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<p>Pituitary neuroendocrine tumours (PitNETs) exhibit significant heterogeneity, posing challenges for clinical management. We developed a deep learning model to predict PitNET lineage, high-risk subtypes, and recurrence directly from routine H&amp;E-stained whole-slide images. Trained on 925 patients from USTC and externally validated on cohorts from Taihe Hospital (<i>n</i> = 226) and Huashan Hospital (<i>n</i> = 193), the model achieved a micro-average AUC of 0.912 for lineage classification (SF1: 0.926, PIT1: 0.932, TPIT: 0.904; Without distinct lineage: 0.706). High-risk subtype prediction yielded AUCs of 0.805 (PIT1), 0.753 (TPIT), and 0.733 (null cell). Recurrence prediction reached an AUC of 0.641. Analysis of the tumour microenvironment revealed that compared with primary tumours, recurrence tumours were characterized by an increased density of M2 macrophages and decreased infiltration of CD8 + T cells. Spatial transcriptomics further elucidated distinct molecular pathways associated with recurrence, providing mechanistic insights into prognostic predictions. Our deep learning model accurately predicts PitNET characteristics from routine H&amp;E slides, and spatial biology validation identified distinct immune and molecular features associated with recurrence.</p>

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Deep learning for predicting pituitary neuroendocrine tumour lineage and high-risk subtypes from histology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Anli Zhang,
  • Fang Zhao,
  • Daizhong Wang,
  • Chong Ge,
  • Jun Xu,
  • Xuhao Tian,
  • Lanqing Cheng,
  • Wei Wang,
  • Zunguo Du,
  • Ao Li,
  • Ji Xiong,
  • Minghui Wang,
  • Haibo Wu

摘要

Pituitary neuroendocrine tumours (PitNETs) exhibit significant heterogeneity, posing challenges for clinical management. We developed a deep learning model to predict PitNET lineage, high-risk subtypes, and recurrence directly from routine H&E-stained whole-slide images. Trained on 925 patients from USTC and externally validated on cohorts from Taihe Hospital (n = 226) and Huashan Hospital (n = 193), the model achieved a micro-average AUC of 0.912 for lineage classification (SF1: 0.926, PIT1: 0.932, TPIT: 0.904; Without distinct lineage: 0.706). High-risk subtype prediction yielded AUCs of 0.805 (PIT1), 0.753 (TPIT), and 0.733 (null cell). Recurrence prediction reached an AUC of 0.641. Analysis of the tumour microenvironment revealed that compared with primary tumours, recurrence tumours were characterized by an increased density of M2 macrophages and decreased infiltration of CD8 + T cells. Spatial transcriptomics further elucidated distinct molecular pathways associated with recurrence, providing mechanistic insights into prognostic predictions. Our deep learning model accurately predicts PitNET characteristics from routine H&E slides, and spatial biology validation identified distinct immune and molecular features associated with recurrence.