Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting.
Diagnostic accuracy in FDG-PET imaging highly depends on the operating procedures. In this clinical study on dementia, we compared the diagnostic accuracy at a single-subject level of a) Clinical Scenarios, b) Standard FDG Images and c) Statistical Parametrical (SPM) Maps generated via a new optimized SPM procedure. We evaluated the added value of FDG-PET, either Standard FDG Images or SPM Maps, to Clinical Scenarios. In 88 patients with neurodegenerative diseases (Alzheimer's Disease-AD, Frontotemporal Lobar Degeneration-FTLD, Dementia with Lewy bodies-DLB and Mild Cognitive Impairment-MCI), 9 neuroimaging experts made a forced diagnostic decision on the basis of the evaluation of the three types of information. There was also the possibility of a decision of normality on the FDG-PET images. The clinical diagnosis confirmed at a long-term follow-up was used as the gold standard. SPM Maps showed higher sensitivity and specificity (96% and 84%), and better diagnostic positive (6.8) and negative (0.05) likelihood ratios compared to Clinical Scenarios and Standard FDG Images. SPM Maps increased diagnostic accuracy for differential diagnosis (AD vs. FTD; beta 1.414, p = 0.019). The AUC of the ROC curve was 0.67 for SPM Maps, 0.57 for Clinical Scenarios and 0.50 for Standard FDG Images. In the MCI group, SPM Maps showed the highest predictive prognostic value (mean LOC = 2.46), by identifying either normal brain metabolism (exclusionary role) or hypometabolic patterns typical of different neurodegenerative conditions.
Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy ; Vita-Salute San Raffaele University, Milan, Italy ; Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy ; Istituto di Bioimmagini e Fisiologia Molecolare, CNR, Segrate, Italy.
Daniela
D
Perani
Istituto di Bioimmagini e Fisiologia Molecolare, CNR, Segrate, Italy.
Pasquale Anthony
PA
Della Rosa
Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy ; Clinical Neurosciences Department, San Raffaele Hospital, Milan, Italy.
Chiara
C
Cerami
Istituto di Bioimmagini e Fisiologia Molecolare, CNR, Segrate, Italy.
Francesca
F
Gallivanone
Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.
Federico
F
Fallanca
Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.
Emilia Giovanna
EG
Vanoli
Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.
Andrea
A
Panzacchi
Dept of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy.
Flavio
F
Nobili
Institute of Biostructure and Bioimaging, CNR, Naples, Italy.
Sabina
S
Pappatà
Dept of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy.
Alessandra
A
Marcone
Department of Medical Imaging, Geneva University, Geneva, Switzerland.
Valentina
V
Garibotto
Istituto di Bioimmagini e Fisiologia Molecolare, CNR, Segrate, Italy.
Isabella
I
Castiglioni
Neurology Department, San Raffaele Hospital, Milan, Italy.
Giuseppe
G
Magnani
Clinical Neurosciences Department, San Raffaele Hospital, Milan, Italy ; Istituto Universitario degli Studi Superiori, Pavia, Italy.
Stefano F
SF
Cappa
Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy.
Luigi
L
Gianolli
D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D008297:Male; D008875:Middle Aged; D019636:Neurodegenerative Diseases / Q000175:diagnosis*; D049268:Positron-Emission Tomography / Q000379:methods*; D012680:Sensitivity and Specificity
D019788:Fluorodeoxyglucose F18
Dementia diagnosis; FDG-PET imaging; Statistical Parametrical Mapping; Voxel-based analysis