Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study).
Purpose: To 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). Methods: From an initial multi-centre cohort of 179 p...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 53; no. 3; pp. 2122 - 2131 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191291319&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191291319 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Feb2026 vid: 53 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191291319 187454649 10.1007/s00259-025-07508-4 191291319 ppf: 2122 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study). aug: au: Filippi, Luca Bianconi, Francesco Ferrari, Cristina Linguanti, Flavia Battisti, Claudia Urbano, Nicoletta Minestrini, Matteo Messina, Salvatore Gerardo Buci, Lisa Baldoncini, Alfonso Rubini, Giuseppe Schillaci, Orazio Palumbo, Barbara affil: https://ror.org/02p77k626 Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier, 1, 00133, Rome, Italy sug: ab: Purpose: To 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). Methods: From 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. Results: Digital 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. Conclusions: Digital 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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