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<p>Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model’s interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.</p>

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Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月15日

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  • Nicolas Coudray,
  • Michelle C. Juarez,
  • Maressa C. Criscito,
  • Adalberto Claudio Quiros,
  • Reason Wilken,
  • Stephanie R. Jackson Cullison,
  • Mary L. Stevenson,
  • Nicole A. Doudican,
  • Ke Yuan,
  • Jamie D. Aquino,
  • Daniel M. Klufas,
  • Jeffrey P. North,
  • Siegrid S. Yu,
  • Fadi Murad,
  • Emily Ruiz,
  • Chrysalyne D. Schmults,
  • Cristian D. Cardona Machado,
  • Javier Cañueto,
  • Anirudh Choudhary,
  • Alysia N. Hughes,
  • Alyssa Stockard,
  • Zachary Leibovit-Reiben,
  • Aaron R. Mangold,
  • Aristotelis Tsirigos,
  • John A. Carucci

摘要

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model’s interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.