It is clinically essential, but sometimes challenging, to distinguish pituitary tissue from pituitary adenomas (PAs). It is helpful to avoid damage of pituitary tissue during management. We evaluated the ability of 13N-ammonia positron emission tomography (PET)/computed tomography (CT) to locate and distinguish pituitary tissue from PAs. Forty-eight patients (four with prolactinoma, 10 with Cushing's disease, 12 with acromegaly, and 22 with nonfunctional PAs) prospectively underwent magnetic resonance imaging (MRI), 13N-ammonia PET/CT, 18F-FDG PET/CT, prior to surgery. Pituitary position could be determined in 31 (64.5%) patients by 13N-ammonia PET/CT, and by MRI in 26 (54.2%) patients. It was detected by 13N-ammonia PET/CT and MRI in eight of eight patients (100%) with pituitary microadenoma, tumor maximum diameter (TMD) <1 cm, and in nine of 10 patients (90%) with PAs with TMD ≥1 cm, but <2 cm. In 16 patients with PAs with TMD ≥2 cm, but <3 cm, pituitary tissue position was detected by 13N-ammonia PET/CT in nine (56%), and by MRI in 8 (50.0%) patients by MRI. In 14 patients with PAs with TMD ≥3 cm, pituitary tissue position was detected by 13N-ammonia PET/CT in five (35.7%) patient, and by MRI in 1 (7.1%). In seven patients, the pituitary tissue could be detected by 13N-ammonia PET, but not by MRI, and in two patients by MRI, but not by 13N-ammonia PET. 13N-ammonia PET/CT imaging is a sensitive means for locating and distinguishing pituitary tissue from PAs, particularly those with TMD <2 cm. It is potentially valuable in detection of pituitary tissue in patients with PAs.

Introduction: Preoperative estimation of non-gross-total resection (non-GTR), as defined on early postoperative magnetic resonance imaging (MRI), may support patient counseling and postoperative surveillance planning after surgery for nonfunctioning pituitary neuroendocrine tumors (NF-PitNETs). We evaluated whether a clinically informed, interpretable preoperative modeling strategy improved risk estimation compared with a reference strategy. Methods: We retrospectively analyzed 354 first-surgery patients from a single center with a documented early postoperative MRI-based resection-status endpoint; 57 (16.1%) had non-GTR. A prespecified clinically informed logistic strategy extended a reference model based on routine preoperative clinical and MRI variables by adding transformed terms representing nonlinear tumor burden and invasion severity. Models underwent stratified repeated nested cross-validation with five outer folds repeated 20 times and four-fold inner tuning. Performance was evaluated using patient-level averaged held-out predictions. Results: Under model-specific tuning strategies, the clinically informed strategy showed little change in discrimination compared with the reference strategy (area under the receiver operating characteristic curve, 0.8054 vs. 0.8022; area under the precision-recall curve, 0.4603 vs. 0.4534), but had a lower Brier score (0.1100 vs. 0.1480) and calibration closer to ideal (intercept, 0.0256 vs. −0.3778; slope, 1.0209 vs. 2.5233). It also showed greater model-based net benefit across an exploratory threshold range of 0.05–0.50 and separated cohort-derived tertiles with observed non-GTR rates of 4.2%, 8.5%, and 35.6%. Postoperative pathological variables provided little incremental value. Discussion: These findings represent single-center internal validation of complete modeling strategies. Because the strategies differed in both predictor representation and tuning objective, the calibration difference cannot be attributed to the transformed predictors alone. Independent external validation using standardized postoperative MRI, together with uniform-tuning and reduced-model sensitivity analyses, is required before application to counseling or surveillance planning.

Purpose The aim of this study was to investigate the function and regulatory mechanism of Interleukin-6 (IL-6) in pituitary-derived folliculostellate (PDFS) cells and to explore the mechanism of metformin against PDFS cells growth through IL-6. Methods Immunohistochemical staining was conducted on clinical samples from non-functioning pituitary adenomas (NFPA) patients and normal individuals to assess IL-6 and Programmed Death-Ligand 1(PD-L1) expression. PDFS cells were treated with IL-6 to evaluate their effects on cell viability, proliferation, and migration through various assays. Similar assays were performed to assess the counteractive effects of metformin, focusing on the IL-6/ERK pathway and PD-L1 expression. Western blot analysis was utilized to examine apoptosis-related proteins, and Annexin V-FITC/PI double staining was used to detect cell apoptosis. It also involves assessing the effects of metformin treatment on tumor IL-6 and PD-L1 expression, tumor size, and potential toxic side effects in PDFS xenograft mice. Results Clinical samples showed increased IL-6 and PD-L1 expression in NFPA compared to normal pituitary tissues. IL-6 treatment significantly enhanced PDFS cell viability(Increased by 46% within 48 h), proliferation(Increased by 24% within 48 h), and migration(Increased by 19% within 48 h). Metformin treatment resulted in the downregulation of IL-6 expression and mitigated IL-6-induced effects on PDFS cells. Additionally, metformin-induced apoptosis and reduced tumor size in xenograft nude mice without observable toxic side effects. Conclusion Metformin downregulates the expression of IL-6 in PDFS cells, inhibits the activation of the ERK pathway, thereby suppressing cell proliferation and PD-L1 expression, and induces cell apoptosis.

Purpose: Differentiating between sparsely granulated and densely granulated somatotroph tumors (SGSTs and DGSTs) currently relies on postoperative immunohistochemistry. This study aimed to evaluate whether triglyceride (TG), uric acid (UA), and their composite TG–UA index [ln(TG × 1000/UA)] could serve as preoperative indicators for distinguishing granulation subtypes of somatotroph tumors. Methods: In this multicenter retrospective cohort study, 230 patients with somatotroph tumors were analyzed. Logistic regression and generalized additive models assessed associations and potential nonlinear associations between metabolic indicators and granulation subtypes. Predictive performance was compared between models using UA and TG separately and those using the TG–UA index. Results: SGSTs were associated with significantly higher TG, growth hormone, insulin‐like growth factor 1, and TG–UA index values. The TG–UA index remained an independent predictor of the SGST subtype (OR = 1.514, p = 0.014). Predictive performance was similar between models ( p = 0.108). Conclusion: The TG–UA index is a promising noninvasive biomarker for identifying the SGST subtype in somatotroph tumors. Although limited by its retrospective design and lack of long‐term data, this study provides a foundation for future prospective validation.