studies in this area were reviewed and cited in the field by describing that single-inspiratory CT-based deep learning lacks interpretability, especially in the diagnosis of preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD). The authors did adequately discuss what is known in the literature and adequately compared their findings with other studies that suggest the importance of quantitative CT in diagnosing and stratifying PRISm, particularly concerning parenchymal and small airway alterations. Also, the authors discussed their findings, highlighting that quantitative uses a single inspiratory-phase CT scan combined with a logistic regression model to efficiently differentiate between PRISm and individuals with normal spirometry or mild-to-moderate COPD (AUC = 0.836 for PRISm vs. normal spirometry; AUC = 0.815 for PRISm vs. mild-to-moderate COPD), while maintaining parameter interpretability, and discussed this finding in relation to other studies. In addition, the authors did provide a detailed paragraph highlighting the limitations of the study.
Recommended revisions
i) Page 23, lines 394 to 398: “The authors should reflect in the Limitations that the quantitative CT measurements /assessments should be performed by two radiologists blinded to each other's findings to assess agreements between the radiologists to further strengthen the measurements made.
Conclusion
The Conclusion paragraph is fairly well-written does aligns with the key findings obtained in the study. In addition, the authors did provide clear implications from their findings to improve clinical practice, especially that finding highlights the imaging boundaries between PRISm and COPD through single-inspiratory quantitative CT parameters, highlighting the critical role of small airway remodeling and vessel diameter at 9 mm from the pleural surface as early biomarkers. In addition, the study offers novel insights into the pathophysiological mechanisms of COPD progression and the potential for precision in pRISm detection and management.
The authors give a good overview and a convincing rationale for the study.
The study is a retrospective analysis of well-characterized patients.
The study consists of a large sample. Methods are explained, and statistics are conducted well.
The findings are relevant for better diagnosis and treatment of early COPD.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.