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Research ArticleClinical Investigation

Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices

Ping Wu, Yu Zhao, Jianjun Wu, Matthias Brendel, Jiaying Lu, Jingjie Ge, Alexander Bernhardt, Ling Li, Ian Alberts, Sabrina Katzdobler, Igor Yakushev, Jimin Hong, Qian Xu, Yimin Sun, Fengtao Liu, Johannes Levin, Günter U. Höglinger, Claudio Bassetti, Yihui Guan, Wolfgang H. Oertel, Wolfgang Weber, Axel Rominger, Jian Wang, Chuantao Zuo and Kuangyu Shi
Journal of Nuclear Medicine November 2022, 63 (11) 1741-1747; DOI: https://doi.org/10.2967/jnumed.121.263029
Ping Wu
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
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Yu Zhao
3Department of Nuclear Medicine, University of Bern, Bern, Switzerland;
4AI Lab, Tencent, Shenzhen, China;
5Department of Informatics, Technische Universität München, Munich, Germany;
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Jianjun Wu
6Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China;
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Matthias Brendel
7Department of Nuclear Medicine, University of Munich, Munich, Germany;
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Jiaying Lu
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
3Department of Nuclear Medicine, University of Bern, Bern, Switzerland;
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Jingjie Ge
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
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Alexander Bernhardt
8Department of Neurology, University of Munich, Munich, Germany;
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Ling Li
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
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Ian Alberts
3Department of Nuclear Medicine, University of Bern, Bern, Switzerland;
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Sabrina Katzdobler
8Department of Neurology, University of Munich, Munich, Germany;
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Igor Yakushev
9Department of Nuclear Medicine, Technische Universität München, Munich, Germany;
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Jimin Hong
3Department of Nuclear Medicine, University of Bern, Bern, Switzerland;
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Qian Xu
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
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Yimin Sun
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
6Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China;
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Fengtao Liu
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
6Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China;
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Johannes Levin
8Department of Neurology, University of Munich, Munich, Germany;
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Günter U. Höglinger
10Department of Neurology, Hannover Medical School, Hannover, Germany;
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Claudio Bassetti
11Department of Neurology, University of Bern, Bern, Switzerland; and
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Yihui Guan
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
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Wolfgang H. Oertel
12Department of Neurology, University of Marburg, Marburg, Germany
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Wolfgang Weber
9Department of Nuclear Medicine, Technische Universität München, Munich, Germany;
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Axel Rominger
3Department of Nuclear Medicine, University of Bern, Bern, Switzerland;
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Jian Wang
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
6Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China;
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Chuantao Zuo
1PET Center, Huashan Hospital, Fudan University, Shanghai, China;
2National Research Center for Aging and Medicine & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China;
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Kuangyu Shi
3Department of Nuclear Medicine, University of Bern, Bern, Switzerland;
5Department of Informatics, Technische Universität München, Munich, Germany;
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  • FIGURE 1.
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    FIGURE 1.

    Study profile, showing a Chinese cohort (Huashan parkinsonian PET imaging dataset) and a German cohort. Clinically definite diagnoses = diagnoses by clinical experts after return visit but without a formal clinical follow-up; clinically confirmative diagnoses = diagnoses resulting from at least 1 formal clinical follow-up over 1 y after PET imaging.

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    FIGURE 2.

    Accuracy of DMI indices in development phase in training cohort and blind-test phase on both Chinese and German test cohorts. Results in cross-validation were plotted using receiver-operating-characteristic curves. Results in Chinese blind-test cohort were illustrated as single points, where overall represents results of all tested 330 patients. One hundred eight patients in blind test underwent follow-up scanning, and performance of these at baseline and follow-up was plotted. Blind-test results in German cohort (90 patients) are also included and denoted with black rectangle for easy comparison.

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    FIGURE 3.

    3D plot of probabilities of IPD, MSA, and PSP of DMI indices in training cohort. (A) Patients with short symptom duration (≤2 y). (B) Patients with long symptom duration (>2 y).

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    FIGURE 4.

    Comparison of probabilities of IPD (A), MSA (B), and PSP (C) of DMI indices on the 108 patients in blind-test cohort with repeated PET scans. (Left column) Comparison of the probability of DMI indices extracted from baseline and follow-up PET for individuals. (Right column) Violin plots demonstrate statistical distribution of probabilities of DMI indices. *P ≤ 0.05. **P ≤ 0.01. ns = no significance.

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    TABLE 1.

