Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model—An ORS spine section initiative

Itzel Paola Melgoza, Srish S. Chenna, Steven Tessier, Yejia Zhang, Simon Y. Tang, Takashi Ohnishi, Emanuel José Novais, Geoffrey J. Kerr, Sarthak Mohanty, Vivian Tam, Wilson C.W. Chan, Chao Ming Zhou, Ying Zhang, Victor Y. Leung, Angela K. Brice, Cheryle A. Séguin, Danny Chan, Nam Vo, Makarand V. Risbud, Chitra L. Dahia

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Mice have been increasingly used as preclinical model to elucidate mechanisms and test therapeutics for treating intervertebral disc degeneration (IDD). Several intervertebral disc (IVD) histological scoring systems have been proposed, but none exists that reliably quantitate mouse disc pathologies. Here, we report a new robust quantitative mouse IVD histopathological scoring system developed by building consensus from the spine community analyses of previous scoring systems and features noted on different mouse models of IDD. The new scoring system analyzes 14 key histopathological features from nucleus pulposus (NP), annulus fibrosus (AF), endplate (EP), and AF/NP/EP interface regions. Each feature is categorized and scored; hence, the weight for quantifying the disc histopathology is equally distributed and not driven by only a few features. We tested the new histopathological scoring criteria using images of lumbar and coccygeal discs from different IDD models of both sexes, including genetic, needle-punctured, static compressive models, and natural aging mice spanning neonatal to old age stages. Moreover, disc sections from common histological preparation techniques and stains including H&E, SafraninO/Fast green, and FAST were analyzed to enable better cross-study comparisons. Fleiss's multi-rater agreement test shows significant agreement by both experienced and novice multiple raters for all 14 features on several mouse models and sections prepared using various histological techniques. The sensitivity and specificity of the new scoring system was validated using artificial intelligence and supervised and unsupervised machine learning algorithms, including artificial neural networks, k-means clustering, and principal component analysis. Finally, we applied the new scoring system on established disc degeneration models and demonstrated high sensitivity and specificity of histopathological scoring changes. Overall, the new histopathological scoring system offers the ability to quantify histological changes in mouse models of disc degeneration and regeneration with high sensitivity and specificity.

Original languageEnglish
Article numbere1164
JournalJOR Spine
Volume4
Issue number2
DOIs
StatePublished - Jun 2021

Keywords

  • aging
  • degeneration
  • pre-clinical models
  • structure-function relationships

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