ReviewAmerican journal of nuclear medicine and molecular imaging2025
Somatostatin receptor PET-guided treatment and artificial intelligence applications in meningioma: a comprehensive review.
Review in American journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Somatostatin receptor PET in meningioma: diagnosis, therapy, and surveillance.Nuclear medicine communications · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Meningiomas are the most common primary intracranial tumors, with treatment involving resection and radiation therapy. However, therapeutic options are limited for recurrent or progressive disease, particularly in higher World Health Organization (WHO) grade tumors. Somatostatin receptor (SSTR) expression in meningiomas has opened new therapeutic opportunities as the differential SSTR2 overexpression permits molecular targeting using radiolabeled somatostatin analogs. PRRT offers promising therapeutic efficacy in select meningioma patients, with clinical responses strongly correlated to WHO tumor grade and SSTR expression levels. Combining SSTR PET imaging, to evaluate receptor density, with radiomic analysis can reveal tumor heterogeneity patterns and quantitative imaging features that can guide clinical decision-making and monitor treatment response. Integrating machine learning and artificial intelligence (AI) into clinical workflows offer novel approaches to apply quantitative SUV parameters, image texture features, and histopathologic data in order to identify patients with WHO grade II and III meningiomas at greater risk of tumor recurrence. Given the heterogeneity in imaging and treatment protocols across institutions and the limited number of PRRT-treated meningioma cohorts, future research should prioritize prospective, multicenter studies that integrate histologic and molecular imaging data to refine patient selection strategies and establish PRRT's role within personalized, precision cancer treatment paradigms.
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Registered trials
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