-->PONE-D-26-02360-->-->Feature Integration of [18F]FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection-->-->PLOS One

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Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern 460 California.

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This material is based upon work supported by the Air Force Office of Scientific Research under Award No. FA9550-22-1-0272 (YK). Also, this work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2024-00406152) (YL). Also, this work was supported by Global - Learning & Academic research institution for Master’s, PhD students, and Postdocs (LAMP) Program of the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. RS-2023-00301938) (SK).

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5. One of the noted authors is a group or consortium [Alzheimer’s Disease Neuroimaging Initiative]. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

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Additional Editor Comments :

Dear Authors,

Thank you for submitting your manuscript to our journal. Your study addresses an interesting and potentially important topic. However, after careful evaluation, we believe that the manuscript requires major revision before it can be considered further.

Several issues need to be addressed to improve the quality and clarity of the work.

We encourage you to revise the manuscript substantially and resubmit a version that more clearly supports its conclusions.

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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

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

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-->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

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-->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

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-->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 present an FDG-PET–based machine learning framework using ADNI data, combining image-derived and region-based SUVr-derived features from the same scan for classification of cognitively normal versus cognitively impaired/AD subjects. The manuscript is generally well organized, and the comparative evaluation of baseline, fusion, and ensemble models is a strength. The study suggests that combining these two FDG-PET–derived representations may improve classification performance within this dataset.

However, I have several major concerns. The manuscript often frames FDG-PET in a way that appears to overextend general advantages of PET or molecular imaging that do not always apply specifically to FDG-PET, particularly with respect to molecular specificity and early detection of AD. In addition, the novelty is somewhat overstated, since both feature sets are derived from the same underlying FDG-PET scan rather than from distinct modalities or independent biological sources. I am also concerned by the repeated use of terms such as “quantification,” when the described analysis appears to involve regional SUVr extraction rather than quantitative PET analysis. Overall, the main findings are best interpreted as improved comparative model performance within an internal ADNI-based ML framework, and I encourage the authors to revise the framing and terminology throughout the manuscript accordingly.

A few comments and concerns as follows:

Major 1. Page 4, lines 83-85. The statement that FDG-PET plays a key role in the early detection of AD seems factually imprecise and potentially misleading. FDG-PET is better considered a marker of downstream neurodegenerative/metabolic change, whereas amyloid and tau biomarkers are more directly tied to early AD pathology.

More broadly, the manuscript appears to repeatedly attribute to FDG-PET some of the molecular specificity advantages that are more characteristic of other PET tracers or biomarker frameworks. The authors should carefully re-examine the wording throughout the manuscript and revise statements that overgeneralize PET’s molecular specificity in a way that does not accurately apply to FDG-PET.

Major 2. Page 5, lines 108-112. The logic in this paragraph is unclear. The authors first highlight inter-institutional variability in scanner resolution and imaging protocols as a key limitation to generalizability, but then shift to the need for models that perform well on small datasets. These are not equivalent issues. Good performance in small datasets does not by itself address cross-scanner, cross-site, or protocol-related heterogeneity. The authors should clarify this rationale and avoid implying that limited-data modeling alone can overcome institutional variability in PET acquisition.

Major 3. Page 9, lines 180-185. The term “quantification” appears imprecise here. As described, the analysis involves extraction/calculation of regional SUVr values rather than quantitative PET modeling. This terminology may overstate the nature of the PET analysis and should be revised accordingly. Because similar wording appears elsewhere in the manuscript, the authors should review and correct this terminology throughout the paper for greater methodological precision.

Major 4. Page 18, lines 367-370. The wording here is somewhat vague and may overstate the methodological novelty. As described in the Methods, both the image-derived and region-based features are derived from the same FDG-PET data, with the latter representing ROI-level SUVr values. Thus, the framework appears to combine two representations of the same modality rather than fundamentally different data sources. The authors should clarify this point and describe more explicitly how these complementary representations provide additional information.

Major 5. Page 20, Conclusion. The Conclusion appears somewhat overstated relative to the actual scope of the study. The reported results mainly show improved internal classification performance within ADNI when combining two FDG-PET-derived representations, rather than establishing early detection capability, clinical relevance, or superior disease differentiability in a broader diagnostic sense. In particular, the statements regarding “early detection of Alzheimer’s disease,” MMSE correlation “confirming” clinical relevance, and better disease differentiability compared with MMSE should be toned down. The conclusion would be more appropriate if it emphasized improved comparative model performance within this dataset and framed the clinical implications more cautiously.

Minor 1. Page 19, lines 397-399. This statement is unclear. The need for segmentation and region-based SUVR extraction does not by itself explain why only a small dataset was available, nor why such a dataset would better reflect real-world clinical settings. Please clarify the reason for the reduced sample size.

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Reviewer #1: No

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Feature Integration of [18F]FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection

PONE-D-26-02360R1

Dear Dr. Kang,

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.

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Kind regards,

Emanuele Bartolini, MD

Academic Editor

PLOS One

Additional Editor Comments (optional):

Dear Authors,

your revised manuscript has now filled the original defects and is suitable for publication.

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. 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

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-->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

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

**********

-->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

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-->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

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-->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: The authors have adequately addressed my previous comments. The revised manuscript now presents the FDG-PET framing and SUVr-based methodology more accurately, and the main claims are better aligned with the presented results. I have no further major concerns.

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-->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: No

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