Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features

Tang, Jing; Bao, Yong; Adams, Matthew P.; Shenkov, Nikolay; Klyuzhin, Ivan S.; Fotouhi, Sima; Davoodi‐Bojd, Esmaeil; Lu, Lijun; Soltanian‐Zadeh, Hamid; Rahmim, Arman

doi:10.1007/s11307-019-01334-5

Cited by 35 publications

(27 citation statements)

References 31 publications

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“…In a longitudinal study, radiomics successfully detected microstructural changes in invisible normal-appearing white matter on conventional T2 fluid-attenuated inversionrecovery (FLAIR) images (18). In addition, recent studies indicated that radiomics features derived from DAT SPECT images can serve as a biomarker for PD progression tracking (26,27). Notably, a previous texture analysis study demonstrated that texture features based on T2-weighted imaging (T2WI) differed between PD patients and healthy controls in many brain regions (28).…”

Section: Introductionmentioning

confidence: 99%

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Liu¹,

Wang

Zheng³

et al. 2020

Front. Neurol.

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Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age-and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

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Section: Introductionmentioning

confidence: 99%

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Liu¹,

Wang

Zheng³

et al. 2020

Front. Neurol.

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“…SVM was the most used algorithm for this binary classification. It was implemented based on imaging signals from PET and SPECT [31,[80][81][82][83][84][85]. These studies were compared with different hand-crafted ML algorithms such as ANN, KNN and RF.…”

Section: Discussionmentioning

confidence: 99%

“…This leads to an uncertainty because of the different conditions of acquisition and the different levels of noise generated from different instruments. Furthermore, Tang's approach [85] is robust and sufficiently fast for routine use. However, it was trained on a small dataset and the year 0 non‐imaging and imaging measures are uncertain.…”

Section: Discussionmentioning

confidence: 99%

“…In 2019, Tang et al [85] presented a process to predict motor's outcome four years post diagnosis from baseline DAT SPECT imaging and clinical measurements using ANN algorithm. Sixty nine patients from the PPMI database used are divided into two classes (divided by the UPDRS III threshold of 30): 26 PD patients (UPDRS III threshold ≥ 30), 43 HC subjects (UPDRS III threshold < 30).…”

Section: Ml/dl In Pd Diagnosismentioning

confidence: 99%

See 1 more Smart Citation

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

2020

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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

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“…Bu çalışmaların en önemli amacı hem hastalar için fiziki zorlukları ortadan kaldırmak hem de klinik çalışanları üzerindeki iş yükünü azaltmaktır. PH'ın erken evrelerinde ve en sık görülen rahatsızlıklarından biri vokal (konuşma ve ses) problemlerdir [9][10][11]21]. Yakın geçmişte bireylerin konuşma kayıtları (sesleri) kaydedilerek PH teşhisi üzerinde çalışmalar gerçekleştirilmiştir [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionunclassified

Parkinson Hastalığı Teşhisi İçin Makine Öğrenmesi Tabanlı Yeni Bir Yöntem

Esmer

Uçar

Çil

et al. 2020

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Parkinson hastalığı (PH), dopamin üreten beyin hücrelerinin ölmesiyle ya da zarar görmesiyle ortaya çıkan bir beyin hastalığıdır. Böyle bir durumda, beyin normal fonksiyonlarını yerine getiremez. PH, konuşma, yürüme ve yazma gibi insan hareketlerini olumsuz olarak etkiler. Bu hastalığın teşhisinde detaylı tıbbi öykü, geçmiş tedavi öyküsü, fiziksel testler ve bazı kan testleri ile beyin filmleri istenilmektedir. Bu işlemler maliyetli ve meşakkatli olabildiği için daha az maliyetli ve daha kolay yapılabilen teşhis bu noktada önem kazanmaktadır. Bu çalışmada doktorun kararına destek olabilmesi için 252 bireyden alınan ses verileri ile PH'ın teşhis edilebilmesi amaçlanmıştır. Verilerden daha iyi sonuç alabilmek için bazı ön işlemler uygulanmıştır. Verilerde dengeleme işlemi yapılmış ve sistematik örnekleme metodu ile işleme alınacak veriler belirlenmiştir. Öznitelik seçme algoritması ile özniteliklerin etiket üzerindeki etki gücü hesaplanıp bazı veri grupları oluşturulmuştur. Sınıflandırma algoritmalarından Karar ağacı, Destek Vektör Makineleri ve K En Yakın Komşu Algoritması kullanılıp, performans değerlendirme kriterleribunlar; Doğruluk Oranı, Duyarlılık, Özgünlük, F-Ölçümü, Kappa, Auc-değerlendirilmiştir. En yüksek performans değerine sahip veri grubu ve kullanılan sınıflandırma algoritması belirlenip model oluşturulmuştur. Model en ilgiliden en ilgisize doğru sıralanmış veri setinin %45'inden ve Destek vektör makineleri algoritması kullanılarak oluşturulmuştur. Performans kriterlerinde %85 doğruluk oranı ve diğer kriterlerde de olumlu sonuçlar elde edilmiştir. Böylece PH olma ihtimali olan bireyin ses kayıtlarından oluşturulan veri seti ve uygulanan model yardımı ile doktora tıbbi karar destek sağlanacağı anlaşılmıştır.

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Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features

Cited by 35 publications

References 31 publications

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

Parkinson Hastalığı Teşhisi İçin Makine Öğrenmesi Tabanlı Yeni Bir Yöntem

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