Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features

Shinnosuke Sawano; Satoshi Kodera; Masataka Sato; Susumu Katsushika; Issei Sukeda; Hirotoshi Takeuchi; Hiroki Shinohara; Atsushi Kobayashi; Hiroshi Takiguchi; Kazutoshi Hirose; Tatsuya Kamon; Akihito Saito; Hiroyuki Kiriyama; Mizuki Miura; Shun Minatsuki; Hironobu Kikuchi; Yasutomi Higashikuni; Norifumi Takeda; Katsuhito Fujiu; Jiro Ando; Hiroshi Akazawa; Hiroyuki Morita; Issei Komuro

doi:10.1371/journal.pone.0276928

Peer Review History

Original SubmissionJuly 26, 2022
14 Sep 2022 Decision Letter - Xianwu Cheng, Editor PONE-D-22-20987Age prediction from coronary angiography using a deep neural network : age as a potential label to extract prognosis-related imaging features.PLOS ONE Dear Dr. Kodera Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Oct 29 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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We will update your Data Availability statement to reflect the information you provide in your cover letter. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors evaluated whether a deep learning neural network could estimate vascular age based on coronary angiographic imaging. They also confirmed the clinical usefulness of the age prediction. The findings are of clinically significance and have an impact. However, several issues should be resolved. Specific comments: The authors enrolled patients undergoing standby diagnostic CAG for any reason. In the study, there might be no control patients, in other words, healthy subjects. This may be a limitation of the study. Methods: CAGs with PCI and CABG were excluded in the study. Does this mean that patients undergoing PCI or CABG after index CAG were excluded? Or were only patients with ad-hoc PCI excluded? Please clarify. Methods, ‘Only those CAGs evaluated as “without clinically meaningful coronary stenosis (defined as 75% in at least one 9 branch)” were included. ‘: Even if coronary stenosis <75%, it may be the reason for cardiac ischemia. Therefore, FFR, iFR, scintigraphy, and so on are needed in the clinical settings. In the study, 65 y/o was a cut off age for younger and older vascular age groups. However, in Japanese clinical practice, average age for PCI seems much older (Cardiovasc Interv Ther. 2022 Apr;37(2):243-247.). Please refer it and discuss. Table 4: As a predictive value, LVEF is an important factor after onset of ACS. In addition, how did the authors adjust medical treatments to predict MACE? Reviewer #2: I have no competing interests. Interesting paper- òprobably it appears a buit out of focus to use deep learning to derive vascular age "gold standard" for vasciular age shoul,d be added Impact of vascular age on outcomes should be added Practical and clinical implications of vascular age should be added ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Hideki ISHII Reviewer #2: Yes: Fabrizio D'Ascenzo ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0276928.r001
Revision 1
21 Sep 2022 Author Response Response to the comment from the Reviewer #1. Thank you for your careful review of our manuscript. We have attempted to incorporate your valuable suggestion. We believe that your suggestion has significantly improved the overall scientific content of this work. Response to the specific question is given below: Specific comment1: The authors enrolled patients undergoing standby diagnostic CAG for any reason. In the study, there might be no control patients, in other words, healthy subjects. This may be a limitation of the study. Response 1. We agree with reviewer 1. The training data for our model may not have included healthy subjects. We revised the following sentence in the study limitations according to the reviewer #1’s suggestion: “Since we enrolled patients undergoing standby diagnostic CAG to train the model for any reason, there might be no control patients, in other words, healthy subjects.” (Please see line 20 of page 17 and line1 of page 18.) Specific comment2: Methods: CAGs with PCI and CABG were excluded in the study. Does this mean that patients undergoing PCI or CABG after index CAG were excluded? Or were only patients with ad-hoc PCI excluded? Please clarify. Response 2. We agree with reviewer 1. Patients undergoing PCI or CABG after index CAG were included in the study. We excluded patients who underwent ad-hoc PCI. If for some reason a wire or balloon used for PCI or measurement of fractional flow reserve was captured on the images, these images were manually excluded, as were contrast images of coronary artery bypass. We revised the following sentence in the study Methods according to the reviewer #1’s suggestion: “Consecutive patients aged ≥18 years who underwent standby diagnostic CAG for any reason between January 2010 and December 2015 at The University of Tokyo Hospital were reviewed. Patients undergoing PCI or CABG after index CAG were included in the study. Patients who underwent PCI concurrently diagnostic catheterization (i.e., ad-hoc PCI) were excluded. CAGs that a highly trained cardiologist judged as showing insufficient contrast effect were excluded after image processing, and at the same time, if for some reason a wire or balloon used for PCI or measurement of fractional flow reserve was captured on the images, these images were manually excluded, as were contrast images of coronary artery bypass.” (Please see line 1-8 of page 4.) Specific comment3: Methods, ‘Only those CAGs evaluated as “without clinically meaningful coronary stenosis (defined as 75% in at least one branch)” were included. ‘: Even if coronary stenosis <75%, it may be the reason for cardiac ischemia. Therefore, FFR, iFR, scintigraphy, and so on are needed in the clinical settings. Response 3. We agree with reviewer 1. FFR, iFR, scintigraphy, and so on are needed in the clinical settings to consider clinically meaningful coronary stenosis. In this study, it was difficult to obtain FFR, iFR, and scintigraphy information, so we revised following sentence in the method in order to accurately present the reality of this study. “Only those CAGs evaluated as “without coronary artery stenosis greater than 75%” were included.” (Please see lines 9-10 of page 4.) Specific comment4: In the study, 65 y/o was a cut off age for younger and older vascular age groups. However, in Japanese clinical practice, average age for PCI seems much older (Cardiovasc Interv Ther. 2022 Apr;37(2):243-247.). Please refer it and discuss. Response 4. We agree with reviewer 1. The average age for PCI is higher in Japanese clinical practice. In this single-center study, the mean age of patients was 67 years, and the cutoff was set at 65 years, which is younger than in Japanese epidemiological studies (32, Cardiovasc Interv Ther. 2022 Apr;37(2):243-247.). The results of this study are difficult to generalize, as noted in the limitations. In the future, we would like to examine the cutoff and usefulness of the method proposed