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.

Background: To develop non-invasive, interpretable machine learning (ML) models using radiomic and clinical ­features to distinguish sparsely granulated (SGST) and densely granulated (DGST) somatotroph tumors by leveraging preoperative multiparametric magnetic resonance imaging (MRI) and clinical data. Methods: We retrospectively analyzed 201 patients (107 DGST, 94 SGST) with surgically treated somatotroph tumors across four institutions (156 in the training cohort, 45 in the external validation cohort). From preoperative contrast-enhanced T1 and T2-weighted MRI scans, 3004 radiomic features were extracted using Pyradiomics. Feature selection involved least absolute shrinkage and selection operator (LASSO), and the Boruta algorithm. Six ML-algorithms were evaluated, with the support vector machine (SVM) model selected for tumor subtype classification. Shapley Additive Explanations (SHAP) enhanced model interpretability by ranking feature importance. Results: The SVM model, integrating eight radiomic features and five clinical factors, achieved areas under the curve (AUCs) of 0.828 in the internal training cohort and 0.820 in the external validation cohort. SHAP analysis identified key radiomic and clinical predictors, enhancing the model’s transparency and clinical applicability. Conclusion: This multi-center study validates an interpretable, radiomics-based SVM model with high accuracy and generalizability for preoperative classification of somatotroph tumor granulation patterns. By offering a non-invasive tool to predict tumor subtypes, this approach enhances personalized treatment planning and holds translational potential for improving acromegaly management.

Background: Nonfunctioning pituitary adenoma (NFPA) is among the most prevalent intracranial tumors. As surgical resection remains the primary treatment, there is a need for new therapeutic strategies. Plumbagin (PLB), a bioactive naphthoquinone compound derived from the roots of Plumbaginaceae Juss, has been used in anti-tumor research in recent years, but its mechanism remains controversial. Purpose: The aim of this study was to explore the molecular mechanism of PLB-induced ferroptosis in NFPA. Methods: The cell viability of PLB against PDFS cells was first assessed by CCK-8, colony formation, and EDU assays. Network pharmacology, molecular docking, and molecular dynamics were then used to screen out targets associated with ferroptosis. Finally, the Nrf2/FTH1 pathway was validated by a 3D multicellular tumour spheroids (MCTSs) model, q-PCR, and Western blot in vitro and in vivo. Results: Compared to temozolomide (TMZ), PLB more potently inhibited the viability of PDFS cells. In vitro, PLB treatment was associated with downregulation of the Nrf2/FTH1 pathway, accompanied by increased lipid peroxidation and labile iron pool (LIP) accumulation. Furthermore, PLB treatment led to the upregulation of NCOA4 and an increase in intracellular free iron, consistent with ferritinophagy activation. These effects collectively point to the induction of ferroptosis. Conclusion: This study demonstrates that PLB may induce ferroptosis in PDFS cells. The mechanism involves suppression of the Nrf2/FTH1 pathway, subsequent upregulation of NCOA4, and ferritinophagy-driven iron overload. These results identify PLB as a potential ferroptosis inducer and warrant further investigation into its therapeutic potential for NFPA. The image shows a biochemical pathway illustrating ferritinophagy and ferroptosis. Iron in the form of Fe superscript 3 is transported by transferrin into the cell, where it is stored in ferritin. Ferritin is directed to the autolysosome, facilitated by Lamp2, where iron is released as Fe superscript 2. NCOA4 and LC3 are involved in ferritinophagy, leading to the labile iron pool. The Fenton reaction produces reactive oxygen species, contributing to ferroptosis. Nrf2 regulates FTH1, with arrows indicating promotion and inhibition. Plumbagin is shown affecting the pathway, with an illustration of a mouse receiving an injection, highlighting the interaction between NCOA4, FTH1 and Nrf2 in the brain. Arrows indicate promotion and inhibition effects within the pathway.Diagram of ferritinophagy and ferroptosis: iron regulation, NCOA4, FTH1, Nrf2 and plumbagin effects.