Scholarlink

<p>Deep learning is expected to aid pathologists in tasks such as tumour segmentation. We developed a general tumour segmentation model for histopathological images and examined its performance in different cancer types. The model was developed using over 20,000 whole-slide images from over 4000 patients with colorectal, endometrial, lung, or prostate carcinoma. Performance was validated in pre-planned analyses on external cohorts with over 3000 patients across six cancer types. Exploratory analyses included over 1500 additional patients from The Cancer Genome Atlas. Average Dice coefficient was over 80% in all validation cohorts with en bloc resection specimens and in The Cancer Genome Atlas cohorts. No performance loss was observed when comparing the general model with single-cancer models specialised in cancer types from the development set. In conclusion, extensive and rigorous evaluations demonstrate that generic tumour segmentation by a single model is possible across cancer types, patient populations, sample preparations and slide scanners.</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

Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月4日

查看全文
  • Ole-Johan Skrede,
  • Manohar Pradhan,
  • Maria Xepapadakis Isaksen,
  • Tarjei Sveinsgjerd Hveem,
  • Ljiljana Vlatkovic,
  • Arild Nesbakken,
  • Kristina Lindemann,
  • Gunnar B. Kristensen,
  • Jenneke Kasius,
  • Alain G. Zeimet,
  • Odd Terje Brustugun,
  • Lill-Tove Rasmussen Busund,
  • Elin H. Richardsen,
  • Erik Skaaheim Haug,
  • Bjørn Brennhovd,
  • Emma Rewcastle,
  • Melinda Lillesand,
  • Vebjørn Kvikstad,
  • Emiel Janssen,
  • David J. Kerr,
  • Knut Liestøl,
  • Fritz Albregtsen,
  • Andreas Kleppe

摘要

Deep learning is expected to aid pathologists in tasks such as tumour segmentation. We developed a general tumour segmentation model for histopathological images and examined its performance in different cancer types. The model was developed using over 20,000 whole-slide images from over 4000 patients with colorectal, endometrial, lung, or prostate carcinoma. Performance was validated in pre-planned analyses on external cohorts with over 3000 patients across six cancer types. Exploratory analyses included over 1500 additional patients from The Cancer Genome Atlas. Average Dice coefficient was over 80% in all validation cohorts with en bloc resection specimens and in The Cancer Genome Atlas cohorts. No performance loss was observed when comparing the general model with single-cancer models specialised in cancer types from the development set. In conclusion, extensive and rigorous evaluations demonstrate that generic tumour segmentation by a single model is possible across cancer types, patient populations, sample preparations and slide scanners.