BACKGROUND Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability. In order to develop a new histopathological outcome measure for SSc, we applied a computer vision technology called a deep neural network (DNN) to stained sections of SSc skin. We tested the hypotheses that DNN analysis could reliably assess mRSS and discriminate SSc from normal skin. METHODS We analyzed biopsies from two independent (primary and secondary) cohorts. One investigator performed mRSS assessments and forearm biopsies, and trichrome-stained biopsy sections were photomicrographed. We used the AlexNet DNN to generate a numerical signature of 4096 quantitative image features (QIFs) for 100 randomly selected dermal image patches/biopsy. In the primary cohort, we used principal components analysis (PCA) to summarize the QIFs into a Biopsy Score for comparison with mRSS. In the secondary cohort, using QIF signatures as the input, we fit a logistic regression model to discriminate between SSc vs. control biopsy, and a linear regression model to estimate mRSS, yielding Diagnostic Scores and Fibrosis Scores, respectively. We determined the correlation between Fibrosis Scores and the published Scleroderma Skin Severity Score (4S) and between Fibrosis Scores and longitudinal changes in mRSS on a per patient basis. RESULTS In the primary cohort (n = 6, 26 SSc biopsies), Biopsy Scores significantly correlated with mRSS (R = 0.55, p = 0.01). In the secondary cohort (n = 60 SSc and 16 controls, 164 biopsies; divided into 70% training and 30% test sets), the Diagnostic Score was significantly associated with SSc-status (misclassification rate = 1.9% [training], 6.6% [test]), and the Fibrosis Score significantly correlated with mRSS (R = 0.70 [training], 0.55 [test]). The DNN-derived Fibrosis Score significantly correlated with 4S (R = 0.69, p = 3 × 10- 17). CONCLUSIONS DNN analysis of SSc biopsies is an unbiased, quantitative, and reproducible outcome that is associated with validated SSc outcomes.

中文翻译：

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使用计算机视觉对系统性硬化症皮肤病进行高通量定量组织学。


背景皮肤纤维化是系统性硬化症（SSc）的临床标志，随着时间的推移，真皮会发生胶原沉积和重塑。 SSc 临床试验中最广泛使用的结果测量是改良的 Rodnan 皮肤评分 (mRSS)，它是对 17 个身体部位的皮肤硬度的半定量评估。然而，mRSS 因肥胖、水肿和评估者间的高变异性而受到干扰。为了开发一种新的 SSc 组织病理学结果测量方法，我们应用了一种称为深度神经网络 (DNN) 的计算机视觉技术来对 SSc 皮肤切片进行染色。我们测试了 DNN 分析可以可靠地评估 mRSS 并区分 SSc 和正常皮肤的假设。方法 我们分析了两个独立（主要和次要）队列的活检结果。一名研究人员进行了 mRSS 评估和前臂活检，并对三色染色的活检切片进行了显微照相。我们使用 AlexNet DNN 为 100 个随机选择的真皮图像块/活检生成 4096 个定量图像特征 (QIF) 的数字签名。在主要队列中，我们使用主成分分析 (PCA) 将 QIF 总结为活检评分，以便与 mRSS 进行比较。在第二队列中，使用 QIF 签名作为输入，我们拟合逻辑回归模型来区分 SSc 与对照活检，并拟合线性回归模型来估计 mRSS，分别产生诊断评分和纤维化评分。我们确定了每个患者的纤维化评分与已发表的硬皮病皮肤严重程度评分 (4S) 之间的相关性，以及纤维化评分与 mRSS 纵向变化之间的相关性。结果 在主要队列（n = 6，26 个 SSc 活检）中，活检评分与 mRSS 显着相关（R = 0.55，p = 0.01）。 在第二队列中（n = 60 个 SSc 和 16 个对照，164 个活检；分为 70% 训练集和 30% 测试集），诊断评分与 SSc 状态显着相关（错误分类率 = 1.9% [训练]、6.6%） [测试]），纤维化评分与 mRSS 显着相关（R = 0.70 [训练]，0.55 [测试]）。 DNN 衍生的纤维化评分与 4S 显着相关（R = 0.69，p = 3 × 10-17）。结论 SSc 活检的 DNN 分析是一种公正、定量且可重复的结果，与经过验证的 SSc 结果相关。