20.500.12556/RUL-168087 Multinomial logistic regression algorithm for the classification of patients with parkinsonisms Background: Accurate differential diagnosis of neurodegenerative parkinsonisms is challenging due to overlapping early symptoms and high rates of misdiagnosis. To improve the diagnostic accuracy, we developed an integrated classification algorithm using multinomial logistic regression and Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) applied to 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images. In this novel classification approach, SSM/PCA is applied to FDG-PET brain images of patients with various parkinsonisms, which are compared against the constructed undetermined images. This process involves spatial normalization of the images and dimensionality reduction via PCA. The resulting principal components are then used in a multinomial logistic regression model, which generates disease-specific topographies that can be used to classify new patients. The algorithm was trained and optimized on a cohort of patients with neurodegenerative parkinsonisms and subsequently validated on a separate cohort of patients with parkinsonisms. Results: The Area Under the Curve (AUC) values were the highest for progressive supranuclear palsy (PSP) (AUC = 0.95), followed by Parkinson’s disease (PD) (AUC = 0.93) and multiple system atrophy (MSA) (AUC = 0.90). When classifying the patients based on their calculated probability for each group, the desired tradeoff between sensitivity and specificity had to be selected. With a 99% probability threshold for classification into a disease group, 82% of PD patients, 29% of MSA patients, and 77% of PSP patients were correctly identified. Only 5% of PD, 6% of MSA and 6% of PSP patients were misclassified, whereas the remaining patients (13% of PD, 65% of MSA and 18% of PSP) are undetermined by our classification algorithm. Conclusions: Compared to existing algorithms, this approach offers comparable accuracy and reliability in diagnosing PD, MSA, and PSP with no need of healthy control images. It can also distinguish between multiple types of parkinsonisms simultaneously and offers the flexibility to easily accommodate new groups. neurology parkinsonisms brain medical imaging nevrologija parkinsonizmi možgani medicinsko slikanje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-03-28 10:16:56 2025-03-28 10:16:57 2025-03-29 04:14:36 0000-00-00 00:00:00 2025 0 0 14 str. art. no. 24 Vol. 15 Mar. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616.831 2191-219X 10.1186/s13550-025-01210-0 230595331 s13550-025-01210-0.pdf s13550-025-01210-0.pdf 1 E83E0D0C7780C9DBB333052FC9F52844 a4329306cd6fa892e4a84ccc502132b0f80b604bee0ce3566709fa767077be08 aee5b891-0bb4-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=201087 https://ejnmmires.springeropen.com/articles/10.1186/s13550-025-01210-0 0 b98a3a05-0bb4-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=201088 Fakulteta za matematiko in fiziko Medicinska fakulteta 0 0 0