Evidence map›Paper›PMID 40844547›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study).

Luca Filippi, Francesco Bianconi, Cristina Ferrari, Flavia Linguanti, Claudia Battisti, Nicoletta Urbano, Matteo Minestrini, Salvatore Gerardo Messina, Lisa Buci, Alfonso Baldoncini and 3 more

Abstract readComparative StudyMulticenter Study
PubMed Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Luca FilippiDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier, 1, Rome, 00133, Italy. luca.filippi@uniroma2.it.ORCID 0000-0003-4423-5496
Francesco BianconiDepartment of Engineering, Università degli Studi di Perugia, Perugia, Italy.
Cristina FerrariNuclear Medicine Unit, Interdisciplinary Department of Medicine (DIM), University of Bari"Aldo Moro", Bari, Italy.
Flavia LinguantiNuclear Medicine Department, Ospedale San Donato, Arezzo, Italy.
Claudia BattistiNuclear Medicine Unit, Interdisciplinary Department of Medicine (DIM), University of Bari"Aldo Moro", Bari, Italy.
Nicoletta UrbanoNuclear Medicine Unit, Department of Oncohaematology, Fondazione PTV Policlinico Tor Vergata University Hospital, Rome, Italy.
Matteo MinestriniNuclear Medicine Division, Azienda Ospedaliera di Perugia, Perugia, Italy.
Salvatore Gerardo MessinaNuclear Medicine Division, Azienda Ospedaliera di Perugia, Perugia, Italy.
Lisa BuciEndocrinology Unit, Careggi Hospital, Florence, Italy.
Alfonso BaldonciniNuclear Medicine Department, Ospedale San Donato, Arezzo, Italy.
Giuseppe RubiniNuclear Medicine Unit, Interdisciplinary Department of Medicine (DIM), University of Bari"Aldo Moro", Bari, Italy.
Orazio SchillaciDepartment of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier, 1, Rome, 00133, Italy.
Barbara PalumboSection of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Perugia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo compare PET-derived metrics between digital and analogue PET/CT in hyperparathyroidism, and to assess whether machine learning (ML) applied to quantitative PET parameters can distinguish parathyroid adenoma (PA) from hyperplasia (PH).

methodsFrom an initial multi-centre cohort of 179 patients, 86 were included, comprising 89 PET-positive lesions confirmed histologically (74 PA, 15 PH). Quantitative PET parameters-maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV), target-to-background ratio (TBR), and maximum diameter-along with serum PTH and calcium levels, were compared between digital and analogue PET scanners using the Mann-Whitney U test. Receiver operating characteristic (ROC) analysis identified optimal threshold values. ML models (LASSO, decision tree, Gaussian naïve Bayes) were trained on harmonised quantitative features to distinguish PA from PH.

resultsDigital PET detected significantly smaller lesions than analogue PET, in both metabolic volume (1.32 ± 1.39 vs. 2.36 ± 2.01 cc; p < 0.001) and maximum diameter (8.35 ± 4.32 vs. 11.87 ± 5.29 mm; p < 0.001). PA lesions showed significantly higher SUVmax and TBR compared to PH (SUVmax: 8.58 ± 3.70 vs. 5.27 ± 2.34; TBR: 14.67 ± 6.99 vs. 8.82 ± 5.90; both p < 0.001). The optimal thresholds for identifying PA were SUVmax > 5.89 and TBR > 11.5. The best ML model (LASSO) achieved an AUC of 0.811, with 79.7% accuracy and balanced sensitivity and specificity.

conclusionsDigital PET outperforms analogue system in detecting small parathyroid lesions. Additionally, ML analysis of PET-derived metrics and PTH may support non-invasive distinction between adenoma and hyperplasia.

Indexed as

Machine LearningParathyroid GlandsParathyroid NeoplasmsPositron Emission Tomography Computed TomographyAdenomaAdultAgedFemaleHumansHyperplasiaImage Processing, Computer-AssistedMaleMiddle Aged18F-cholineArtificial intelligenceAutomated classificationDigital PET/CTHyperparathyroidismMetabolic imaging

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.