TY - JOUR
T1 - Radiomics analysis enhances the diagnostic performance of CMR stress perfusion
T2 - a proof-of-concept study using the Dan-NICAD dataset
AU - Raisi-Estabragh, Zahra
AU - Martin-Isla, Carlos
AU - Nissen, Louise
AU - Szabo, Liliana
AU - Campello, Victor M.
AU - Escalera, Sergio
AU - Winther, Simon
AU - Bøttcher, Morten
AU - Lekadir, Karim
AU - Petersen, Steffen E.
N1 - Funding Information:
ZR-E recognizes the National Institute for Health Research (NIHR) Integrated Academic Training programme which supports her Academic Clinical Lectureship post and was also supported by British Heart Foundation Clinical Research Training Fellowship No. FS/17/81/33318. SEP acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Centre at Barts. SEP acknowledges support from the “SmartHeart” EPSRC programme grant ( www.nihr.ac.uk ; EP/P001009/1). LS was supported by the ÚNKP-22-4-I New National Excellence Program of the Ministry for Culture and Innovation from the National Research, Development and Innovation Fund. SEP and LS have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project). SW acknowledges support from Novo Nordisk Foundation (Clinical Emerging Investigator grant, NNF21OC0066981). This article is supported by the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH), which is funded from the Data to Early Diagnosis and Precision Medicine strand of the government's Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI). Views expressed are those of the authors and not necessarily those of the AI4VBH Consortium members, the NHS, Innovate UK, or UKRI. This work was partly funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825903 (euCanSHare project). This work has been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya.). LS received funding from the European Association of Cardiovascular Imaging (EACVI Research Grant App000076437). This work is partially supported by ICREA under the ICREA Academia programme. KL is supported by the Ramony Cajal Program of the the Spanish Ministry of Economy and Competitiveness under grant no. RYC-2015-17183. The Dan-NICAD trial was funded by the The Danish Heart Foundation (Grant No. 15-R99-A5837-22920) and the Health Research Fund of Central Denmark Region.
Funding Information:
ZR-E recognizes the National Institute for Health Research (NIHR) Integrated Academic Training programme which supports her Academic Clinical Lectureship post and was also supported by British Heart Foundation Clinical Research Training Fellowship No. FS/17/81/33318. SEP acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Centre at Barts. SEP acknowledges support from the “SmartHeart” EPSRC programme grant (www.nihr.ac.uk ; EP/P001009/1). LS was supported by the ÚNKP-22-4-I New National Excellence Program of the Ministry for Culture and Innovation from the National Research, Development and Innovation Fund. SEP and LS have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project). SW acknowledges support from Novo Nordisk Foundation (Clinical Emerging Investigator grant, NNF21OC0066981). This article is supported by the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH), which is funded from the Data to Early Diagnosis and Precision Medicine strand of the government's Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI). Views expressed are those of the authors and not necessarily those of the AI4VBH Consortium members, the NHS, Innovate UK, or UKRI. This work was partly funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825903 (euCanSHare project). This work has been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya.). LS received funding from the European Association of Cardiovascular Imaging (EACVI Research Grant App000076437). This work is partially supported by ICREA under the ICREA Academia programme. KL is supported by the Ramony Cajal Program of the the Spanish Ministry of Economy and Competitiveness under grant no. RYC-2015-17183. The Dan-NICAD trial was funded by the The Danish Heart Foundation (Grant No. 15-R99-A5837-22920) and the Health Research Fund of Central Denmark Region.
Publisher Copyright:
2023 Raisi-Estabragh, Martin-Isla, Nissen, Szabo, Campello, Escalera, Winther, Bøttcher, Lekadir and Petersen.
PY - 2023/9
Y1 - 2023/9
N2 - Objectives: To assess the feasibility of extracting radiomics signal intensity based features from the myocardium using cardiovascular magnetic resonance (CMR) imaging stress perfusion sequences. Furthermore, to compare the diagnostic performance of radiomics models against standard-of-care qualitative visual assessment of stress perfusion images, with the ground truth stenosis label being defined by invasive Fractional Flow Reserve (FFR) and quantitative coronary angiography. Methods: We used the Dan-NICAD 1 dataset, a multi-centre study with coronary computed tomography angiography, 1,5 T CMR stress perfusion, and invasive FFR available for a subset of 148 patients with suspected coronary artery disease. Image segmentation was performed by two independent readers. We used the Pyradiomics platform to extract radiomics first-order (n = 14) and texture (n = 75) features from the LV myocardium (basal, mid, apical) in rest and stress perfusion images. Results: Overall, 92 patients (mean age 62 years, 56 men) were included in the study, 39 with positive FFR. We double-cross validated the model and, in each inner fold, we trained and validated a per territory model. The conventional analysis results reported sensitivity of 41% and specificity of 84%. Our final radiomics model demonstrated an improvement on these results with an average sensitivity of 53% and specificity of 86%. Conclusion: In this proof-of-concept study from the Dan-NICAD dataset, we demonstrate the feasibility of radiomics analysis applied to CMR perfusion images with a suggestion of superior diagnostic performance of radiomics models over conventional visual analysis of perfusion images in picking up perfusion defects defined by invasive coronary angiography.
AB - Objectives: To assess the feasibility of extracting radiomics signal intensity based features from the myocardium using cardiovascular magnetic resonance (CMR) imaging stress perfusion sequences. Furthermore, to compare the diagnostic performance of radiomics models against standard-of-care qualitative visual assessment of stress perfusion images, with the ground truth stenosis label being defined by invasive Fractional Flow Reserve (FFR) and quantitative coronary angiography. Methods: We used the Dan-NICAD 1 dataset, a multi-centre study with coronary computed tomography angiography, 1,5 T CMR stress perfusion, and invasive FFR available for a subset of 148 patients with suspected coronary artery disease. Image segmentation was performed by two independent readers. We used the Pyradiomics platform to extract radiomics first-order (n = 14) and texture (n = 75) features from the LV myocardium (basal, mid, apical) in rest and stress perfusion images. Results: Overall, 92 patients (mean age 62 years, 56 men) were included in the study, 39 with positive FFR. We double-cross validated the model and, in each inner fold, we trained and validated a per territory model. The conventional analysis results reported sensitivity of 41% and specificity of 84%. Our final radiomics model demonstrated an improvement on these results with an average sensitivity of 53% and specificity of 86%. Conclusion: In this proof-of-concept study from the Dan-NICAD dataset, we demonstrate the feasibility of radiomics analysis applied to CMR perfusion images with a suggestion of superior diagnostic performance of radiomics models over conventional visual analysis of perfusion images in picking up perfusion defects defined by invasive coronary angiography.
KW - CMR (cardiovascular magnetic resonance)
KW - Dan-NICAD
KW - machine learning (ML)
KW - radiomics
KW - stress perfusion cardiac MRI
UR - http://www.scopus.com/inward/record.url?scp=85173589783&partnerID=8YFLogxK
U2 - 10.3389/fcvm.2023.1141026
DO - 10.3389/fcvm.2023.1141026
M3 - Journal article
C2 - 37781298
AN - SCOPUS:85173589783
SN - 2297-055X
VL - 10
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
M1 - 1141026
ER -