Quantitative analysis of prion disease using an AI-powered digital pathology framework.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 10 2023
18 10 2023
Historique:
received:
18
07
2023
accepted:
12
10
2023
medline:
23
10
2023
pubmed:
19
10
2023
entrez:
18
10
2023
Statut:
epublish
Résumé
Prion disease is a fatal neurodegenerative disorder characterized by accumulation of an abnormal prion protein (PrPSc) in the central nervous system. To identify PrPSc aggregates for diagnostic purposes, pathologists use immunohistochemical staining of prion protein antibodies on tissue samples. With digital pathology, artificial intelligence can now analyze stained slides. In this study, we developed an automated pipeline for the identification of PrPSc aggregates in tissue samples from the cerebellar and occipital cortex. To the best of our knowledge, this is the first framework to evaluate PrPSc deposition in digital images. We used two strategies: a deep learning segmentation approach using a vision transformer, and a machine learning classification approach with traditional classifiers. Our method was developed and tested on 64 whole slide images from 41 patients definitively diagnosed with prion disease. The results of our study demonstrated that our proposed framework can accurately classify WSIs from a blind test set. Moreover, it can quantify PrPSc distribution and localization throughout the brain. This could potentially be extended to evaluate protein expression in other neurodegenerative diseases like Alzheimer's and Parkinson's. Overall, our pipeline highlights the potential of AI-assisted pathology to provide valuable insights, leading to improved diagnostic accuracy and efficiency.
Identifiants
pubmed: 37853094
doi: 10.1038/s41598-023-44782-4
pii: 10.1038/s41598-023-44782-4
pmc: PMC10584956
doi:
Substances chimiques
Prion Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17759Informations de copyright
© 2023. Springer Nature Limited.
Références
Das, A. S. & Zou, W.-Q. Prions: Beyond a single protein. Clin. Microbiol. Rev. 29, 633–658 (2016).
doi: 10.1128/CMR.00046-15
pubmed: 27226089
pmcid: 4978610
Morales, R. et al. Strain-dependent profile of misfolded prion protein aggregates. Sci. Rep. 6, 20526 (2016).
doi: 10.1038/srep20526
pubmed: 26877167
pmcid: 4753423
Rossi, M., Baiardi, S. & Parchi, P. Understanding prion strains: Evidence from studies of the disease forms affecting humans. Viruses 11, 309 (2019).
doi: 10.3390/v11040309
pubmed: 30934971
pmcid: 6520670
Lee, J. et al. Laboratory diagnosis and surveillance of Creutzfeldt–Jakob disease. J. Med. Virol. 87, 175–186 (2015).
doi: 10.1002/jmv.24004
pubmed: 24978677
Cali, I. et al. Classification of sporadic Creutzfeldt–Jakob disease revisited. Brain 129, 2266–2277 (2006).
doi: 10.1093/brain/awl224
pubmed: 16923954
Budka, H. et al. Neuro-pathological diagnostic criteria for CreutzfeldtJakob disease (CJD) and other human spongiform encephalopathies (prion diseases). Brain Pathol. 5, 459466 (1995).
Nishio, M., Nishio, M., Jimbo, N. & Nakane, K. Homology-based image processing for automatic classification of histopathological images of lung tissue. Cancers 13, 1192 (2021).
doi: 10.3390/cancers13061192
pubmed: 33801859
pmcid: 8001245
Rexhepaj, E. et al. A texture based pattern recognition approach to distinguish melanoma from non-melanoma cells in histopathological tissue microarray sections. PLoS ONE 8, e62070 (2013).
doi: 10.1371/journal.pone.0062070
pubmed: 23690928
pmcid: 3656869
Alinsaif, S. & Lang, J. Texture features in the Shearlet domain for histopathological image classification. BMC Med. Inform. Decis .Mak. 20, 1–19 (2020).
doi: 10.1186/s12911-020-01327-3
Trivizakis, E. et al. A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis. Sci. Rep. 11, 15546 (2021).
doi: 10.1038/s41598-021-94781-6
pubmed: 34330946
pmcid: 8324876
Haghighat, M. et al. Automated quality assessment of large digitised histology cohorts by artificial intelligence. Sci. Rep. 12, 5002 (2022).
doi: 10.1038/s41598-022-08351-5
pubmed: 35322056
pmcid: 8943120
Takamatsu, M. et al. Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence. Sci. Rep. 12, 2963 (2022).
doi: 10.1038/s41598-022-07038-1
pubmed: 35194184
pmcid: 8863850
Salvi, M. et al. Histopathological classification of canine cutaneous round cell tumors using deep learning: A multi-center study. Front. Vet. Sci. 8, 66 (2021).
doi: 10.3389/fvets.2021.640944
Salvi, M. et al. Automated assessment of glomerulosclerosis and tubular atrophy using deep learning. Comput. Med. Imaging Graph. 90, 101930 (2021).
doi: 10.1016/j.compmedimag.2021.101930
pubmed: 33964790
Laury, A. R., Blom, S., Ropponen, T., Virtanen, A. & Carpén, O. M. Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone. Sci. Rep. 11, 19165 (2021).
doi: 10.1038/s41598-021-98480-0
pubmed: 34580357
pmcid: 8476598
Salvi, M., Cerrato, V., Buffo, A. & Molinari, F. Automated segmentation of brain cells for clonal analyses in fluorescence microscopy images. J. Neurosci. Methods 325, 108348 (2019).
