Nasi-Kordhishti I, Hladik M, Kandilaris K, et al (2025) Transcription factor-based classification of pituitary adenomas / PitNETs: a comparative analysis and clinical implications across WHO 2004, 2017 and 2022 in 921 cases. Acta Neuropathol Commun 13(1):135. https://doi.org/10.1186/s40478-025-02050-8

Article  CAS  PubMed  PubMed Central  Google Scholar 

Asa SL, Ezzat S, Mete O (2025) Clinical integration and application of the 2022 WHO pituitary tumor classification. Neurooncol Adv 7(Suppl 1):i10-i16. https://doi.org/10.1093/noajnl/vdae145

Article  PubMed  PubMed Central  Google Scholar 

Asa SL, Mete O, Perry A, et al (2022) Overview of the 2022 WHO Classification of Pituitary Tumors. Endocr Pathol 33(1):6–26. https://doi.org/10.1007/s12022-022-09703-7

Article  CAS  PubMed  Google Scholar 

Louis DN, Perry A, Wesseling P, et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23(8):1231–1251. https://doi.org/10.1093/neuonc/noab106

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lalchungnunga H, Dampier CH, Singh O et al (2026) Classification accuracy of a hierarchical molecular inference-based deep-learning system for CNS tumour diagnosis: a multi-institutional, retrospective study. Lancet Oncol 27(2):243–253. https://doi.org/10.1016/S1470-2045(25)00661-8

Article  CAS  PubMed  Google Scholar 

Zhao J, Ji C, Cheng H, et al (2023) Digital image analysis allows objective stratification of patients with silent PIT1-lineage pituitary neuroendocrine tumors. J Pathol Clin Res 9(6):488–497. https://doi.org/10.1002/cjp2.340

Article  PubMed  PubMed Central  Google Scholar 

Wu G, Ning Z, Yan X, et al (2025) Deep learning based semi-automated model can predict lineage in patients with pituitary neuroendocrine tumors. Acta Neuropathol Commun 13(1):200. https://doi.org/10.1186/s40478-025-02104-x

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kameda-Smith MM, Lu J-Q (2020) The Pituitary Tumors and Their Tumor-Specific Microenvironment. Adv Exp Med Biol 1296:117–135. https://doi.org/10.1007/978-3-030-59038-3_7

Article  CAS  PubMed  Google Scholar 

Brussee S, Buzzanca G, Schrader AMR, et al (2025) Graph neural networks in histopathology: Emerging trends and future directions. Med Image Anal 101:103444. https://doi.org/10.1016/j.media.2024.103444

Article  PubMed  Google Scholar 

Li R, Yao J, Zhu X, et al (2018) Graph CNN for Survival Analysis on Whole Slide Pathological Images. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. LNCS 11071:174–182. Springer, Cham. https://doi.org/10.1007/978-3-030-00934-2_20

Nakhli R, Zhang A, Mirabadi A, et al (2023) CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 21063–21073. https://doi.org/10.1109/ICCV51070.2023.01926

Ali M, Richter S, Ertürk A, et al (2025) Graph neural networks learn emergent tissue properties from spatial molecular profiles. Nat Commun 16(1):8419. https://doi.org/10.1038/s41467-025-63758-8

Article  CAS  PubMed  PubMed Central  Google Scholar 

Di D, Zou C, Feng Y, et al (2023) Generating Hypergraph-Based High-Order Representations of Whole-Slide Histopathological Images for Survival Prediction. IEEE Trans Pattern Anal Mach Intell 45(5):5800–5815. https://doi.org/10.1109/TPAMI.2022.3209652

Article  PubMed  Google Scholar 

Weng Z, Seper A, Pryalukhin A, et al (2024) GrandQC: A comprehensive solution to quality control problem in digital pathology. Nat Commun 15(1):10685. https://doi.org/10.1038/s41467-024-54769-y

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang A, Jaume G, Vaidya A, et al (2025) Accelerating Data Processing and Benchmarking of AI Models for Pathology. arXiv preprint arXiv:2502.06750. https://arxiv.org/abs/2502.06750

