Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Jonathan N. Thomas; Joanna Roopkumar; Tushar Patel

doi:10.1371/journal.pone.0260098

Peer Review History

Original SubmissionMay 8, 2021
27 May 2021 Decision Letter - Matias A Avila, Editor PONE-D-21-15285 Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study PLOS ONE Dear Dr. Patel, Dear Tushar, 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 Jul 02 2021 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 require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data. 5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information. [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 manuscript is technically sound and it represents an extensive piece of research in machine learning field. I have only some reflections and considerations to share with the authors. The major issues had been resolved adequately taking into account the limitation, principally the sample size. As far as it demonstrates the consistency of the data, techniques (5fold-CV, correctly defined train-validation cohorts) and the groups are well balanced, there is nothing to remark. In the case of the SVM and other mentioned approaches, when they reach a 100% sensitivity and the specificity falls, often is a result of sacrificing the smaller group classifying all samples into the bigger group, so the accuracy (normally the score used to fit the model) will reach the maximum. The use of ensemble and tandem methods is particularly indicated when the data is complex and the individual markers have small to medium predictive capacity (AUC=0.5-0.7) as it is in this case. The implementation of ResNet50 is a foot in the door in the good sense, because it’s easily customizable to normalize and remove the noise from raw data, for this purpose is currently used in computer vision. So it can be easily seen the future application of it to automatize the analysis. As a final remark, it will be very exciting to implement a reinforced learning to this approach, this will give to the algorithm the capacity of learning on each example and thereby it will be very useful in diagnostic units, but this is the future as far as it is a pilot study. Reviewer #2: Thomas and colleagues demonstrate in this proof of concept study the feasibility and utility of breath volatolomic profiling to classify liver disease (cirrhosis or portal hypertension). The manuscript is well conducted, material and methods detailed and results well structured and supported. I have only few minor suggestions: 1) Similar to determination of inter- and intra-individual variability across different samples or participant technical replicates, did authors evaluate the age-related variability? They have participants from 24 to 76 years old. 2) Did authors explore the correlation with the primary liver disease etiology? 3) In line 269, related to Figure 3 (graph on the right): authors have written that “four showed a relationship with disease stage”. I would suggest to explain that these four trend to increase with disease stage. ****** 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:** Jose Maria Herranz Alzueta Reviewer #2: No [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. 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Revision 1
22 Oct 2021 Author Response Response to reviewers. We thank the editor and reviewers for their careful and thoughtful review of our manuscript and for their comments. We have responded to each of these below: Reviewer #1: The manuscript is technically sound and it represents an extensive piece of research in machine learning field. I have only some reflections and considerations to share with the authors. The major issues had been resolved adequately taking into account the limitation, principally the sample size. As far as it demonstrates the consistency of the data, techniques (5fold-CV, correctly defined train-validation cohorts) and the groups are well balanced, there is nothing to remark. In the case of the SVM and other mentioned approaches, when they reach a 100% sensitivity and the specificity falls, often is a result of sacrificing the smaller group classifying all samples into the bigger group, so the accuracy (normally the score used to fit the model) will reach the maximum. The use of ensemble and tandem methods is particularly indicated when the data is complex and the individual markers have small to medium predictive capacity (AUC=0.5-0.7) as it is in this case. The implementation of ResNet50 is a foot in the door in the good sense, because it’s easily customizable to normalize and remove the noise from raw data, for this purpose is currently used in computer vision. So it can be easily seen the future application of it to automatize the analysis. As a final remark, it will be very exciting to implement a reinforced learning to this approach, this will give to the algorithm the capacity of learning on each example and thereby it will be very useful in diagnostic units, but this is the future as far as it is a pilot study. Response: We thank the reviewer for their helpful comments and insights. We concur that reinforced learning approaches could be interesting and we will embark on additional studies to explore these approaches in future. Reviewer #2: Thomas and colleagues demonstrate in this proof of concept study the feasibility and utility of breath volatolomic profiling to classify liver disease (cirrhosis or portal hypertension). The manuscript is well conducted, material and methods detailed and results well-structured and supported. I have only few minor suggestions: 1) Similar to determination of inter- and intra-individual variability across different samples or participant technical replicates, did authors evaluate the age-related variability? They have participants from 24 to 76 years old. Response: We have now examined the age-related variability in molecular features within biological replicates. A greater variability was observed in samples from participants aged 45yr or younger. These data are now included in the revised manuscript in lines 258-261, and the data included in the Supplementary Information as S3 Fig. 2) Did authors explore the correlation with the primary liver disease etiology? Response: We have examined the average peak area, and the median SD of each molecular feature across replicates from individuals with disease of different etiology. These data are now included in the revised manuscript and the data included in the Supplementary Information as S3 Fig. 3) In line 269, related to Figure 3 (graph on the right): authors have written that “four showed a relationship with disease stage”. I would suggest to explain that these four trend to increase with disease stage. Response: We thank the expert reviewer for their helpful comments and have revised this statement to clarify this as recommended by the reviewer. Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0260098.r002
3 Nov 2021 Decision Letter - Matias A Avila, Editor Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study PONE-D-21-15285R1 Dear Dr. Patel, 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. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. 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. Kind regards, Matias A Avila, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0260098.r003
Formally Accepted
17 Nov 2021 Acceptance Letter - Matias A Avila, Editor PONE-D-21-15285R1 Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study Dear Dr. Patel: 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 Dr Matias A Avila Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0260098.r004

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