Automated Imaging Differentiation for Parkinsonism.
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Vaillancourt David E, Barmpoutis Angelos, Wu Samuel S, DeSimone Jesse C, Schauder Marissa, Chen Robin, Parrish Todd B, Wang Wei-En, Molho Eric, Morgan John C, Simon David K, Scott Burton L, Rosenthal Liana S, Gomperts Stephen N, Akhtar Rizwan S, Grimes David, De Jesus Sol, Stover Natividad, Bayram Ece, Ramirez-Zamora Adolfo, Prokop Stefan, Fang Ruogu, Slevin John T, Kanel Prabesh, Bohnen Nicolaas I, Tuite Paul, Aradi Stephen, Strafella Antonio P, Siddiqui Mustafa S, Davis Albert A, Huang Xuemei, Ostrem Jill L, Fernandez Hubert, Litvan Irene, Hauser Robert A, Pantelyat Alexander, McFarland Nikolaus R, Xie Tao, Okun Michael S, AIDP Study Group, Leader Alicia, Russell Áine, Babcock Hannah, White-Tong Karen, Hua Jun, Goodheart Anna E, Peterec Erin Colleen, Poon Cynthia, Galarce Max B, Thompson Tanya, Collier Autumn M, Cromer Candace, Putra Natt, Costello Reilly, Yilmaz Eda, Mercado Crystal, Mercado Tomas, Fessenden Amanda, Wagner Renee, Spears C Chauncey, Caswell Jacqueline L, Bryants Marina, Kuzianik Kristyn, Ahmed Youshra, Bendahan Nathaniel, Njoku Joy O, Stiebel Amy, Zahed Hengameh, Wang Sarah S, Hoang Phuong T, Seemiller Joseph, Du Guangwei
Аннотация
OBJECTIVE: To assess the discriminative performance of Automated Imaging Differentiation for Parkinsonism (AIDP) using 3-T diffusion MRI and support vector machine (SVM) learning.
DESIGN, SETTING, AND PARTICIPANTS: This was a prospective, multicenter cohort study conducted from July 2021 to January 2024 across 21 Parkinson Study Group sites (US/Canada). Included were patients with PD, MSA, and PSP with established criteria and unanimous agreement in the clinical diagnosis among 3 independent, blinded neurologists who specialize in movement disorders. Patients were assigned to a training set or an independent testing set.
EXPOSURE: MRI.
MAIN OUTCOMES AND MEASURES: Area under the receiver operating characteristic curve (AUROC) in the testing set for primary model end points of PD vs atypical parkinsonism, MSA vs PSP, PD vs MSA, and PD vs PSP. AIDP was also paired with antemortem MRI to test against postmortem neuropathology in a subset of autopsy cases.
RESULTS: A total of 316 patients were screened and 249 patients (mean [SD] age, 67.8 [7.7] years; 155 male [62.2%]) met inclusion criteria. Of these patients, 99 had PD, 53 had MSA, and 97 had PSP. A retrospective cohort of 396 patients (mean [SD] age, 65.8 [8.9] years; 234 male [59.1%]) was also included. Of these patients, 211 had PD, 98 had MSA, and 87 had PSP. Patients were assigned to the training set (78%; 104 prospective, 396 retrospective) or independent testing set, which included 145 (22%; 60 PD, 27 MSA, 58 PSP) prospective patients (mean age, 67.4 [SD 7.7] years; 95 male [65.5%]). The model was robust in differentiating PD vs atypical parkinsonism (AUROC, 0.96; 95% CI, 0.93-0.99; positive predictive value [PPV], 0.91; negative predictive value [NPV], 0.83), MSA vs PSP (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.98; NPV, 0.81), PD vs MSA (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.97; NPV, 0.97), and PD vs PSP (AUROC, 0.98; 95% CI, 0.96-1.00; PPV, 0.92; NPV, 0.98). AIDP predictions were confirmed neuropathologically in 46 of 49 brains (93.9%).
CONCLUSIONS AND RELEVANCE: This prospective multicenter cohort study of AIDP met its primary end points. Results suggest using AIDP in the diagnostic workup for common parkinsonian syndromes.