A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT

Cantoni, Valeria; Green, Roberta; Ricciardi, Carlo; Assante, Roberta; Zampella, Emilia; Nappi, Carmela; Gaudieri, Valeria; Mannarino, Teresa; Genova, Andrea; Simini, Giovanni De; Giordano, A; D’Antonio, Adriana; Acampa, Wanda; Petretta, Mario; Cuocolo, Alberto

doi:10.1007/s12350-020-02187-0

Cited by 19 publications

(11 citation statements)

References 42 publications

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“…All patients were submitted to stress technetium-99m sestamibi-gated SPECT MPI by physical exercise or dipyridamole stress test, according to the recommendations of the European Association of Nuclear Medicine and European Society of Cardiology [21]. The protocol followed in this paper was the same employed in our previous research [20]. All patients underwent MPI by both C-SPECT and CZT-SPECT systems according to a randomized scheme in 1 : 1 ratio that determined which camera was used for first acquisition.…”

Section: Study Protocolmentioning

confidence: 99%

“…Several studies have been conducted to test CAD detection using ML algorithms and to predict patient outcome [15][16][17][18]. An innovative approach is to use ML models to compare the performance of biomedical technologies, and an evaluation of the performance in terms of diagnostic power has already been reported [19,20], demonstrating CZT-SPECT has a better ability to detect CAD. To the best of our knowledge, the prognostic value of CZT-SPECT and C-SPECT has not been investigated to date by using ML techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The 10-fold crossvalidation was used to train and test the models; the dataset was divided into 10 groups of data, 9 were used for training the model and one group for testing it; the procedure was repeated 10 times, and the evaluation metrics are computed by averaging all those obtained [23]. The tree-based approach has shown in literature great results not only in the cardiologic context in cases such as diagnosis [24][25][26], prognosis [27,28], and comparison of biomedical technologies [19,20] but also in other medical specialties [29][30][31]. The classification tree is a simple and effective model consisting of nodes, branches, and leaves: each node has a rule that the data is routed along several branches while the leaves represent the output of the system [32].…”

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confidence: 99%

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Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach

Cantoni

Green

Ricciardi

et al. 2021

Computational and Mathematical Methods in Medicine

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We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k -nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy ( p value = 0.02 and p value = 0.01) and recall ( p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall.

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Section: Study Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach

Cantoni

Green

Ricciardi

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Several classifiers can be used and are well-known in literature; among all we chose to follow a tree-based approach because in literature it has often been successful in literature [63][64][65] and, particularly, because the decision tree (J48), a well-known structure made up of leaves and nodes that represent the features and the classes, allowed us to open the black box nature of ML algorithms by performing the top 10 feature importance and, consequently, the univariate statistical analysis on those features. Each split in the tree can be performed in different ways, the most used (that also give similar results when applied) being information gain and gini index 66 .…”

Section: K-fold Cross-validationmentioning

confidence: 99%

Machine learning to predict mortality after rehabilitation among patients with severe stroke

Scrutinio

Ricciardi

Donisi

et al. 2020

Sci Rep

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Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.

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“…More recently, the new cadmium zinc telluride (CZT) cameras have made a significant difference in evaluation of patients for CAD. 24,25 Nishiyama et al 26 assessed the feasibility of combined imaging using a novel ultrafast CZT camera. They concluded that the combined supine and prone CZT SPECT yields significant gains in specificity and accuracy, whereas acquisition time is reduced by up to one fifth.…”

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confidence: 99%

Prone-only SPECT myocardial perfusion imaging: An alternative standard in clinical practice?

Cantoni

Green

Cuocolo

2022

Journal of Nuclear Cardiology

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A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT

Cited by 19 publications

References 42 publications

Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach

Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach

Machine learning to predict mortality after rehabilitation among patients with severe stroke

Prone-only SPECT myocardial perfusion imaging: An alternative standard in clinical practice?

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