Dataset Information

Development of a standardized histopathology scoring system for human intervertebral disc degeneration: an Orthopaedic Research Society Spine Section Initiative.

ABSTRACT:

Background

Histopathological analysis of intervertebral disc (IVD) tissues is a critical domain of back pain research. Identification, description, and classification of attributes that distinguish abnormal tissues form a basis for probing disease mechanisms and conceiving novel therapies. Unfortunately, lack of standardized methods and nomenclature can limit comparisons of results across studies and prevent organizing information into a clear representation of the hierarchical, spatial, and temporal patterns of IVD degeneration. Thus, the following Orthopaedic Research Society (ORS) Spine Section Initiative aimed to develop a standardized histopathology scoring scheme for human IVD degeneration.

Methods

Guided by a working group of experts, this prospective process entailed a series of stages that consisted of reviewing and assessing past grading schemes, surveying IVD researchers globally on current practice and recommendations for a new grading system, utilizing expert opinion a taxonomy of histological grading was developed, and validation performed.

Results

A standardized taxonomy was developed, which showed excellent intra-rater reliability for scoring nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous end plate (CEP) regions (interclass correlation [ICC] > .89). The ability to reliably detect subtle changes varied by IVD region, being poorest in the NP (ICC: .89-.95) where changes at the cellular level were important, vs the AF (ICC: .93-.98), CEP (ICC: .97-.98), and boney end plate (ICC: .96-.99) where matrix and structural changes varied more dramatically with degeneration.

Conclusions

The proposed grading system incorporates more comprehensive descriptions of degenerative features for all the IVD sub-tissues than prior criteria. While there was excellent reliability, our results reinforce the need for improved training, particularly for novice raters. Future evaluation of the proposed system in real-world settings (eg, at the microscope) will be needed to further refine criteria and more fully evaluate utility. This improved taxonomy could aid in the understanding of IVD degeneration phenotypes and their association with back pain.

SUBMITTER: Le Maitre CL

PROVIDER: S-EPMC8313169 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

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