Relevance of 18F-DOPA visual and semi-quantitative PET metrics for the diagnostic of Parkinson disease in clinical practice: a machine learning-based inference study

Alex Iep; Mohammad B Chawki; Lucas Goldfarb; Loc Nguyen; Vincent Brulon; Claude Comtat; Vincent Lebon; Florent L Besson

doi:10.1186/s13550-023-00962-x

Relevance of ¹⁸F-DOPA visual and semi-quantitative PET metrics for the diagnostic of Parkinson disease in clinical practice: a machine learning-based inference study

EJNMMI Res. 2023 Feb 13;13(1):13. doi: 10.1186/s13550-023-00962-x.

Authors

Alex Iep¹, Mohammad B Chawki², Lucas Goldfarb², Loc Nguyen², Vincent Brulon², Claude Comtat³, Vincent Lebon³, Florent L Besson²

Affiliations

¹ Nuclear Medicine Department, Service Hospitalier Frédéric Joliot SHFJ-CEA, Orsay, France. alexiep@ymail.com.
² Nuclear Medicine Department, Service Hospitalier Frédéric Joliot SHFJ-CEA, Orsay, France.
³ Inserm, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale BioMaps, SHFJ, Université Paris Saclay, Orsay, France.

Abstract

Purpose: To decipher the relevance of visual and semi-quantitative 6-fluoro-(18F)-L-DOPA (¹⁸F-DOPA) interpretation methods for the diagnostic of idiopathic Parkinson disease (IPD) in hybrid positron emission tomography (PET) and magnetic resonance imaging.

Material and methods: A total of 110 consecutive patients (48 IPD and 62 controls) with 11 months of median clinical follow-up (reference standard) were included. A composite visual assessment from five independent nuclear imaging readers, together with striatal standard uptake value (SUV) to occipital SUV ratio, striatal gradients and putamen asymmetry-based semi-quantitative PET metrics automatically extracted used to train machine learning models to classify IPD versus controls. Using a ratio of 70/30 for training and testing sets, respectively, five classification models-k-NN, LogRegression, support vector machine, random forest and gradient boosting-were trained by using 100 times repeated nested cross-validation procedures. From the best model on average, the contribution of PET parameters was deciphered using the Shapley additive explanations method (SHAP). Cross-validated receiver operating characteristic curves (cv-ROC) of the most contributive PET parameters were finally estimated and compared.

Results: The best machine learning model (k-NN) provided final cv-ROC of 0.81. According to SHAP analyses, visual PET metric was the most important contributor to the model overall performance, followed by the minimum between left and right striatal to occipital SUV ratio. The 10-time cv-ROC curves of visual, min SUVr or both showed quite similar performance (mean area under the ROC of 0.81, 0.81 and 0.79, respectively, for visual, min SUVr or both).

Conclusion: Visual expert analysis remains the most relevant parameter to predict IPD diagnosis at 11 months of median clinical follow-up in ¹⁸F-FDOPA. The min SUV ratio appears interesting in the perspective of simple semi-automated diagnostic workflows.

Keywords: Fluorodopa F 18; Machine learning; Parkinson's disease; Positron-emission tomography.