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Background <p>The modified Rodnan skin score (mRSS), a measure of systemic sclerosis (SSc) skin thickness, is agnostic to inflammation and vasculopathy. Previously, we demonstrated the potential of neural network-based digital pathology applied to SSc skin biopsies as a quantitative outcome. Here, we leverage deep learning and histologic analyses of clinical trial biopsies to decipher SSc skin features ‘seen’ by artificial intelligence (AI).</p> Methods <p>Adults with diffuse cutaneous SSc ≤ 6&#xa0;years were enrolled in an open-label trial of belumosudil [a Rho-associated coiled-coil containing protein kinase 2 (ROCK2) inhibitor]. Participants underwent serial mRSS and arm biopsies at week (W) 0, 24 and 52. Two blinded dermatopathologists scored stained sections (e.g., Masson’s trichrome, hematoxylin and eosin, CD3, α-smooth muscle actin) for 16 published SSc dermal pathological parameters. We applied our deep learning model to generate QIF signatures/biopsy and obtain ‘Fibrosis Scores’. Associations between Fibrosis Score and mRSS (Spearman correlation), and between Fibrosis Score and mRSS versus histologic parameters [odds ratios (OR)], were determined.</p> Results <p>Only ten patients were enrolled due to early study termination, and of those, five had available biopsies due to fixation issues. Median, interquartile range (IQR) for mRSS change (0–52 W) for the ten participants was -2 (-9—7.5) and for the five with biopsies was -2.5 (-11—7.5). The correlation between Fibrosis Score and mRSS was R = 0.3; <i>p</i> = 0.674. Per 1-unit mRSS change (0–52 W), histologic parameters with the greatest associated changes were (OR, 95% CI, <i>p</i>-value): telangiectasia (2.01, [(1.31—3.07], 0.001), perivascular CD3 + (0.99, [0.97—1.02], 0.015), and % of CD8 + among CD3 + (0.95, [0.89—1.01], 0.031). Likewise, per 1-unit Fibrosis Score change, parameters with greatest changes were (OR, <i>p</i>-value): hyalinized collagen (1.1, [1.04 – 1.16], &lt; 0.001), subcutaneous (SC) fat loss (1.47, [1.19—1.81], &lt; 0.001), thickened intima (1.21, [1.06—1.38], 0.005), and eccrine entrapment (1.14, [1—1.31], 0.046).</p> Conclusions <p>Belumosudil was associated with non-clinically meaningful mRSS improvement. The histologic features that significantly correlated with Fibrosis Score changes (<i>e.g.,</i> hyalinized collagen, SC fat loss) were distinct from those associated with mRSS changes (<i>e.g.,</i> telangiectasia and perivascular CD3 +). These data suggest that AI applied to SSc biopsies may be useful for quantifying pathologic features of SSc beyond skin thickness.</p>

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Neural network analysis as a novel skin outcome in a trial of belumosudil in patients with systemic sclerosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月11日

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  • Ilayda Gunes,
  • Elana J. Bernstein,
  • Shawn E. Cowper,
  • Gauri Panse,
  • Niki Pradhan,
  • Lucy Duran Camacho,
  • Nicolas Page,
  • Elizabeth Bundschuh,
  • Alyssa Williams,
  • Mary Carns,
  • Kathleen Aren,
  • Sarah Fantus,
  • Elizabeth R. Volkmann,
  • Heather Bukiri,
  • Chase Correia,
  • Vijaya B. Kolachalama,
  • F. Perry Wilson,
  • Seamus Mawe,
  • J. Matthew Mahoney,
  • Monique Hinchcliff

摘要

Background

The modified Rodnan skin score (mRSS), a measure of systemic sclerosis (SSc) skin thickness, is agnostic to inflammation and vasculopathy. Previously, we demonstrated the potential of neural network-based digital pathology applied to SSc skin biopsies as a quantitative outcome. Here, we leverage deep learning and histologic analyses of clinical trial biopsies to decipher SSc skin features ‘seen’ by artificial intelligence (AI).

Methods

Adults with diffuse cutaneous SSc ≤ 6 years were enrolled in an open-label trial of belumosudil [a Rho-associated coiled-coil containing protein kinase 2 (ROCK2) inhibitor]. Participants underwent serial mRSS and arm biopsies at week (W) 0, 24 and 52. Two blinded dermatopathologists scored stained sections (e.g., Masson’s trichrome, hematoxylin and eosin, CD3, α-smooth muscle actin) for 16 published SSc dermal pathological parameters. We applied our deep learning model to generate QIF signatures/biopsy and obtain ‘Fibrosis Scores’. Associations between Fibrosis Score and mRSS (Spearman correlation), and between Fibrosis Score and mRSS versus histologic parameters [odds ratios (OR)], were determined.

Results

Only ten patients were enrolled due to early study termination, and of those, five had available biopsies due to fixation issues. Median, interquartile range (IQR) for mRSS change (0–52 W) for the ten participants was -2 (-9—7.5) and for the five with biopsies was -2.5 (-11—7.5). The correlation between Fibrosis Score and mRSS was R = 0.3; p = 0.674. Per 1-unit mRSS change (0–52 W), histologic parameters with the greatest associated changes were (OR, 95% CI, p-value): telangiectasia (2.01, [(1.31—3.07], 0.001), perivascular CD3 + (0.99, [0.97—1.02], 0.015), and % of CD8 + among CD3 + (0.95, [0.89—1.01], 0.031). Likewise, per 1-unit Fibrosis Score change, parameters with greatest changes were (OR, p-value): hyalinized collagen (1.1, [1.04 – 1.16], < 0.001), subcutaneous (SC) fat loss (1.47, [1.19—1.81], < 0.001), thickened intima (1.21, [1.06—1.38], 0.005), and eccrine entrapment (1.14, [1—1.31], 0.046).

Conclusions

Belumosudil was associated with non-clinically meaningful mRSS improvement. The histologic features that significantly correlated with Fibrosis Score changes (e.g., hyalinized collagen, SC fat loss) were distinct from those associated with mRSS changes (e.g., telangiectasia and perivascular CD3 +). These data suggest that AI applied to SSc biopsies may be useful for quantifying pathologic features of SSc beyond skin thickness.