下垂体性PRL分泌亢進症

PURPOSE: Prolactinomas are the commonest pituitary adenoma encountered in clinical practice, yet many aspects of their assessment and management remain variable. The 2023 Pituitary Society Consensus Statement on the diagnosis and management of prolactin-secreting pituitary adenomas provided important contemporary guidance, but the extent to which current practice aligns with these recommendations remains unclear. We aimed to define international multidisciplinary perspectives on resource availability and clinical decision-making in prolactinoma care. METHODS: We conducted an international cross-sectional survey of endocrinologists and neurosurgeons involved in prolactinoma management. Questions examined access to clinical resources, awareness of emerging issues in prolactinoma care, and areas of concordance or discordance between reported practice and published guidance. RESULTS: A total of 249 clinicians completed the survey, including 210 endocrinologists and 39 neurosurgeons, from America, Europe, Oceania and Asia. Important resource limitations included inconsistent reporting of Knosp grade and limited availability of transcription factor immunohistochemistry. Only 20% of respondents routinely referred newly diagnosed prolactinoma cases for multidisciplinary team discussion. Most endocrinologists counselled patients regarding dopamine agonist-related risks. Amongst neurosurgeons, reported indications for surgery and views on dopamine agonist-induced tumour fibrosis varied considerably. Although current guidelines support surgery as an equal first-line option in selected microadenomas, cabergoline remained the preferred initial treatment in these scenarios. CONCLUSION: International prolactinoma practice is heterogeneous and often discordant with contemporary guidance. Most notably, cabergoline therapy remains the preferred treatment strategy in microprolactinomas, and multidisciplinary discussion of prolactinoma cases is infrequent. This survey dataset serves as a roadmap for future research and clinical service improvement in prolactinoma care globally.

PURPOSE: Surgical resection is an important treatment modality for prolactinomas, yet approximately 20% of patients require postoperative dopamine agonist therapy (DAT) due to prolactin rebound. Accurate preoperative identification of this patient population would better inform surgical decision-making and longitudinal care. We aimed to develop a parsimonious, interpretable machine learning model to predict postoperative DAT requirement following prolactinoma resection. METHODS: A retrospective cohort of 138 patients who underwent surgical prolactinoma resection at a single institution between 2000 and 2024 was analyzed. Eight classification algorithms were evaluated on 75 preoperative features, including four clinically motivated engineered features. A five-feature model was identified through Gini-impurity-based feature selection. Nested cross-validation with a five-fold outer loop and five-fold inner loop was used for unbiased performance estimation. Shapley Additive Explanations (SHAP) were computed to characterize feature contributions to individual predictions. RESULTS: The cohort was nearly evenly split between patients requiring (49.3%) and not requiring (50.7%) postoperative DAT. The five selected features were preoperative serum prolactin, tumor volume, FSH, maximum tumor diameter, and prolactin density. Under nested cross-validation, Random Forest achieved the highest AUROC of 0.792 (95% CI: 0.656-0.928), with XGBoost (0.784), Gradient Boosting (0.780), and Logistic Regression (0.775) performing comparably. A pre-specified clinical logistic-regression baseline achieved an AUROC of 0.735, indicating a modest incremental gain from the machine learning model. Calibration (Brier score 0.199; calibration slope 0.906, intercept -0.022) and decision-curve analysis supported clinical utility across the relevant threshold range. SHAP analysis identified elevated preoperative prolactin, larger tumor volume, and lower FSH as the primary drivers of predicted medication requirements. CONCLUSION: A parsimonious five-feature model achieved modest, consistent discrimination for postoperative DAT requirement following prolactinoma resection, with several algorithms performing comparably and only a modest gain over a conventional clinical baseline. Nested cross-validation and SHAP analysis address key methodological limitations in existing pituitary machine learning literature. External multicenter validation is necessary prior to clinical implementation.

