Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Pituitary
The hook effect in macroprolactinomas: tumor size thresholds, prolactin patterns, and clinical consequences - a systematic review.
PURPOSE: The immunoassay "hook effect" may cause spuriously low prolactin (PRL) measurements in patients with macroprolactinomas, leading to misdiagnosis and inappropriate management. This systematic review aimed to determine the tumor size threshold and distribution at which the hook effect has been documented, characterize the pre-dilution PRL pattern, quantify the magnitude of PRL underestimation, and assess the clinical consequences of unrecognized hook effect. METHODS: PubMed, Embase, Scopus, Google Scholar, and Web of Science were searched for articles published between January 2000 and August 2025. Studies reporting individual patient data with confirmed hook effect (pre- and post-dilution PRL, tumor size, and confirmation by serial dilution) were included. Quality was assessed using the JBI Critical Appraisal Checklist for Case Reports. Data were synthesized using individual patient data (IPD) quantitative methods. The review was registered on PROSPERO (CRD420261375863) and reported per PRISMA 2020 guidelines. RESULTS: Twenty-three patients from 14 studies across 9 countries were included, with a median maximum tumor diameter of 48.0 mm (IQR 35.5-55.0; range 22-100 mm); the smallest confirmed hook-effect tumor diameter measured 22 mm, reported in two independent cases, and 9 of 23 cases (39.1%) occurred in tumors below the conventional giant adenoma threshold (≥ 40 mm). Median pre-dilution PRL was 100.0 ng/mL (IQR 62.0-164.5), while median post-dilution PRL was 6,460.0 ng/mL (IQR 3,608.5-16,657.5), yielding a median fold increase of 64.7× (IQR 46.8-141.5). Pre-dilution PRL ranged from 26 to 200 ng/mL in 82.6% of cases (95% CI: 62.9-93.0%), values often misattributed to stalk effect. Unrecognized hook effect led to unnecessary surgery in 55.6% of cases with documented clinical outcomes. The hook effect was documented across multiple immunoassay platforms. CONCLUSIONS: Pre-dilution PRL values often overlap with the range attributed to stalk effect, and serial dilution testing should be considered for pituitary macroadenoma with PRL disproportionately low relative to tumor size, regardless of whether the tumor meets the giant adenoma threshold to prevent misdiagnosis, unnecessary surgical intervention and delayed initiation of dopamine agonist therapy.
30 May 2026
Read appraisal →Endocrine pathology
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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