The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases

Son, Hye Joo; Oh, Jungsu S.; Oh, Minyoung; Kim, Soo Jong; Lee, Jae Hong; Roh, Jee Hoon; Kim, Jae Seung

doi:10.1007/s00259-019-04595-y

Cited by 45 publications

(45 citation statements)

References 25 publications

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“…Although diagnosis was typically confirmed by follow-up, it is possible that some of the patients were misdiagnosed. An alternative could be to use amyloid data from PET imaging or cerebrospinal fluid to classify AD pathology instead of relying on the clinical diagnosis (e.g., Son et al, 2020 ). In addition, because of the limited availability of diagnostic information at follow-up in the PND data set, its MCI data is relatively small.…”

Section: Discussionmentioning

confidence: 99%

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

Bron

Klein

Papma

et al. 2021

NeuroImage: Clinical

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This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; CI: ) than for CNN (AUC = 0.742; CI: ) ( for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; : ) and CNN (AUC = 0.702; CI: ). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images ( . Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.

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Section: Discussionmentioning

confidence: 99%

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

Bron

Klein

Papma

et al. 2021

NeuroImage: Clinical

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“…There are several advantages of using an “explainable” model rather than “black-box” approaches of deep-learning methods used previously for similar classification questions [ 39 – 41 ]. Foremost, amongst these are the ease of interpretability of the spatial features derived from clustering and its relative simplicity of implementation.…”

Section: Discussionmentioning

confidence: 99%

Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

et al. 2021

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Background Amyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD. Methods Voxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology. Results This classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr. Conclusions The diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.

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“…Advances in the theory of stochastic gradient (in an analogy of MLEM and OSEM) [97] made it possible to train ML with big data by faster convergence, which enabled us to train DL with a more complex structure more efficiently. [24,107,108] and Parkinson's disease [109], and brain perfusion reserve decreases [110]). These methods [115,116], increasing spatial resolution of PET [117], and generating an MR-like mask from amyloid PET [118]).…”

Section: ) Rise Of Ml/dl Algorithmsmentioning

confidence: 99%

“…The present standard of PET/CT scanners has incorporated many advanced hardware/software methods in recent decades. These developments in NMP have led to better image quality in terms of both sensitivity and spatial resolution, and have thus improved small lesion detectability (in tumor imaging, including FDG PET) [22], in vivo pathology detectability (in neurodegenerative disease imaging, including amyloid and tau PET) [23,24], and the general quantification accuracy of PET uptake.…”

Section: Limitations and Solutions For Nm/pet Imagingmentioning

confidence: 99%

Nuclear Medicine Physics: Review of Advanced Technology

2020

Progress in Medical Physics

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This review aims to provide a brief, comprehensive overview of advanced technologies of nuclear medicine physics, with a focus on recent developments from both hardware and software perspectives. Developments in image acquisition/reconstruction, especially the time-of-flight and point spread function, have potential advantages in the image signal-to-noise ratio and spatial resolution. Modern detector materials and devices (including lutetium oxyorthosilicate, cadmium zinc tellurium, and silicon photomultiplier) as well as modern nuclear medicine imaging systems (including positron emission tomography [PET]/computerized tomography [CT], whole-body PET, PET/magnetic resonance [MR], and digital PET) enable not only high-quality digital image acquisition, but also subsequent image processing, including image reconstruction and postreconstruction methods. Moreover, theranostics in nuclear medicine extend the usefulness of nuclear medicine physics far more than quantitative image-based diagnosis, playing a key role in personalized/precision medicine by raising the importance of internal radiation dosimetry in nuclear medicine. Now that deep-learning-based image processing can be incorporated in nuclear medicine image acquisition/processing, the aforementioned fields of nuclear medicine physics face the new era of Industry 4.0. Ongoing technological developments in nuclear medicine physics are leading to enhanced image quality and decreased radiation exposure as well as quantitative and personalized healthcare.

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The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases

Cited by 45 publications

References 25 publications

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

Nuclear Medicine Physics: Review of Advanced Technology

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