Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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The Journal of clinical endocrinology and metabolism
Efficacy and Safety of Once-Weekly Lonapegsomatropin in Adults With Growth Hormone Deficiency: foresiGHt Trial Results
CONTEXT: Adult growth hormone (GH) deficiency (GHD) is characterized by metabolic abnormalities caused by insufficient GH production. Lonapegsomatropin, a prodrug administered once weekly, was designed to provide sustained release of unmodified somatropin to reduce the burden of daily somatropin injections. OBJECTIVE: This work aimed to evaluate the efficacy and safety of lonapegsomatropin vs placebo as treatment for adults with GHD. METHODS: The foresiGHt trial was a multicenter, randomized, parallel-arm, placebo-controlled (double-blind) and active-controlled (open-label) trial (NCT04615273) conducted at 116 centers in North America, Europe, and Asia-Pacific. The trial randomly assigned and dosed 259 adults with GHD. Participants were randomly assigned 1:1:1 to receive once-weekly lonapegsomatropin, once-weekly placebo, or daily somatropin for 38 weeks. The primary efficacy end point was change from baseline in trunk percent fat at week 38. Secondary efficacy end points included change from baseline in trunk fat mass and total body lean mass. RESULTS: At week 38, lonapegsomatropin significantly reduced trunk percent fat (-1.68% vs +0.37%; least squares [LS] mean difference -2.04%; P < .001), increased total body lean mass (+1.60 kg vs -0.11 kg; LS mean difference 1.70 kg; P < .0001), and reduced trunk fat mass (-0.48 kg vs +0.22 kg; LS mean difference -0.70 kg; P = .0053) vs placebo. The safety and tolerability profile of lonapegsomatropin was comparable to somatropin. CONCLUSION: The foresiGHt trial met its primary efficacy end point by demonstrating superiority of lonapegsomatropin vs placebo with similar safety and tolerability, supporting its potential as a once-weekly treatment option for adults with GHD.
19 May 2026
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Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning
Pituitary neuroendocrine tumors (PitNETs) are common sellar neoplasms and represent a major component of routine pituitary pathology. In the 2022 World Health Organization (WHO) Classification, transcription factor-defined lineage assignment is central to diagnosis. However, lineage-related morphologic information on routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) has not been systematically characterized. We developed an attention-guided graph neural network to predict PitNET lineage directly from H&E-stained WSIs and to identify regions prioritized for classification. Consecutive patients who underwent surgical resection for PitNETs at Beijing Tiantan Hospital between 2021 and 2025 were included. In the internal hold-out set, the five fold-specific models achieved a mean F1-score of 92.78% and a mean balanced accuracy of 94.84%. In a temporally independent validation cohort, the final model achieved an F1-score of 87.64% and a balanced accuracy of 89.48%. Performance varied across lineages and histomorphologic subtypes, and the most common error pattern was misassignment of PIT-1 and T-PIT tumors to SF-1. Attention maps predominantly highlighted tumor-rich regions. Quantitative cell-level morphometry supported lineage-associated patterns, including larger cell and nuclear sizes in PIT-1 tumors, more elongated nuclei in SF-1 tumors, and higher cellular density with reduced intercellular spacing in T-PIT tumors. In six cases with multiple synchronous PitNETs of distinct lineages, patch-level prediction maps corresponded closely to transcription factor immunohistochemistry. In a small exploratory subset with available DNA methylation data, methylation classifier results were concordant with model predictions in 11 of 15 cases and with routine histopathologic diagnoses in 8 of 15 cases. These findings indicate that routine H&E-stained WSIs contain learnable morphologic information related to PitNET lineage. Attention-guided spatial modeling provides an interpretable framework for characterizing lineage-associated patterns in PitNETs.
12 May 2026
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