Scholarlink

<p>Predicting cancer patient disease progression is a key step towards personalized medicine and secondary prevention. Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. We developed a transformer-based approach for predicting progression of cutaneous squamous cell carcinoma (cSCC) patients based on diagnostic histopathology tumor slides. Our initial model showed AUROC = 0.92 on a held-out test set, with average AUROC of 0.65 on external validation cohorts. To further increase generalizability and reduce potential privacy concerns, we trained the model in a federated manner across three clinical centers, reaching AUROC = 0.82 across all cohorts, with image-based risk scores achieving hazard ratios up to 7.42 (<i>p</i> &lt; 0.01) in multivariable analyses. Through interpretability analysis, we identified spatial and morphological features predictive of progression, suggesting that tumor boundary information and tissue heterogeneity characterize progressive cSCCs. Trained exclusively on routine diagnostic slides and offering biological insights, our model can improve secondary prevention and understanding of cSCC while enabling deployment across clinical centers without administrative overheads or privacy concerns.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月28日

查看全文
  • Juan I. Pisula,
  • Doris Helbig,
  • Lucas Sancéré,
  • Oana-Diana Persa,
  • Corinna Bürger,
  • Anne Fröhlich,
  • Carina Lorenz,
  • Sandra Bingmann,
  • Dennis Niebel,
  • Konstantin Drexler,
  • Jennifer Landsberg,
  • Roman Thomas,
  • Katarzyna Bozek,
  • Johannes Brägelmann

摘要

Predicting cancer patient disease progression is a key step towards personalized medicine and secondary prevention. Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. We developed a transformer-based approach for predicting progression of cutaneous squamous cell carcinoma (cSCC) patients based on diagnostic histopathology tumor slides. Our initial model showed AUROC = 0.92 on a held-out test set, with average AUROC of 0.65 on external validation cohorts. To further increase generalizability and reduce potential privacy concerns, we trained the model in a federated manner across three clinical centers, reaching AUROC = 0.82 across all cohorts, with image-based risk scores achieving hazard ratios up to 7.42 (p < 0.01) in multivariable analyses. Through interpretability analysis, we identified spatial and morphological features predictive of progression, suggesting that tumor boundary information and tissue heterogeneity characterize progressive cSCCs. Trained exclusively on routine diagnostic slides and offering biological insights, our model can improve secondary prevention and understanding of cSCC while enabling deployment across clinical centers without administrative overheads or privacy concerns.