PT  - JOURNAL ARTICLE
AU  - Wu, Ping
AU  - Zhao, Yu
AU  - Wu, Jianjun
AU  - Brendel, Matthias
AU  - Lu, Jiaying
AU  - Ge, Jingjie
AU  - Bernhardt, Alexander
AU  - Li, Ling
AU  - Alberts, Ian
AU  - Katzdobler, Sabrina
AU  - Yakushev, Igor
AU  - Hong, Jimin
AU  - Xu, Qian
AU  - Sun, Yimin
AU  - Liu, Fengtao
AU  - Levin, Johannes
AU  - Höglinger, Günter U.
AU  - Bassetti, Claudio
AU  - Guan, Yihui
AU  - Oertel, Wolfgang H.
AU  - Weber, Wolfgang
AU  - Rominger, Axel
AU  - Wang, Jian
AU  - Zuo, Chuantao
AU  - Shi, Kuangyu
TI  - Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices
AID  - 10.2967/jnumed.121.263029
DP  - 2022 Nov 01
TA  - Journal of Nuclear Medicine
PG  - 1741--1747
VI  - 63
IP  - 11
4099  - http://jnm.snmjournals.org/content/63/11/1741.short
4100  - http://jnm.snmjournals.org/content/63/11/1741.full
SO  - J Nucl Med2022 Nov 01; 63
AB  - The clinical presentations of early idiopathic Parkinson disease (IPD) substantially overlap with those of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1,275 parkinsonian patients and 863 nonparkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3-dimensional deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices and blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8%, respectively, for the diagnosis of IPD, MSA, and PSP in the blind-test cohort. In the German cohort, they resulted in sensitivities of 94.1%, 82.4%, and 82.1%, and specificities of 84.0%, 99.9%, and 94.1%, respectively. Using the PET scans independently achieved a performance comparable to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show the potential to provide an early and accurate differential diagnosis for parkinsonism and are robust when dealing with discrepancies between populations and imaging acquisitions.