    Demographic and Clinical Data

    Clinical parametersHuashan parkinsonian PET imaging dataset (Chinese cohort)German cohort
    Pretraining cohortTraining cohortBlind-test cohort
    OverallShort symptom duration (≤2 y)Long symptom duration (>2 y)OverallBaselineFollow-up
    IPD*
     Patient (n)241299136163211666634
     Sex (male/female)154/87166/13373/6393/70130/8143/2343/2321/13 (34/34)
     Age at PET (y)50.0 ± 15.560.2 ± 8.559.1 ± 9.061.0 ± 8.060.0 ± 7.660.0 ± 7.962.1 ± 7.972.9 ± 9.5 (34/34)
     Symptom duration at PET (mo)–45.3 ± 46.013.0 ± 5.972.3 ± 47.439.0 ± 41.326.0 ± 24.153.4 ± 24.244.5 ± 32.9 (18/34)
     Hoehn and Yahr stage†–2.2 ± 1.01.7 ± 0.62.7 ± 1.01.9 ± 0.91.6 ± 0.71.9 ± 0.61.6 ± 0.8 (22/34)
     UPDRS III–27.0 ± 14.318.9 ± 8.933.8 ± 14.522.8 ± 12.119.6 ± 9.124.2 ± 10.112.0 ± 3.6 (3/34)
    Clinical follow-up (mo)––––46.8 ± 30.4–64.5 ± 25.319.1 ± 21.8 (14/34)
    MSA*
     Patient (n) (MSA-C/MSA-P)79150 (57/93)90 (39/51)60 (18/42)61 (21/40)22 (8/14)22 (8/14)17 (8/8/1)
     Sex (male/female)42/3778/7247/4331/2932/2914/814/810/7 (17/17)
     Age at PET (y)57.5 ± 10.657.8 ± 8.056.5 ± 8.159.6 ± 7.458.5 ± 6.358.3 ± 7.460.3 ± 7.361.3 ± 8.3 (17/17)
     Symptom duration at PET (mo)–24.3 ± 17.113.9 ± 6.039.9 ± 16.527.0 ± 20.122.1 ± 11.845.6 ± 12.530.0 ± 22.2 (17/17)
     Hoehn and Yahr stage†–3.1 ± 0.83.0 ± 0.83.5 ± 0.72.9 ± 0.82.6 ± 0.63.4 ± 0.82.4 ± 1.1 (15/17)
     UPDRS III–30.6 ± 14.525.9 ± 12.437.6 ± 14.729.3 ± 14.423.5 ± 8.236.4 ± 11.134.6 ± 12.8 (11/17)
     Clinical follow-up (mo)––––30.7 ± 18.2–41.7 ± 16.422.6 ± 22.4 (17/17)
    PSP*
     Patient (n)7898346458202039
     Sex (male/female)45/3360/3823/1137/2739/1917/317/321/18 (39/39)
     Age at PET (y)64.6 ± 8.667.2 ± 8.065.0 ± 9.368.5 ± 6.965.1 ± 6.664.8 ± 7.567.0 ± 7.270.0 ± 7.1 (39/39)
     Symptom duration at PET (mo)–35.0 ± 20.715.3 ± 5.445.5 ± 18.034.1 ± 22.732.4 ± 22.058.8 ± 22.822.4 ± 15.7 (37/39)
     Hoehn and Yahr stage†–3.2 ± 0.82.9 ± 0.63.4 ± 0.83.0 ± 0.82.7 ± 1.03.6 ± 0.82.6 ± 1.1 (37/39)
     UPDRS III–30.1 ± 13.528.0 ± 11.031.2 ± 14.626.8 ± 11.023.0 ± 10.434.6 ± 15.937.0 ± 15.9 (20/39)
     Clinical follow-up (mo)––––25.1 ± 15.7–37.5 ± 12.922.2 ± 13.8 (17/39)
    • ↵* Diagnosis information: Supplemental Table 1.

    • ↵† Detailed Hoehn and Yahr stage information: Supplemental Table 2.

    • Data are shown as mean ± SD. In German cohort, associated numbers of subjects with these items are provided together with statistics information (subject number with certain item/total subject number).

    • UPDRS = Unified Parkinson’s Disease Rating Scale; MSA-C/MSA-P = MSA-cerebellar/MSA-parkinsonian.

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    TABLE 2.

