Comparison of the Prognostic Value of Global and Regional Myocardial Flow Capacity Radius, Myocardial Flow Reserve, and Stress Myocardial Blood Flow Using Rubidium-82 with SiPM PET/CT

Dietz, Matthieu; Kamani, Christel H.; Allenbach, Gilles; Rubimbura, Vladimir; Fournier, Stéphane; Lalonde, Marie Nicod; Schaefer, Niklaus; Eeckhout, Éric; Müller, Olivier; Prior, John O.

doi:10.21203/rs.3.rs-1122028/v1

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The first two signals (sMBF and MFR) are established methods for the quantitative assessment of myocardial perfusion and known to be predictive for MACE 13 . We introduce MFC radius as a simple geometric method to evaluate MFC at a pixel level to generate a "capacity" image 53 . We recall that MFC is able to leverage information from both sMBF and MFR 17 and was previously reported to be predictive of MACE 13 .…”

Section: Quantitative Myocardial Perfusion Assessment and Data Formatmentioning

confidence: 99%

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Bors,

Abler,

Dietz

et al. 2024

Sci Rep

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Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [$$^{82}$$ 82 Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model ($$p<0.001$$ p < 0.001 ), where the best AUC of 73.9% (CI 72.5–75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3–74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.

show abstract

Section: Quantitative Myocardial Perfusion Assessment and Data Formatmentioning

confidence: 99%

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Bors,

Abler,

Dietz

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first two signals (sMBF and MFR) are established methods for the quantitative assessment of myocardial perfusion and known to be predictive for MACE 16 . We introduce MFC radius as a simple geometric method to evaluate MFC at a pixel level to generate a "capacity" image 49 . We recall that MFC is able to leverage information from both sMBF and MFR 17 and was previously reported to be predictive of MACE 16 .…”

Section: Quantitative Myocardial Perfusion Assessment and Data Formatmentioning

confidence: 99%

Predicting MACE from [82Rb] PET: Can AI outperformmore traditional quantitative assessment of themyocardial perfusion ?

Bors,

Abler,

Matthieu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model (p < 0.001), where the best AUC of 73.9% (CI: 72.5-75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI: 72.3-74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.

show abstract

Advances in Digital PET Technology and Its Potential Impact on Myocardial Perfusion and Blood Flow Quantification

et al. 2023

View full text Add to dashboard Cite

Comparison of the Prognostic Value of Global and Regional Myocardial Flow Capacity Radius, Myocardial Flow Reserve, and Stress Myocardial Blood Flow Using Rubidium-82 with SiPM PET/CT

Cited by 4 publications

References 19 publications

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Predicting MACE from [82Rb] PET: Can AI outperformmore traditional quantitative assessment of themyocardial perfusion ?

Advances in Digital PET Technology and Its Potential Impact on Myocardial Perfusion and Blood Flow Quantification

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