doi: 10.1016/j.jneumeth.2019.108348
pubmed: 31283938
Wang, C.-W. et al. Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci. Rep. 11, 16244 (2021).
doi: 10.1038/s41598-021-95545-y
pubmed: 34376717
pmcid: 8355253
Singh, R., Wu, W., Wang, G. & Kalra, M. K. Artificial intelligence in image reconstruction: The change is here. Phys. Med. 79, 113–125 (2020).
doi: 10.1016/j.ejmp.2020.11.012
pubmed: 33246273
Salvi, M. et al. Karpinski score under digital investigation: A fully automated segmentation algorithm to identify vascular and stromal injury of Donors’ kidneys. Electronics 9, 66 (2020).
doi: 10.3390/electronics9101644
Zhao, Y. & Li, X. Research on the application of artificial intelligence in medical imaging diagnosis. in 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT) 237–241 (IEEE, 2022).
Borghammer, P. How does Parkinson’s disease begin? Perspectives on neuroanatomical pathways, prions, and histology. Move. Disord. 33, 48–57 (2018).
doi: 10.1002/mds.27138
Kothari, S., Phan, J. H., Stokes, T. H. & Wang, M. D. Pathology imaging informatics for quantitative analysis of whole-slide images. J. Am. Med. Inform. Assoc. 20, 1099–1108 (2013).
doi: 10.1136/amiajnl-2012-001540
pubmed: 23959844
pmcid: 3822114
Van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A. & Viergever, M. A. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470 (2022).
doi: 10.1016/j.media.2022.102470
pubmed: 35576821
Saleem, T. J. et al. Deep learning-based diagnosis of Alzheimer’s disease. J. Pers. Med. 12, 815 (2022).
doi: 10.3390/jpm12050815
pubmed: 35629237
pmcid: 9143671
Chang, C.-H., Lin, C.-H. & Lane, H.-Y. Machine learning and novel biomarkers for the diagnosis of Alzheimer’s disease. Int. J. Mol. Sci. 22, 2761 (2021).
doi: 10.3390/ijms22052761
pubmed: 33803217
pmcid: 7963160
Pagano, G., Niccolini, F. & Politis, M. Imaging in Parkinson’s disease. Clin. Med. 16, 371 (2016).
doi: 10.7861/clinmedicine.16-4-371
Jin, L. et al. Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers. Neuro Oncol. 23, 44–52 (2021).
doi: 10.1093/neuonc/noaa163
pubmed: 32663285
Signaevsky, M. et al. Artificial intelligence in neuropathology: Deep learning-based assessment of tauopathy. Lab. Invest. 99, 1019–1029 (2019).
doi: 10.1038/s41374-019-0202-4
pubmed: 30770886
pmcid: 7684013
Bhakta, A. & Byrne, C. Creutzfeldt–Jakob disease prediction using machine learning techniques. in 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) 535–542 (IEEE, 2021).
Bizzi, A. et al. Evaluation of a new criterion for detecting prion disease with diffusion magnetic resonance imaging. JAMA Neurol. 77, 1141–1149 (2020).
doi: 10.1001/jamaneurol.2020.1319
pubmed: 32478816
Kovács, G. G. et al. Immunohistochemistry for the prion protein: comparison of different monoclonal antibodies in human prion disease subtypes. Brain Pathol. 12, 1–11 (2002).
doi: 10.1111/j.1750-3639.2002.tb00417.x
pubmed: 11770893
Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: Images are more than pictures, they are data. Radiology 278, 563–577 (2016).
doi: 10.1148/radiol.2015151169
pubmed: 26579733
Corrias, G., Micheletti, G., Barberini, L., Suri, J. S. & Saba, L. Texture analysis imaging “what a clinical radiologist needs to know”. Eur. J. Radiol. 146, 110055 (2022).
doi: 10.1016/j.ejrad.2021.110055
pubmed: 34902669
Davnall, F. et al. Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice?. Insights Imaging 3, 573–589 (2012).
doi: 10.1007/s13244-012-0196-6
pubmed: 23093486
pmcid: 3505569
Tomita, F., Tsuji, S., Tomita, F. & Tsuji, S. Statistical texture analysis. Comput. Anal. Vis. Text. 66, 13–36 (1990).
Galloway, M. M. Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4, 172–179 (1975).
doi: 10.1016/S0146-664X(75)80008-6
Pietikäinen, M. Local binary patterns. Scholarpedia 5, 9775 (2010).
doi: 10.4249/scholarpedia.9775
Jolliffe, I. T. Principal Component Analysis, 2nd edn (Springer, 2002).
De Jay, N. et al. mRMRe: An R package for parallelized mRMR ensemble feature selection. Bioinformatics 29, 2365–2368 (2013).
doi: 10.1093/bioinformatics/btt383
pubmed: 23825369
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 66 (2017).
Lin, T., Wang, Y., Liu, X. & Qiu, X. A survey of transformers. AI Open 6, 66 (2022).
Xiao, T., Liu, Y., Zhou, B., Jiang, Y. & Sun, J. Unified perceptual parsing for scene understanding. in Proceedings of the European conference on computer vision (ECCV) 418–434 (2018).
Lin, T.-Y. et al. Feature pyramid networks for object detection. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2117–2125 (2017).
Chu, X. et al. Twins: Revisiting the design of spatial attention in vision transformers. Adv. Neural Inf. Process. Syst. 34, 9355–9366 (2021).