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@ARTICLE{Li:285004,
      author       = {Li, Chenyang and Jiao, Fangyang and Wu, Shaoyou and Wang,
                      Chenhan and Wei, Min and Zhang, Shuoyan and Wang, Luyao and
                      Huang, Yu and Yin, Yafu and Tian, Rong and Bernhardt,
                      Alexander and Katzdobler, Sabrina and Levin, Johannes and
                      Höglinger, Günter U and Brendel, Matthias and Rominger,
                      Axel and Shi, Kuangyu and Zuo, Chuantao and Jiang, Jiehui},
      title        = {{E}nhancing interpretability of {AI} with radiomics-based
                      deep neural network: proof of concept in the classification
                      of {P}arkinsonian syndromes with 18{F}-{FDG} {PET} imaging.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {53},
      number       = {3},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DZNE-2026-00138},
      pages        = {1962 - 1979},
      year         = {2026},
      abstract     = {Interpretability and reproducibility remain major
                      challenges in applying deep neural network (DNN) to
                      neuroimaging-based diagnosis. This study proposes a
                      radiomics-guided dual-channel deep neural network (RDDNN) to
                      improve feature transparency and enhance clinical
                      understanding in the classification of Parkinsonian
                      syndromes.In this bi-centric study, we analysed two
                      independent cohorts comprising 1,275 patients with
                      idiopathic Parkinson's disease (IPD), multiple system
                      atrophy (MSA), and progressive supranuclear palsy (PSP),
                      alongside 223 healthy controls from Huashan Hospital and 90
                      patients with IPD, MSA, and PSP (34IPD, 17MSA, 39PSP) from
                      the University Hospital Munich. It is a re-analysis of
                      well-studied Chinese and German cohorts of
                      18F-fluorodeoxyglucose Positron emission tomography
                      (FDG-PET) imaging of parkinsonian patients and the FDG scans
                      were of 10-min static acquisition at 60 min post FDG
                      injection and normalized against whole brain activity. The
                      RDDNN model combines local features extracted via dilated
                      convolutional networks and global features derived from
                      Transformer-based self-attention networks. Model performance
                      was evaluated using classification metrics and compared to
                      radiomics and DNN approaches. The model's outputs were also
                      compared with nuclear medicine specialists' visual
                      assessments to assess interpretability and time efficiency.
                      Furthermore, SHapley Additive Explanations (SHAP),
                      Layer-wise Class Activation Mapping (Layer-CAM), and Rollout
                      Attention Map (RAM) were employed to evaluate which features
                      played the most critical roles in the model's final
                      classification decisions after supervised training, and to
                      examine how both networks spatially corresponded to known
                      brain connectivity regions.In the internal blind-test
                      cohort, the RDDNN achieved high accuracy (AUC = 0.99,
                      accuracy = 0.98). SHAP and correlation analyses jointly
                      indicated complementary information across channels, some of
                      which were clinically interpretable. In the external cohort,
                      the model maintained robust performance (AUC = 0.94,
                      accuracy = 0.81), with consistent feature patterns across
                      populations. The model significantly reduced evaluation time
                      compared to nuclear medicine specialists' readings (p <
                      0.001), and the heatmaps showed disease-specific activation
                      in anatomically relevant regions for IPD, MSA, and PSP.The
                      RDDNN framework provides a clinically interpretable and
                      reproducible DNN solution for classifying Parkinsonian
                      disorders. By integrating radiomics and attention-based
                      modeling, it enhances lesion localization, supports clinical
                      decision-making, and offers performance comparable to human
                      specialists-while substantially improving diagnostic
                      efficiency.},
      keywords     = {Humans / Fluorodeoxyglucose F18 / Parkinsonian Disorders:
                      diagnostic imaging / Parkinsonian Disorders: classification
                      / Positron-Emission Tomography / Male / Female / Neural
                      Networks, Computer / Middle Aged / Aged / Proof of Concept
                      Study / Deep Learning / Image Processing, Computer-Assisted:
                      methods / Radiomics / 18F-fluorodeoxyglucosePET imaging
                      (Other) / Dual-channel neural network (Other) / Model
                      interpretability (Other) / Parkinsonian syndromes (Other) /
                      Radiomics (Other) / SHAP (Other) / Fluorodeoxyglucose F18
                      (NLM Chemicals)},
      cin          = {Clinical Research (Munich) / AG Levin / AG Haass},
      ddc          = {610},
      cid          = {I:(DE-2719)1111015 / I:(DE-2719)1111016 /
                      I:(DE-2719)1110007},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 352 -
                      Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40932612},
      doi          = {10.1007/s00259-025-07478-7},
      url          = {https://pub.dzne.de/record/285004},
}