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PreSight: Preoperative Outcome Prediction for Parkinson's Disease via Region-Prior Morphometry and Patient-Specific Weighting

Yand Wang, Chen Zhang, Lanyun Zhu, Yixin Chen, Qunbo Wang, Yutong Bai, Jurgen Germann, Yinghong Wen, Shuai Shao

TL;DR

It is demonstrated that integrating clinical prior knowledge with region-adaptive morphometry enables reliable presurgical decision support in routine practice and produces end-to-end, calibrated, decision-ready predictions with patient-level explanations.

Abstract

Preoperative improvement rate prediction for Parkinson's disease surgery is clinically important yet difficult because imaging signals are subtle and patients are heterogeneous. We address this setting, where only information available before surgery is used, and the goal is to predict patient-specific postoperative motor benefit. We present PreSight, a presurgical outcome model that fuses clinical priors with preoperative MRI and deformation-based morphometry (DBM) and adapts regional importance through a patient-specific weighting module. The model produces end-to-end, calibrated, decision-ready predictions with patient-level explanations. We evaluate PreSight on a real-world two-center cohort of 400 subjects with multimodal presurgical inputs and postoperative improvement labels. PreSight outperforms strong clinical, imaging-only, and multimodal baselines. It attains 88.89% accuracy on internal validation and 85.29% on an external-center test for responder classification and shows better probability calibration and higher decision-curve net benefit. Ablations and analyses confirm the contribution of DBM and the patient-specific weighting module and indicate that the model emphasizes disease-relevant regions in a patient-specific manner. These results demonstrate that integrating clinical prior knowledge with region-adaptive morphometry enables reliable presurgical decision support in routine practice.

PreSight: Preoperative Outcome Prediction for Parkinson's Disease via Region-Prior Morphometry and Patient-Specific Weighting

TL;DR

It is demonstrated that integrating clinical prior knowledge with region-adaptive morphometry enables reliable presurgical decision support in routine practice and produces end-to-end, calibrated, decision-ready predictions with patient-level explanations.

Abstract

Preoperative improvement rate prediction for Parkinson's disease surgery is clinically important yet difficult because imaging signals are subtle and patients are heterogeneous. We address this setting, where only information available before surgery is used, and the goal is to predict patient-specific postoperative motor benefit. We present PreSight, a presurgical outcome model that fuses clinical priors with preoperative MRI and deformation-based morphometry (DBM) and adapts regional importance through a patient-specific weighting module. The model produces end-to-end, calibrated, decision-ready predictions with patient-level explanations. We evaluate PreSight on a real-world two-center cohort of 400 subjects with multimodal presurgical inputs and postoperative improvement labels. PreSight outperforms strong clinical, imaging-only, and multimodal baselines. It attains 88.89% accuracy on internal validation and 85.29% on an external-center test for responder classification and shows better probability calibration and higher decision-curve net benefit. Ablations and analyses confirm the contribution of DBM and the patient-specific weighting module and indicate that the model emphasizes disease-relevant regions in a patient-specific manner. These results demonstrate that integrating clinical prior knowledge with region-adaptive morphometry enables reliable presurgical decision support in routine practice.
Paper Structure (6 sections, 13 equations, 2 figures, 2 tables)

This paper contains 6 sections, 13 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Experiments
  4. Dataset and Preprocessing
  5. Comparison Results and Analysis
  6. Conclusion

Figures (2)

  • Figure 1: Overview of PreSight. Given preoperative MRI and clinical records, PreSight predicts postoperative DBS responsiveness in PD. Clinical variables are converted into text prompts and encoded by a large language model into patient-specific embeddings, which modulate region-prior morphometric maps via a Patient-Specific Weighting Module. The resulting weighted deformation-based morphometry (DBM) features are fed into a classifier to determine responders and non-responders.
  • Figure 2: Multi-view visualization of parcellation and region-wise weights (axial, sagittal, coronal). Columns show MRI, atlas parcellation, region-wise weighting (opacity $\propto$ weight), and DBM visualizations (DBM, DBM+parcellation, DBM+weighting).