Hoque MZ, Keskinarkaus A, Nyberg P, et al (2024) Stain normalization methods for histopathology image analysis: a comprehensive review and experimental comparison. Information Fusion 102:101997. https://doi.org/10.1016/j.inffus.2023.101997

Article  Google Scholar 

Reinhard E, Adhikhmin M, Gooch B, et al (2001) Color transfer between images. IEEE Comput Graph Appl 21(5):34–41. https://doi.org/10.1109/38.946629

Article  Google Scholar 

Chen RJ, Ding T, Lu MY, et al (2024) Towards a general-purpose foundation model for computational pathology. Nat Med 30(3):850–862. https://doi.org/10.1038/s41591-024-02857-3

Article  CAS  PubMed  PubMed Central  Google Scholar 

Arthur D, Vassilvitskii S (2007) k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp 1027–1035. https://doi.org/10.5555/1283383.1283494

Yan R, Zhang X, Jiang Z, et al (2026) Pathway-Aware Multimodal Transformer (PAMT): Integrating Pathological Image and Gene Expression for Interpretable Cancer Survival Analysis. IEEE Trans Pattern Anal Mach Intell 48(1):896–913. https://doi.org/10.1109/TPAMI.2025.3611531

Article  PubMed  Google Scholar 

Kipf TN, Welling M (2017) Semi-Supervised Classification with Graph Convolutional Networks. arXiv preprint arXiv:1609.02907. https://arxiv.org/abs/1609.02907

Graham S, Vu QD, Raza SEA, et al (2019) Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal 58:101563. https://doi.org/10.1016/j.media.2019.101563

Article  PubMed  Google Scholar 

Quinot V, Herta J, Stiglbauer-Tscholakoff A, et al (2025) Synchronous pituitary neuroendocrine tumors (PitNETs)/adenomas of triple SF1/PIT1/TPIT cell lineages with multiple hormone expression. Clin Neuropathol 44(2):55–62. https://doi.org/10.5414/NP301630

Article  PubMed  Google Scholar 

Mete O, Alshaikh OM, Cintosun A, et al (2018). Synchronous Multiple Pituitary Neuroendocrine Tumors of Different Cell Lineages. Endocr Pathol 29(4):332–338. https://doi.org/10.1007/s12022-018-9545-4

Article  CAS  PubMed  Google Scholar 

Ricklefs FL, Fita KD, Rotermund R, et al (2020) Genome-wide DNA methylation profiles distinguish silent from non-silent ACTH adenomas. Acta Neuropathol 140(1):95–97. https://doi.org/10.1007/s00401-020-02149-3

Article  PubMed  PubMed Central  Google Scholar 

Neou M, Villa C, Armignacco R, et al (2020) Pangenomic Classification of Pituitary Neuroendocrine Tumors. Cancer Cell 37(1):123–134.e5. https://doi.org/10.1016/j.ccell.2019.11.002

Article  CAS  PubMed  Google Scholar 

Belakhoua S, Vasudevaraja V, Schroff C, et al (2025) DNA methylation profiling of pituitary neuroendocrine tumors identifies distinct clinical and pathological subtypes based on epigenetic differentiation. Neuro Oncol 27(9):2341–2354. https://doi.org/10.1093/neuonc/noaf109

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dottermusch M (2025) Pituitary Neuroendocrine Tumor or Pituitary Adenoma? Let’s Ask the Epigenome! Endocr Pathol 36:35. https://doi.org/10.1007/s12022-025-09879-8

Article  PubMed  PubMed Central  Google Scholar 

Rafiq T, Matschke J, Flitsch J, et al (2025) Three Synchronous Pituitary Neuroendocrine Tumors—Epigenomics Confirm an Exceptional Triple PitNET. Endocr Pathol 36:19. https://doi.org/10.1007/s12022-025-09864-1

Article  PubMed  PubMed Central  Google Scholar