Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson Disease.

Journal: Neurosurgery
Published Date: Sep 18, 2025

Abstract

BACKGROUND AND OBJECTIVES: Parkinson disease (PD) patients with motor complications are often considered for deep brain stimulation (DBS) surgery. Predicting symptom improvement to separate DBS responders and nonresponders remains an unmet need. Currently, DBS candidacy is evaluated using the levodopa challenge test (LCT) to confirm dopamine responsiveness and diagnosis. However, prediction of DBS success by measuring presurgical symptom improvement associated with levodopa dosage changes is highly problematic. Quantitative susceptibility mapping (QSM) is a recently developed MRI method that depicts brain iron distribution. As the substantia nigra and subthalamic nuclei are well visualized, QSM has been used in presurgical planning of DBS. Spatial features resulting from iron distribution in these nuclei have been previously linked with disease progression and motor symptom severity. Given its clear target depiction and prior findings regarding susceptibility and PD, this study demonstrates the technical feasibility of predicting DBS outcomes from presurgical QSM. METHODS: A novel presurgical QSM radiomics approach using a regression model is presented to predict DBS outcome according to spatial features in QSM deep gray nuclei. To overcome limited and noisy training data, data augmentation using label noise injection or "compensation" was used to improve outcome prediction of the regression model. The QSM radiomics model was evaluated on 67 patients with PD who underwent DBS at 2 medical centers. RESULTS: The QSM radiomics model predicted DBS improvement in the Unified Parkinson Disease Rating Scale at Center 1 and Center 2 with Pearson correlation , ( ) and , ( ), respectively. LCT failed to predict DBS improvement at Center 1 and Center 2 with Pearson correlation ( ) and ( ), respectively. CONCLUSION: QSM radiomics has potential to accurately predict DBS outcome in treating patients with PD, offering a valuable alternative to the time-consuming and low-accuracy LCT.

Authors

  • Alexandra G Roberts
    Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA.
  • Jinwei Zhang
    Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
  • Ceren Tozlu
    Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  • Dominick Romano
    Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
  • Sema Akkus
    Department of Neurosurgery, Mount Sinai Hospital, New York, New York, USA.
  • Heejong Kim
    Nokia Bell Labs, New Providence, New Jersey 07974 United States.
  • Mert R Sabuncu
    Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  • Pascal Spincemaille
    Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
  • Jianqi Li
    Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
  • Yi Wang
    Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Xi Wu
  • Brian H Kopell
    Department of Neurosurgery, Mount Sinai Hospital, New York, New York, USA.

Keywords

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