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Controls-based denoising, a new approach for medical image analysis, improves prediction of conversion to Alzheimer’s disease with FDG-PET

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Objective

The pattern expression score (PES), i.e., the degree to which a pathology-related pattern is present, is frequently used in FDG-brain-PET analysis and has been shown to be a powerful predictor of conversion to Alzheimer’s disease (AD) in mild cognitive impairment (MCI). Since, inevitably, the PES is affected by non-pathological variability, our aim was to improve classification with the simple, yet novel approach to identify patterns of non-pathological variance in a separate control sample using principal component analysis and removing them from patient data (controls-based denoising, CODE) before calculating the PES.

Methods

Multi-center FDG-PET from 220 MCI patients (64 non-converter, follow-up ≥ 4 years; 156 AD converter, time-to-conversion ≤ 4 years) were obtained from the ADNI database. Patterns of non-pathological variance were determined from 262 healthy controls. An AD pattern was calculated from AD patients and controls. We predicted AD conversion based on PES only and on PES combined with neuropsychological features and ApoE4 genotype. We compared classification performance achieved with and without CODE and with a standard machine learning approach (support vector machine).

Results

Our model predicts that CODE improves the signal-to-noise ratio of AD-PES by a factor of 1.5. PES-based prediction of AD conversion improved from AUC 0.80 to 0.88 (p= 0.001, DeLong’s method), sensitivity 69 to 83%, specificity 81% to 88% and Matthews correlation coefficient (MCC) 0.45 to 0.66. Best classification (0.93 AUC) was obtained when combining the denoised PES with clinical features.

Conclusions

CODE, applied in its basic form, significantly improved prediction of conversion based on PES. The achieved classification performance was higher than with a standard machine learning algorithm, which was trained on patients, explainable by the fact that CODE used additional information (large sample of healthy controls). We conclude that the proposed, novel method is a powerful tool for improving medical image analysis that offers a wide spectrum of biomedical applications, even beyond image analysis.

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Acknowledgements

We acknowledge the funding received from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 603646 (MultISyn).

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 California.

Funding

The research leading to these results received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 603646 (MultISyn).

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Authors and Affiliations

  1. Institute for Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University, Tuebingen, Germany

    Dominik Blum, Christian la Fougère & Matthias Reimold

  2. German Center of Neurodegenerative Diseases, Eberhard Karls University, Tuebingen, Germany

    Inga Liepelt-Scarfone & Thomas Gasser

  3. Hertie-Institute for Clinical Brain Research, Eberhard Karls University, Tuebingen, Germany

    Inga Liepelt-Scarfone & Thomas Gasser

  4. Department of Neurology, Christian-Albrechts-University, Kiel, Germany

    Daniela Berg

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Correspondence to Dominik Blum.

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Conflict of interest

Dominik Blum declares that he has no conflict of interest. Inga Liepelt-Scarfone declares that she has no conflict of interest. Daniela Berg declares that she has no conflict of interest. Thomas Gasser declares that he has no conflict of interest. Christian la Fougère declares that he has no conflict of interest. Matthias Reimold declares that he has no conflict of interest.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Blum, D., Liepelt-Scarfone, I., Berg, D. et al. Controls-based denoising, a new approach for medical image analysis, improves prediction of conversion to Alzheimer’s disease with FDG-PET. Eur J Nucl Med Mol Imaging 46, 2370–2379 (2019). https://doi.org/10.1007/s00259-019-04400-w

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