    Accuracy of DMI Indices in Cross-Validation on Training Cohort

    DiagnosisMetricsOverallShort symptom duration (≤2 y)Long symptom duration (>2 y)
    IPDAUC0.986 (0.977–0.996)0.981 (0.965–0.997)0.991 (0.981–1.000)
    Sensitivity95.7% (92.7%–97.7%)94.9% (89.7%–97.9%)95.7% (91.4%–98.3%)
    Specificity97.6% (94.8%–99.1%)97.6% (93.1%–99.5%)98.4% (94.3%–99.8%)
    PPV97.9% (95.6%–98.9%)97.7% (93.5%–99.1%)98.7% (95.5%–99.5%)
    NPV94.9% (91.5%–98.1%)94.5% (89.1%–98.8%)94.6% (89.2%–99.3%)
    MSAAUC0.997 (0.994–1.000)0.996 (0.988–1.000)0.998 (0.995–1.000)
    Sensitivity97.3% (93.3%–99.3%)100% (96.0%–100%)98.3% (91.1%–100%)
    Specificity99.5% (98.2%–99.9%)98.2% (94.9%–99.6%)99.6% (97.6%–100%)
    PPV98.6% (95.3%–99.6%)96.8% (91.0%–100%)98.3% (91.3%–100%)
    NPV99.0% (97.4%–99.9%)100% (97.8%–100%)99.6% (97.5%–100%)
    PSPAUC0.982 (0.965–0.998)0.968 (0.925–1.000)0.990 (0.980–1.000)
    Sensitivity91.8% (84.5%–96.4%)88.2% (72.5%–96.7%)93.8% (84.8%–98.3%)
    Specificity98.2% (96.5%–99.2%)98.2% (95.5%–99.5%)98.2% (95.5%–99.5%)
    PPV91.8% (85.0%–96.4%)88.2% (74.3%–96.7%)93.7% (85.2%–98.3%)
    NPV98.2% (96.4%–99.2%)98.2% (95.1%–99.5%)98.2% (95.3%–99.5%)
    • AUC = area under the curve.

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    TABLE 3.

    Accuracy of DMI Indices on Blind-Test Cohort from Huashan Parkinsonian PET Imaging Dataset (Chinese Cohort) and German Cohort

    DiagnosisMetricsHuashan parkinsonian PET imaging dataset (Chinese cohort)German cohort
    OverallBaselineFollow-up
    IPDSensitivity98.1%98.5%95.5%94.1%
    Specificity90.0%88.1%97.6%84.0%
    PPV94.5%92.9%98.4%78.0%
    NPV96.4%97.4%93.2%95.9%
    MSASensitivity88.5%81.8%95.4%82.4%
    Specificity99.2%99.9%98.8%99.9%
    PPV96.4%99.9%95.5%99.9%
    NPV97.4%95.6%98.8%96.1%
    PSPSensitivity84.5%90.0%95.0%82.1%
    Specificity97.8%97.7%96.6%94.1%
    PPV89.1%90.0%86.4%91.4%
    NPV97.0%97.7%98.8%87.3%

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Journal of Nuclear Medicine: 63 (11)
Journal of Nuclear Medicine
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November 1, 2022
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Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices
Ping Wu, Yu Zhao, Jianjun Wu, Matthias Brendel, Jiaying Lu, Jingjie Ge, Alexander Bernhardt, Ling Li, Ian Alberts, Sabrina Katzdobler, Igor Yakushev, Jimin Hong, Qian Xu, Yimin Sun, Fengtao Liu, Johannes Levin, Günter U. Höglinger, Claudio Bassetti, Yihui Guan, Wolfgang H. Oertel, Wolfgang Weber, Axel Rominger, Jian Wang, Chuantao Zuo, Kuangyu Shi
Journal of Nuclear Medicine Nov 2022, 63 (11) 1741-1747; DOI: 10.2967/jnumed.121.263029

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Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices
Ping Wu, Yu Zhao, Jianjun Wu, Matthias Brendel, Jiaying Lu, Jingjie Ge, Alexander Bernhardt, Ling Li, Ian Alberts, Sabrina Katzdobler, Igor Yakushev, Jimin Hong, Qian Xu, Yimin Sun, Fengtao Liu, Johannes Levin, Günter U. Höglinger, Claudio Bassetti, Yihui Guan, Wolfgang H. Oertel, Wolfgang Weber, Axel Rominger, Jian Wang, Chuantao Zuo, Kuangyu Shi
Journal of Nuclear Medicine Nov 2022, 63 (11) 1741-1747; DOI: 10.2967/jnumed.121.263029
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