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<p>Frozen section (Frozen) analysis is essential for intraoperative margin assessment in skin cancer surgery, but assessment is limited by freezing artifacts that obscure cellular detail. Formalin-fixed paraffin-embedded (FFPE) histopathology, the gold standard, provides higher quality but requires &gt;24 h for processing. Herein, we developed and validated generative AI models to translate Frozen images into AI-generated FFPE images (GenFFPE) using 2594 slides from 283 cases across five major skin cancer types. Four unpaired image-to-image models (CycleGAN, CUT, AIFFPE, and SANTA) were trained and compared using quantitative metrics and expert rankings; CUT demonstrated the best overall fidelity. External validation and a visual Turing test (accuracy 60.2%) confirmed image realism. Among 55 discrepant cases, GenFFPE-based reassessment increased diagnostic concordance by 53.3%. AI-based Frozen-to-FFPE translation is, thus, feasible and clinically meaningful, offering a potential tool to improve intraoperative diagnostic reliability and support decision-making for challenging tumor types such as extramammary Paget’s disease.</p>

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Translation of frozen sections into FFPE images for skin cancer resection margins using generative AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Sang-Hoon Lee,
  • Yu-Sung Chu,
  • Yosep Chong,
  • Seong Min Hong,
  • Eung Ho Choi,
  • Seung-Phil Hong,
  • Taeyeong Kim,
  • Sejung Yang,
  • Minseob Eom

摘要

Frozen section (Frozen) analysis is essential for intraoperative margin assessment in skin cancer surgery, but assessment is limited by freezing artifacts that obscure cellular detail. Formalin-fixed paraffin-embedded (FFPE) histopathology, the gold standard, provides higher quality but requires >24 h for processing. Herein, we developed and validated generative AI models to translate Frozen images into AI-generated FFPE images (GenFFPE) using 2594 slides from 283 cases across five major skin cancer types. Four unpaired image-to-image models (CycleGAN, CUT, AIFFPE, and SANTA) were trained and compared using quantitative metrics and expert rankings; CUT demonstrated the best overall fidelity. External validation and a visual Turing test (accuracy 60.2%) confirmed image realism. Among 55 discrepant cases, GenFFPE-based reassessment increased diagnostic concordance by 53.3%. AI-based Frozen-to-FFPE translation is, thus, feasible and clinically meaningful, offering a potential tool to improve intraoperative diagnostic reliability and support decision-making for challenging tumor types such as extramammary Paget’s disease.