Cost-effectiveness of 82-Rubidium PET myocardial perfusion imaging for the diagnosis of myocardial ischemia depending on the prevalence of coronary artery disease

Mimouni, Maroua; Bulsei, Julie; Darlington, Meryl; Estellat, Candice; Rouzet, François; Hyafil, Fabien; Durand‐Zaleski, Isabelle

doi:10.1186/s13550-023-00954-x

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…As an alternative, quantitative MPI can be obtained using [ 82 Rb] Positron Emission Tomography (PET) and is nowadays a functional and non-invasive method for assessing the risk of MACE, or to investigate physiological consequences on the organ after a cardiac event [7][8][9][10][11] . It is already known to outperform the predictive value of SPECT for MACE prediction 12 and was proven cost effective 13 . This procedure quantifies the Myocardial Blood Flow (MBF) and the Myocardial Flow Reserve (MFR) in the LV.…”

Section: Introductionmentioning

confidence: 99%

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

Bors,

Abler,

Matthieu

et al. 2023

Preprint

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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 [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.

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Section: Introductionmentioning

confidence: 99%

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

Bors,

Abler,

Matthieu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As an alternative, quantitative MPI can be obtained using [ 82 Rb] Positron Emission Tomography (PET) and is nowadays a functional and non-invasive method for assessing the risk of MACE, or to investigate physiological consequences on the organ after a cardiac event [7][8][9][10][11][12] . It is already known to bring a strong predictive value for MACE prediction 13 and was proven cost effective 14 . This procedure quantifies the Myocardial Blood Flow (MBF) and the Myocardial Flow Reserve (MFR) in the LV using dynamic [ 82 Rb] PET/CT acquisitions.…”

mentioning

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.

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Internal dosimetry study of [82Rb]Cl using a long axial field-of-view PET/CT

Mercolli,

Bregenzer,

Diemling

et al. 2024

Eur J Nucl Med Mol Imaging

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Purpose Long axial field-of-view (LAFOV) positron emission tomography (PET) systems allow to image all major organs with one bed position, which is particularly useful for acquiring whole-body dynamic data using short-lived radioisotopes like 82Rb. Methods We determined the absorbed dose in target organs of three subjects (29, 40, and 57 years old) using two different methods, i.e., MIRD and voxel dosimetry. The subjects were injected with 407.0 to 419.61 MBq of [82Rb]Cl and were scanned dynamically for 7 min with a LAFOV PET/CT scanner. Results Using the MIRD formalism and voxel dosimetry, the absorbed dose ranged from 1.84 to 2.78 μGy/MBq (1.57 to 3.92 μGy/MBq for voxel dosimetry) for the heart wall, 2.76 to 5.73 μGy/MBq (3.22 to 5.37 μGy/MBq for voxel dosimetry) for the kidneys, and 0.94 to 1.88 μGy/MBq (0.98 to 1.92 μGy/MBq for voxel dosimetry) for the lungs. The total body effective dose lied between 0.50 and 0.76 μSv/MBq. Conclusion Our study suggests that the radiation dose associated with [82Rb]Cl PET/CT can be assessed by means of dynamic LAFOV PET and that it is lower compared to literature values.

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Cost-effectiveness of 82-Rubidium PET myocardial perfusion imaging for the diagnosis of myocardial ischemia depending on the prevalence of coronary artery disease

Cited by 7 publications

References 17 publications

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

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

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

Internal dosimetry study of [82Rb]Cl using a long axial field-of-view PET/CT

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