High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

Assessment of disease outcome measures in systemic sclerosis

The assessment of disease activity in systemic sclerosis (SSc) is challenging owing to its heterogeneous manifestations across multiple organ systems, the variable rate of disease progression and regression, and the relative paucity of patients in early-phase therapeutic trials. Despite some recent successes, most clinical trials have failed to show efficacy, underscoring the need for improved outcome measures linked directly to disease pathogenesis, particularly applicable for biomarker studies focused on skin disease. Current outcome measures in SSc-associated interstitial lung disease and SSc skin disease are largely adequate, although advancing imaging technology and the incorporation of skin mRNA biomarkers might provide opportunities for earlier detection of the therapeutic effect. Biomarkers can further inform pathogenesis, enabling early phase trials to act as reverse translational studies through the incorporation of routine high-throughput sequencing.

Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.

Cellular and Molecular Diversity in Scleroderma

With the increasing armamentarium of high-throughput tools available at manageable cost, it is attractive and informative to determine the molecular underpinnings of patient heterogeneity in systemic sclerosis (SSc). Given the highly variable clinical outcomes of patients labelled with the same diagnosis, unravelling the cellular and molecular basis of disease heterogeneity will be crucial to predicting disease risk, stratifying management and ultimately informing a patient-centered precision medicine approach. Herein, we summarise the findings of the past several years in the fields of genomics, transcriptomics, and proteomics that contribute to unraveling the cellular and molecular heterogeneity of SSc. Expansion of these findings and their routine integration with quantitative analysis of histopathology and imaging studies into clinical care promise to inform a scientifically driven patient-centred personalized medicine approach to SSc in the near future.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Computer vision applied to dual-energy computed tomography images for precise calcinosis cutis quantification in patients with systemic sclerosis

Background

Although treatments have been proposed for calcinosis cutis (CC) in patients with systemic sclerosis (SSc), a standardized and validated method for CC burden quantification is necessary to enable valid clinical trials. We tested the hypothesis that computer vision applied to dual-energy computed tomography (DECT) finger images is a useful approach for precise and accurate CC quantification in SSc patients.

Methods

De-identified 2-dimensional (2D) DECT images from SSc patients with clinically evident lesser finger CC lesions were obtained. An expert musculoskeletal radiologist confirmed accurate manual segmentation (subtraction) of the phalanges for each image as a gold standard, and a U-Net Convolutional Neural Network (CNN) computer vision model for segmentation of healthy phalanges was developed and tested. A validation study was performed in an independent dataset whereby two independent radiologists manually measured the longest length and perpendicular short axis of each lesion and then calculated an estimated area by assuming the lesion was elliptical using the formula long axis/2 × short axis/2 × π, and a computer scientist used a region growing technique to calculate the area of CC lesions. Spearman’s correlation coefficient, Lin’s concordance correlation coefficient with 95% confidence intervals (CI), and a Bland-Altman plot (Stata V 15.1, College Station, TX) were used to test for equivalence between the radiologists’ and the CNN algorithm-generated area estimates.

Results

Forty de-identified 2D DECT images from SSc patients with clinically evident finger CC lesions were obtained and divided into training (N = 30 with image rotation × 3 to expand the set to N = 120) and test sets (N = 10). In the training set, five hundred epochs (iterations) were required to train the CNN algorithm to segment phalanges from adjacent CC, and accurate segmentation was evaluated using the ten held-out images. To test model performance, CC lesional area estimates calculated by two independent radiologists and a computer scientist were compared (radiologist 1 vs. radiologist 2 and radiologist 1 vs. computer vision approach) using an independent test dataset comprised of 31 images (8 index finger and 23 other fingers). For the two radiologists’, and the radiologist vs. computer vision measurements, Spearman’s rho was 0.91 and 0.94, respectively, both p < 0.0001; Lin’s concordance correlation coefficient was 0.91 (95% CI 0.85–0.98, p < 0.001) and 0.95 (95% CI 0.91–0.99, p < 0.001); and Bland-Altman plots demonstrated a mean difference between radiologist vs. radiologist, and radiologist vs. computer vision area estimates of − 0.5 mm² (95% limits of agreement − 10.0–9.0 mm²) and 1.7 mm² (95% limits of agreement − 6.0–9.5 mm², respectively.

Conclusions

We demonstrate that CNN quantification has a high degree of correlation with expert radiologist measurement of finger CC area measurements. Future work will include segmentation of 3-dimensional (3D) images for volumetric and density quantification, as well as validation in larger, independent cohorts.

Skin Histology in Systemic Sclerosis: a Relevant Clinical Biomarker

PURPOSE OF REVIEW

Systemic sclerosis (SSc) is a life-threatening autoimmune disease that causes debilitating skin fibrosis. The skin in SSc is easily accessible, and skin biopsies may provide rich biological data regarding underlying disease pathophysiology. Here, we review literature relevant to the potential for skin histology to serve as a diagnostic, pharmacokinetic/response, and predictive biomarker in SSc.

RECENT FINDINGS

Multiple histologic parameters correlate with SSc severity, including alpha smooth muscle actin (aSMA), CD34, collagen density, thickness, and inflammatory cell infiltration. Recent clinical trials incorporate skin histology as exploratory outcome measurements; however, a standard approach is not yet established. The possibility that skin histology may be useful as a predictive biomarker was suggested by a recent study that identified genes related to skin aSMA and CD34 staining intensity that were increased at baseline among improvers versus nonimprovers. Current literature supports skin histology as a mechanism to measure treatment response, but future work is needed to define minimally meaningful changes in key SSc skin histologic features.