ArticleThe journal of pathology. Clinical research2023
Digital image analysis allows objective stratification of patients with silent PIT1-lineage pituitary neuroendocrine tumors.
Article in The journal of pathology. Clinical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 5 citations in OpenAlex.
- Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning.Endocrine pathology · 2026Article
- Deep learning for predicting pituitary neuroendocrine tumour lineage and high-risk subtypes from histology.NPJ precision oncology · 2026Article
- Exploring the Dynamic Interaction Between Pituitary Neuroendocrine Tumors (Pit-NETs) Cells and Their Angiogenic Microenvironment by Using the MIB1 Labeling Index, VEGF Expression and Digital Image Analysis.Current issues in molecular biology · 2025Article
- Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI.Journal of imaging informatics in medicine · 2025Article
- Pathological characteristics of reoperated regrowing clinically nonfunctioning pituitary tumor cases in comparison with initial surgical cases.Frontiers in endocrinology · 2024Article
- Differences in invasiveness and recurrence rate among nonfunctioning pituitary neuroendocrine tumors depending on tumor subtype.Neurosurgical review · 2023Article
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Authors and funding
17 authors at 2 institutions in 1 country.
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Abstract
Studies describing the clinical presentation and prognosis of patients with silent PIT1 (pituitary specific transcription factor)-lineage pituitary neuroendocrine tumors (PitNETs) are rare. We identified patients with positive PIT1 tumor staining but without evidence of hormone hypersecretion at a tertiary center. Clusters were obtained according to cell morphology and immunostaining from each patient's digitally segmented whole slide image. We compared the clinical presentations, radiological features, and prognoses of the different clusters. We identified 146 patients (68 male, 42.9 ± 14.1 years old) with silent PIT1-lineage PitNETs. Morphology clustering suggested that tumors with large nuclei and apparent eccentricity were associated with a higher proportion of aggressiveness and a higher hazard of recurrence [hazard ratio (HR): 2.64, (95% CI, 1.06-6.55), p = 0.037]. Immunohistochemical clustering suggested that tumors with thyroid stimulating hormone (TSH) staining or all negative PIT1-lineage hormones were associated with a higher proportion of aggressiveness and a higher risk of recurrence [HR: 12.4, (95% CI, 1.60-93.5), p = 0.015]. We obtained three-tier risk profiles by combining morphological and immunohistochemical clustering. Patients with the high-risk profile presented the highest recurrence rate compared with those in the medium-risk and low-risk profiles [HR: 3.54, (95% CI, 1.40-8.93), p = 0.002]. In conclusion, digital image analysis based on cell morphology and immunohistochemical staining allows objective stratification of patients with silent PIT1-lineage tumors. Typical morphological characteristics of high-risk tumors are large tumor nuclei and high eccentricity, and typical immunostaining characteristics are TSH staining or negative staining for all PIT1-lineage hormones.
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