Assessing the clinical application of deep-learning-derived CT volumetric measures in neurodegenerative disease diagnostics

Background Existing bio fluid and imaging biomarkers used in research and clinical diagnostics of neurodegenerative diseases are often expensive or invasive and are mainly available in specialised care centres. CT is an affordable and widely available imaging modality predominantly used to evaluate structural abnormalities, but not for the volumetric quantification of neurodegeneration. Previously, we developed a deep learning model trained on MRI segmentations from individuals with paired CT and MR scans, which achieved high accuracy and robust tissue classification based on brain CT images. Purpose To explore the diagnostic utility of deep-learning-derived CT-based atrophy measures and study their association with relevant cognitive, biochemical and other imaging markers of neurodegenerative diseases. Materials and methods In this retrospective study, we analysed 917 CT and 744 MR scans from cognitively healthy participants of the Gothenburg H70 Birth Cohort (70.4 ± 2.6 years) and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore (73 Alzheimer’s disease, 20 vascular dementia, 22 cognitively normal; 74.0 ± 8.2 years). We tested associations between six CT-derived volumetric measures with clinical diagnosis, fluid and imaging biomarkers and cognition. Results In the Memory Clinic Cohort, deep-learning-derived CT-based atrophy measures differentiated cognitively healthy individuals from Alzheimer’s disease (AUC 0.88; 95% CI: 0.79-0.96) and vascular dementia (AUC 0.91; 95% CI: 0.81-1.00) patients with high accuracy levels comparable to MR-derived measures. Additionally, CT-based measures distinguished early, prodromal Alzheimer’s disease (AUC= 0.73, 95% CI: 0.62, 0.85) and prodromal vascular dementia patients from healthy individuals (CT-GM: AUC= 0.7, 95% CI: 0.51, 0.81). CT-derived volumes were significantly associated with measures of cognition and biochemical markers of neurodegeneration, notably plasma-derived neurofilament light (ρ=-0.43, p<0.001, in the Memory Clinic Cohort). Conclusion Our findings provide strong evidence for the potential of deep-learning-derived CT-based atrophy measures in aiding neurodegenerative disease diagnostics in primary care settings. ### Competing Interest Statement KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Ono Pharma, Pharmatrophix, Prothena, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. SK has served at scientific advisory boards and / or as consultant for Geras Solutions and Biogen. MS has served at an advisory board for Servier Pharmaceuticals, outside of the present work. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). ### Funding Statement HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), Swedish State Support for Clinical Research (#ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimers Association (#ADSF-21-831376-C, #ADSF-21-831381-C and #ADSF-21-831377-C), the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen f ör Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228), the European Unions Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), and the UK Dementia Research Institute at UCL. KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986), and European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236). KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), the National Institute of Health (NIH), USA, (grant #1R01AG068398-01), and the Alzheimers Association 2021 Zenith Award (ZEN-21-848495). SK was financed by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (ALFGBG-965923, ALFGBG-81392, ALF GBG-771071). The Alzheimerfonden (AF-842471, AF-737641, AF-15939825). The Swedish Research Council (2019-02075), Psykiatriska Forskningsfonden, Stiftelsen Demensfonden, Stiftelsen Hjalmar Svenssons Forskningsfond, Stiftelsen Wilhelm och Martina Lundgrens vetenskapsfond. MS is supported by the Knut and Alice Wallenberg Foundation (Wallenberg Centre for Molecular and Translational Medicine; KAW 2014.0363), the Swedish Research Council (#2017-02869), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-813971), and the Swedish Alzheimer Foundation (#AF-740191). Image analysis computations were in part carried out with resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Memory Clinic Cohort Study, Singapore (study protocol number DEM4333) was approved by the National Healthcare Group Domain Specific Review Board (Reference number: NHG DSRB 2018/01098-SRF0004). The H70 study was approved by the Regional Ethical Review Board in Gothenburg (Approval Numbers: 869-13, T076-14, T166-14, 976-13, 127-14, T936-15, 006-14, T703-14, 006-14, T201-17, T915-14, 959-15, T139-15), and by the Radiation Protection Committee (Approval Number: 13-64). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The Gothenburg H70 Birth cohort and Memory Clinic Cohort, Singapore cannot openly share data according to existing ethical and data sharing approvals, however, relevant data can and will be shared with research groups after submitting a research proposal which has to be approved by the respective study coordinators.