BACKGROUND: Multiple Endocrine Neoplasia type 1 (MEN1) is an autosomal dominant tumor predisposition syndrome involving primary hyperparathyroidism (PHPT), pituitary neuroendocrine tumors (PitNETs), and pancreatic neuroendocrine tumors. MEN1-associated insulinomas may present with multifocal disease, complicating management, while Cushing's disease is uncommon, and the coexistence of multiple functioning tumors complicates management. CASE PRESENTATION: A 28-year-old man presented with adrenocorticotropic hormone-dependent hypercortisolism and progressive cushingoid features. Pituitary Magnetic Resonance Imaging revealed a microadenoma, confirmed as a PitNET on histopathology. Concurrent PHPT and genetically confirmed MEN1 (splice-site pathogenic variant c.825-1G>A) were identified. Transsphenoidal surgery failed to achieve remission, and metyrapone provided partial control. Subtotal parathyroidectomy resulted in persistent PHPT. Imaging revealed multifocal pancreatic NETs with biochemically confirmed insulinoma. Lanreotide achieved complete resolution of hypoglycemia, while 68Ga-NOTA-exendin-4 positron emission tomography/computed tomography did not identify a dominant lesion, supporting conservative management. CONCLUSION: This case highlights the complexity of MEN1 with multiple functioning tumors and the clinical impact of hormonal interactions. It underscores the importance of multidisciplinary, individualized management, particularly when tumor multifocality limits surgical options. It also emphasizes the need to recognize complex endocrine syndromes in general clinical practice.

Lactotroph adenomas (prolactinomas, PRLomas) are the most frequently diagnosed pituitary neuroendocrine tumors and originate from anterior pituitary lactotroph cells. Excess prolactin secretion is the defining feature and typically leads to reproductive/endocrine dysfunction; when lesions enlarge, symptoms related to local compression may also occur. In clinical practice, microadenomas are reported predominantly in women, whereas men more often present with macroadenomas, a distribution that may relate to differences in symptom recognition and time to diagnosis. Current mechanistic data support a multifaceted model in which estrogen-related signaling, inherited or acquired genetic/epigenetic alterations, and dysregulated pathways, including PI3K/AKT/mTOR and RAS/RAF/MEK/ERK, intersect to promote tumor growth and functional activity. Patients therefore present along a spectrum, ranging from hypogonadism, infertility, and sex-specific manifestations to visual compromise, cranial neuropathies, or hypopituitarism in advanced disease. Dopamine agonists (DAs), particularly cabergoline, remain the standard first-line therapy; however, endoscopic surgery is increasingly used in selected patients (e.g., wellcircumscribed microadenomas and carefully chosen macroadenomas in experienced centers). For aggressive, DA-resistant, or malignant PRLomas, temozolomide remains the main systemic salvage option, and immunotherapy is being explored in early-phase settings. A precision approach will likely depend on integrating molecular stratification with clinical behavior to individualize treatment sequencing and improve long-term control and quality of life.

PURPOSE: Population-based studies overall report no excess mortality among patients with prolactinoma compared with the general population. However, since prolactinomas are clinically heterogeneous, mortality may not be uniform across data-driven patient profiles. METHODS: We analyzed 3,378 adult prolactinoma cases (26,118 person-years) from the Surveillance, Epidemiology, and End Results database (2004-2022). Standardized mortality ratios (SMRs) were calculated using US life tables matched by age, sex, calendar year, race/ethnicity, and geography. Among 2,481 patients with available tumor size (18,097 person-years), unsupervised clustering (Fuzzy C-Means) based on age, tumor size, and sex defined patient profiles. Excess mortality ratios (EMRs) compared observed-to-expected mortality across profiles, adjusting for race, calendar year, surgery, and radiotherapy. RESULTS: Prolactinoma was associated with significant excess all-cause mortality (SMR 1.21, 95% CI 1.03-1.41). Two profiles emerged: SAYF (Small and Young, Female-predominant; 63%) and LOOM (Large or Old, Male-predominant; 37%). Among patients with tumor size data, mortality was increased in LOOM (SMR 1.36, 95% CI 1.11-1.65) and reduced in SAYF (SMR 0.48, 95% CI 0.22-0.90), with significant between-profile heterogeneity (EMR 2.92, 95% CI 1.44-5.90; p = 0.003). Results were consistent in sensitivity analyses with imputed data, although the survival advantage in SAYF was attenuated and no longer statistically significant. CONCLUSION: Prolactinoma captured in a population-based tumor registry was associated with excess all-cause mortality, with heterogeneity across patient profiles. An older, male-predominant profile with larger tumors showed excess mortality, whereas a younger, female-predominant profile with small tumors showed directionally lower mortality. These hypothesis-generating findings warrant further investigation into the underlying biological and contextual factors.