in this study more deeply using National Registry data. We revised the following sentence in the study Methods according to the reviewer #1’s suggestion. We believe that your suggestion has significantly improved the discussion of this work. “In addition, the cut off age for younger and older vascular age groups was set at 65 years old, based on the average age of the ACS patients included in this study. It is difficult to generalize the method used in this study because, as already mentioned, it is the result of a single-centre retrospective study with a limited number of patients. In fact, the average age for PCI patients in Japanese clinical practice is known to be higher. In the future, it may be useful to examine the usefulness of predictive age from coronary angiography by large amount of CAG images and clinical data from ACS patients.” (Please see lines 4-10 of page 18.) Specific comment5: Table 4: As a predictive value, LVEF is an important factor after onset of ACS. In addition, how did the authors adjust medical treatments to predict MACE? Response 5. We agree with reviewer 1. Information on LVEF and medications at discharge for ACS patients was added to table 2; unfortunately, multivariate analysis including LVEF and medications did not show a significant association between predicted age and prognosis. The sample size of this study was very small, which may have limited its power, and further studies using larger data are warranted. The example of the results of the multivariate analysis will be submitted as " Response to Reviewers.docx". (Please see Response table1 and Response table2 in " Response to Reviewers.docx".) Response to the comment from the Reviewer #2. Thank you for your careful review of our manuscript. We have attempted to incorporate your valuable suggestion. We believe that your suggestion has significantly improved the overall scientific content of this work. Response to the specific question is given below: Specific comment1: òprobably it appears a buit out of focus to use deep learning to derive vascular age "gold standard" for vasciular age shoul,d be added Response 1. Thank you for your careful comment. The gold standard of the vascular age is evaluated as coronary artery calcium (CAC) scanning by computed tomography (CT) and carotid intima-media thickness (CIMT) assessment by B-mode ultrasonography (Cuocolo A, Klain M, Petretta M. Coronary vascular age comes of age. J Nucl Cardiol. 2017 Dec;24(6):1835-1836. doi: 10.1007/s12350-017-1078-6. Epub 2017 Oct 3. PMID: 28975506.). In our study, we used deep learning to analyze from a different perspective (age-related changes in coronary angiography) from the gold standard of vascular age. To show the gold standard for vascular age we describe in introduction as following sentence. “Vascular age is a concept in relation to the hypothesis that the conversion of chronological age to age derived from vascular imaging features will lead to more accurate assessment of an individual’s cardiovascular risk. Although coronary artery calcium (CAC) scoring by computed tomography (CT)(6) and carotid intima-media thickness (CIMT)(7) assessment by B-mode ultrasonography can be used to define vascular age(8), it is not clearly known whether CAG contains useful age-related imaging features.” (Please see lines 7-12 of page 3.) Specific comment2: Impact of vascular age on outcomes should be added Response 2. Thank you very much for your comment. In this study, the clinical prognosis may be worse in the older vascular age group predicted from CAG images. Patients undergoing coronary angiography are expected to be at high risk of arteriosclerosis, but age-related information obtained from coronary arteries may be used to assess these patient risk. To show the impact of vascular age on outcome we describe in discussion as following sentence. “In our analysis of ACS patients who underwent PCI at our institution, we showed that when the patients were stratified according to the vascular age estimated from pre-PCI CAG, the stratification showed a significant association with long-term outcomes. This suggests that pre-treated coronary artery status may provide useful information reflecting the patient prognosis. In general, the training of a neural network requires a large quantity of labelled data and the cost of labelling sufficient numbers of data entries to create the network can be enormous. However, the method used in this study to extract prognosis-related features from images on the basis of age, which has a well-established association with prognosis (30, 31), may have a potential for creating prognostic models.” (Please see lines 1-8 of page 17.) Specific comment3: Practical and clinical implications of vascular age should be added Response 3. Thanks very much for your comment. Since this study is a single-center retrospective study, it is difficult to generalize the findings obtained in this study immediately, but we consider the following opinions as a proof of concept. “The potential clinical implications of the present study should be noted. The age predicted using CAG imaging by neural network had high predictive value. Our study is particularly relevant because it adds further value to CAG and may lead to the exploration of new clinical findings that are potentially modifiable. For example, there is a method such as observing changes in vascular age over time to assist in determining drug therapy and follow-up periods, although this could not be assessed in the present study. Future studies to assess the impact of age-related coronary artery features in the clinical practice are warranted.” (Please see lines 9-14 of page 17.) Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0276928.r002
17 Oct 2022 Decision Letter - Xianwu Cheng, Editor Age prediction from coronary angiography using a deep neural network : age as a potential label to extract prognosis-related imaging features. PONE-D-22-20987R1 Dear Dr. Kodera We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: (No Response) ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: (No Response) ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: (No Response) ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Thank you for changings. The reviewer found that the manuscript has been significantly improved. There is no criticism. Reviewer #2: (No Response) ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Hideki ISHII Reviewer #2: Yes: Fabrizio D'Ascenzo ******** https://doi.org/10.1371/journal.pone.0276928.r003
Formally Accepted
20 Oct 2022 Acceptance Letter - Xianwu Cheng, Editor PONE-D-22-20987R1 Age prediction from coronary angiography using a deep neural network: age as a potential label to extract prognosis-related imaging features. Dear Dr. Kodera: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Associate Prof. Xianwu Cheng Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0276928.r004

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