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Purpose <p>To evaluate whether semiquantitative striatal [¹²³I]FP-CIT SPECT-derived metrics improve clinical differentiation of degenerative parkinsonism using an integrated machine learning approach.</p> Methods <p>This cross-sectional study included 487 patients with Parkinson’s disease (PD) and 219 with atypical parkinsonisms (APS), classified as progressive supranuclear palsy (PSP, <i>n</i> = 127), multiple system atrophy parkinsonian type (MSA-P, <i>n</i> = 37), multiple system atrophy cerebellar type (MSA-C, <i>n</i> = 12), and corticobasal degeneration (CBD, <i>n</i> = 43). All participants underwent a [¹²³I]FP-CIT SPECT. Striatal [¹²³I]FP-CIT uptake was quantified using anatomical (caudate, putamen, ventral striatum) and functional (limbic, executive, sensorimotor) parcellations to calculate specific binding ratios, asymmetry indices, and inter-regional ratios. Discriminative performance of each metric was evaluated using receiver operating characteristic (ROC) curves analyses. A random forest classifier integrating all semiquantitative metrics was trained and validated, enabling data-driven identification diagnostic pathways.</p> Results <p>Caudate-to-putamen and sensorimotor-to-limbic inter-regional ratios showed the strongest discriminative performance (AUC up to 0.95) for differentiating PD from APS. The random forest achieved a 64% overall accuracy with high per-class specificities (&gt; 84%) and revealed two diagnostic pathways. A lower caudate-to-posterior putamen ratio, primarily grouped PSP, CBD and MSA-C, where lower contralateral sensorimotor uptake pointing to PSP while higher values and ipsilateral caudate uptake subregion further distinguishing CBD from MSA-C. A higher caudate-to-posterior putamen ratio, included PD and MSA-P, where lower ipsilateral caudate uptake together with a higher sensorimotor-to-cognitive ratio mainly differentiate both groups.</p> Conclusions <p>Integrating anatomical and functional [¹²³I]FP-CIT SPECT metrics within a machine learning framework enhances the clinical differentiation of degenerative parkinsonisms and supports [¹²³I]FP-CIT SPECT as a robust in vivo disease biomarker.</p>

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Semiquantitative [¹²³I]FP-CIT SPECT metrics combined with machine learning improve clinical differentiation of Parkinson’s disease and atypical parkinsonian syndrome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Paula Fernández-Rodríguez,
  • Pablo Franco-Rosado,
  • Patricia Diaz-Galvan,
  • Laura Muñoz-Delgado,
  • Antonio Cristobal Luque-Ambrosiani,
  • Miguel Ángel Labrador-Espinosa,
  • Jesús Silva-Rodríguez,
  • Elena Ojeda-Lepe,
  • Daniel Macías-García,
  • Astrid Adarmes-Gómez,
  • Silvia Jesús,
  • Fátima Carrillo,
  • Jose Antonio Lojo-Ramírez,
  • David García Solis,
  • Michel J. Grothe,
  • Pablo Mir

摘要

Purpose

To evaluate whether semiquantitative striatal [¹²³I]FP-CIT SPECT-derived metrics improve clinical differentiation of degenerative parkinsonism using an integrated machine learning approach.

Methods

This cross-sectional study included 487 patients with Parkinson’s disease (PD) and 219 with atypical parkinsonisms (APS), classified as progressive supranuclear palsy (PSP, n = 127), multiple system atrophy parkinsonian type (MSA-P, n = 37), multiple system atrophy cerebellar type (MSA-C, n = 12), and corticobasal degeneration (CBD, n = 43). All participants underwent a [¹²³I]FP-CIT SPECT. Striatal [¹²³I]FP-CIT uptake was quantified using anatomical (caudate, putamen, ventral striatum) and functional (limbic, executive, sensorimotor) parcellations to calculate specific binding ratios, asymmetry indices, and inter-regional ratios. Discriminative performance of each metric was evaluated using receiver operating characteristic (ROC) curves analyses. A random forest classifier integrating all semiquantitative metrics was trained and validated, enabling data-driven identification diagnostic pathways.

Results

Caudate-to-putamen and sensorimotor-to-limbic inter-regional ratios showed the strongest discriminative performance (AUC up to 0.95) for differentiating PD from APS. The random forest achieved a 64% overall accuracy with high per-class specificities (> 84%) and revealed two diagnostic pathways. A lower caudate-to-posterior putamen ratio, primarily grouped PSP, CBD and MSA-C, where lower contralateral sensorimotor uptake pointing to PSP while higher values and ipsilateral caudate uptake subregion further distinguishing CBD from MSA-C. A higher caudate-to-posterior putamen ratio, included PD and MSA-P, where lower ipsilateral caudate uptake together with a higher sensorimotor-to-cognitive ratio mainly differentiate both groups.

Conclusions

Integrating anatomical and functional [¹²³I]FP-CIT SPECT metrics within a machine learning framework enhances the clinical differentiation of degenerative parkinsonisms and supports [¹²³I]FP-CIT SPECT as a robust in vivo disease biomarker.