Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model-An ORS spine section initiative.
Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model-An ORS spine section initiative.
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作者：
Melgoza IP;Chenna SS;Tessier S;Zhang Y;Tang SY;Ohnishi T;Novais EJ;Kerr GJ;Mohanty S;Tam V;Chan WCW;Zhou CM;Zhang Y;Leung VY;Brice AK;Séguin CA;Chan D;Vo N;Risbud MV;Dahia CL
小鼠已越来越多地用作临床前模型来阐明治疗椎间盘退变（IDD）的机制和测试治疗方法。已经提出了几种椎间盘（IVD）组织学评分系统，但不存在可靠的定量小鼠椎间盘病变。在这里，我们报告了一个新的强大的定量小鼠IVD组织病理学评分系统，通过建立共识，从脊柱社区分析以前的评分系统和功能，指出不同的小鼠模型IDD。新的评分系统分析了髓核（NP）、纤维环（AF）、终板（EP）和AF/NP/EP界面区域的14个关键组织病理学特征。对每个特征进行分类和评分;因此，用于量化椎间盘组织病理学的权重是均匀分布的，而不是仅由几个特征驱动。我们使用来自两种性别的不同IDD模型的腰椎和尾椎椎间盘图像测试了新的组织病理学评分标准，包括遗传、针刺、静态压缩模型和自然老化小鼠，从新生儿到老年阶段。此外，还分析了常用组织学制备技术和染色剂（包括H&E、番红O/Fast绿色和FAST）的椎间盘切片，以便更好地进行交叉研究比较。Fleiss的多名评分者一致性检验显示，经验丰富的和新手多名评分者对几种小鼠模型和使用各种组织学技术制备的切片的所有14个特征均具有显著一致性。新评分系统的灵敏度和特异性使用人工智能和监督和无监督机器学习算法进行了验证，包括人工神经网络，k均值聚类和主成分分析。最后，我们将新的评分系统应用于已建立的椎间盘退变模型，并证明了组织病理学评分变化的高敏感性和特异性。总的来说，新的组织病理学评分系统能够以高灵敏度和特异性量化椎间盘退变和再生小鼠模型的组织学变化。我们使用逐步的方法开发了一种新的小鼠椎间盘组织病理学（MERCY）系统，包括在脊柱社区建立共识，使用各种小鼠椎间盘退变模型测试可靠性以获得多个评分者的一致性，使用AI和机器学习算法验证高灵敏度和特异性，并应用于已建立的小鼠椎间盘退变模型。因此，该新系统可广泛应用于量化椎间盘退变和再生模型中的小鼠IVD组织病理学。
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. We have developed a new Mouse intErveRtebral disC histopathologY (MERCY) system using a step‐wise approach that included building consensus in the spine community, testing reliability using various mouse disc degeneration models for agreement by multiple raters, validating for high sensitivity and specificity using AI and machine learning algorithms, and applied on established models of murine disc degeneration. Hence, this new system can be broadly applied to quantify mouse IVD histopathology in disc degeneration and regeneration models.
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影响因子：
13.3
作者：
McCann, Matthew R.;Patel, Priya;Seguin, Cheryle A.
通讯作者：
Seguin, Cheryle A.
影响因子：
--
作者：
Hoy, Damian;Bain, Christopher;Buchbinder, Rachelle
通讯作者：
Buchbinder, Rachelle
影响因子：
7.8
作者：
Novais, Emanuel J.;Tran, Victoria A.;Risbud, Makarand V.
通讯作者：
Risbud, Makarand V.
影响因子：
3
作者：
Han, Bin;Zhu, Kai;Chen, Qi-xin
通讯作者：
Chen, Qi-xin
影响因子：
2.9
作者：
Liu, Jennifer W.;Piersma, Sytse;Tang, Simon Y.
通讯作者：
Tang, Simon Y.