The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET/CT in the diagnosis of Parkinson’s disease

Piccardo, Arnoldo; Cappuccio, Roberto; Bottoni, Gianluca; Cecchin, Diego; Mazzella, L.; Cirone, A.; Righi, Sergio; Ugolini, Martina; Bianchi, P. E.; Bertolaccini, P.; Lorenzini, Elena; Massollo, Michela; Castaldi, Antonio; Fiz, Francesco; Strada, Laura; Cistaro, Angelina; Sette, Massimo Del

doi:10.1007/s00330-021-07779-z

Cited by 19 publications

(8 citation statements)

References 19 publications

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“…In any event, we aver that for practical and ethical purposes, the suitability of the CNN or the BPNN model for SPECT imaging should still be assessed via clinical trials. As for the PET and the MRI study cases, we note that the highest performing CNN model was 93% [41] and 95.3% [42], respectively (Figure 7, Appendix A Table A1).…”

Section: Resultsmentioning

confidence: 91%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

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Section: Resultsmentioning

confidence: 91%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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“…As mentioned by the authors, the clinical utility of such approach remains unknown, also questioning its conceptual diagnostic relevance in this particular topic, given the limited semiology of 18 F-DOPA PET pattern abnormalities, and the well-known major logistical drawbacks of handcrafted radiomic pipelines in real-life practice. Recently, the clinical utility of deep-learning based methods to identify Parkinson disease directly from PET data has been emphasized, with very promising results [45][46][47]. Deep learning conceptually tackles all the limitations of handcrafted radiomics procedures and would probably constitute a more powerful and efficient alternative to human expert reading for basic imaging identification tasks.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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“…In a context of increased imbalance between workforce and workflow in daily practice, simplified and fast imaging postprocessing becomes relevant, and few minutes of gain per scan can be expected with this method. Ultimately, the acceptable diagnostic performance of simplified analysis of 3D PET MIP without huge image postprocessing would promote the design of dedicated automated IA-based analysis pipelines into imaging workflows 11–15 …”

Section: Discussionmentioning

confidence: 99%

Head-to-Head Comparison Between Rabbit Sign and EANM/SNMMI Criteria for the 18F-DOPA Visual Assessment of Parkinsonian Syndromes in PET/MRI

et al. 2022

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The aim of this study was to compare the diagnostic performance of the rabbit visual pattern versus the one endorsed by the EANM/SNMMI for the diagnosis of parkinsonian syndromes in PET/MRI. Patients and Methods: The 18 F-DOPA PET images of 129 consecutive patients (65 Park+ and 64 controls) with 1 year of clinical follow-up were reviewed independently by 5 experienced readers on the same imaging workstation, blinded to the final clinical diagnosis. Two visual methods were assessed independently, with several days to months of interval: the criteria endorsed by EANM/SNMMI and the "rabbit" shape of the striate assessed on 3D MIP images. The sensitivities, specificities, likelihood ratios, and predictive values of the 2 diagnostic tests were estimated simultaneously by using the "comparison of 2 binary diagnostic tests to a paired design" method. Results: The estimated 95% confidence interval (CI) of sensitivities and specificities ranged from 49.4% to 76.5% and from 83.2% to 97.7%, respectively. The 95% CI estimates of positive and negative likelihood ratios ranged from 3.8 to 26.7 and from 0.26 to 0.56, respectively. The 95% CI estimates of the positive and negative predictive values ranged from 78.1% to 96.7% and from 60.3% to 81.4%, respectively. For all the parameters, no statistical difference was observed between the 2 methods ( P > 0.05). The rabbit sign reduced the readers' discrepancies by 25%, while maintaining the same performance. Conclusions: The rabbit visual pattern appears at least comparable to the current EANM/SNMMI reference procedure for the assessment of parkinsonian syndromes in daily clinical practice, without the need of any image postprocessing. Further multicenter prospective studies would be of relevance to validate these findings.

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The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET/CT in the diagnosis of Parkinson’s disease

Cited by 19 publications

References 19 publications

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

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

Head-to-Head Comparison Between Rabbit Sign and EANM/SNMMI Criteria for the 18F-DOPA Visual Assessment of Parkinsonian Syndromes in PET/MRI

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