ArticleEJNMMI research2024
An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-body
Article in EJNMMI research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 6 papers.
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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.
Lu-177-DOTATATE (Lutathera) in Therapy of Inoperable Pheochromocytoma/ Paraganglioma
Phase I/II Study of Lu-177-DOTATATE (Lutathera) in Combination With Olaparib in Inoperable Gastroenteropancreatico Neuroendocrine Tumors (GEP-NET)
Phase 2 Study of Bintrafusp Alfa in Recurrent/Metastatic Olfactory Neuroblastoma (BARON).
Who cites it
6 citing papers in PubMed.
- PET/CT contouring techniques in neuroendocrine neoplasms: a state-of-the-art scoping review.EJNMMI reports · 2026Review
- Artificial Intelligence-Based Approach for Automated Gonad Volume Quantification Using Magnetic Resonance Imaging in Healthy Adolescents Across Puberty.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.European radiology · 2026Article
- Precision imaging and evolving therapies in paragangliomas and pheochromocytomas: from molecular diagnostics to imaging-guided management.Insights into imaging · 2026Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence-enabled early intracranial volumetric response predicts systemic progression-free survival in the phase III CROWN study.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSomatostatin receptor (SSR) targeting radiotracer
resultsThe nnUNet AI model achieved an average 5-fold validation dice similarity coefficient of 0.84 at the scan level. The model achieved dice similarity coefficients (DSC) of 0.88, 0.6, and 0.67 at the scan level, the sensitivity of 86%, 61.13%, and 61.64%, and a positive predictive value of 89%, 74%, and 86.54% at the lesion level for the PPGL test, NET and ONB cohorts, respectively. For PPGL cohorts, smaller lesions with low uptake were missed by the AI model (p < 0.001). Anatomical region-based failure analysis showed most of the false negative and false positive lesions within the liver for all the cohorts, mainly due to the high physiologic liver background activity and image noise on
conclusionsThe developed deep learning-based AI model showed reliable performance for automated segmentation of metastatic PPGL lesions on whole-body CLINICALTRIALS: gov/study/NCT03206060 , https://www. CLINICALTRIALS: gov/study/NCT04086485 , https://www. CLINICALTRIALS: gov/study/NCT05012098 .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.