European journal of nuclear medicine and molecular imaging · 2026
Machine learning for automated differentiation of parkinson's disease and its mimics using ¹²³I-mIBG scintigraphy: insights from a multicentre real-world cohort (ITA-mIBG study).
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Filippi Luca, Bianconi Francesco, Frantellizzi Viviana, Ferrari Cristina, Marongiu Andrea, De Feo Maria Silvia, Battisti Claudia, Aghakhanyan Gayane, Gazzilli Maria, Urbano Nicoletta, Nuvoli Susanna, Volterrani Duccio, Fravolini Mario Luca, Rubini Giuseppe, De Vincentis Giuseppe, Spanu Angela, Palumbo Barbara
Аннотация
PURPOSE: To assess machine learning (ML) classifiers trained on harmonised multicentre ¹²³I-mIBG planar scintigraphy for differentiating Parkinson's disease (PD) from non-PD parkinsonian syndromes and to determine whether early imaging alone may ensure accurate discrimination.
METHODS: This retrospective study included patients with suspected PD who underwent early (~ 15 min) and delayed (~ 240 min) imaging and received a definitive diagnosis after ≥ 12 months. Harmonised region of interest (ROI) placement and ComBat correction were applied. Early and late heart-to-mediastinum (H/M) ratios and washout rate (WR) were calculated. Differences were tested by Mann-Whitney U test, and cut-points identified by ROC analysis. Logistic regression, Gaussian naïve Bayes, and support vector machine were trained on these features with Z-score normalisation and synthetic minority oversampling technique (SMOTE).
RESULTS: 127 patients were analysed (85 PD, 42 non-PD). Early and late H/M ratios were significantly lower in PD than non-PD (early H/M 1.45 ± 0.20 vs. 1.80 ± 0.20; late H/M 1.33 ± 0.22 vs. 1.68 ± 0.21; both p < 0.001). WR was modestly higher in PD (8.74 ± 5.76% vs. 6.49 ± 6.19%, p = 0.024). Optimal cut-points for PD were: early H/M ≤ 1.62 (accuracy 80.3%, sensitivity 83.3%, specificity 78.8%, and AUC 0.878), late H/M ≤ 1.52 (83.5%, 83.3%, 83.5% and 0.866) and WR ≥ 6.03% (70.1%, 70.6%, 69.0% and 0.645). ML achieved mean accuracy 78.9-80.7%, sensitivity 81.9-84.0%, specificity 68.6-78.0%, and AUC 0.850-0.875.
CONCLUSION: Classifiers trained on ¹²³I-mIBG semi-quantitative indices accurately distinguished PD from non-PD. Early H/M ratio alone provided excellent discrimination, supporting early-imaging; prospective validation is warranted.
METHODS: This retrospective study included patients with suspected PD who underwent early (~ 15 min) and delayed (~ 240 min) imaging and received a definitive diagnosis after ≥ 12 months. Harmonised region of interest (ROI) placement and ComBat correction were applied. Early and late heart-to-mediastinum (H/M) ratios and washout rate (WR) were calculated. Differences were tested by Mann-Whitney U test, and cut-points identified by ROC analysis. Logistic regression, Gaussian naïve Bayes, and support vector machine were trained on these features with Z-score normalisation and synthetic minority oversampling technique (SMOTE).
RESULTS: 127 patients were analysed (85 PD, 42 non-PD). Early and late H/M ratios were significantly lower in PD than non-PD (early H/M 1.45 ± 0.20 vs. 1.80 ± 0.20; late H/M 1.33 ± 0.22 vs. 1.68 ± 0.21; both p < 0.001). WR was modestly higher in PD (8.74 ± 5.76% vs. 6.49 ± 6.19%, p = 0.024). Optimal cut-points for PD were: early H/M ≤ 1.62 (accuracy 80.3%, sensitivity 83.3%, specificity 78.8%, and AUC 0.878), late H/M ≤ 1.52 (83.5%, 83.3%, 83.5% and 0.866) and WR ≥ 6.03% (70.1%, 70.6%, 69.0% and 0.645). ML achieved mean accuracy 78.9-80.7%, sensitivity 81.9-84.0%, specificity 68.6-78.0%, and AUC 0.850-0.875.
CONCLUSION: Classifiers trained on ¹²³I-mIBG semi-quantitative indices accurately distinguished PD from non-PD. Early H/M ratio alone provided excellent discrimination, supporting early-imaging; prospective validation is warranted.