Effect of high-intensity statin therapy on atherosclerosis (IBIS-4): Manual versus automated methods of IVUS analysis
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Effect of high-intensity statin therapy on atherosclerosis (IBIS-4) : Manual versus automated methods of IVUS analysis. / Bass, Ronald D.; García-García, Héctor M.; Ueki, Yasushi; Holmvang, Lene; Pedrazzini, Giovanni; Roffi, Marco; Koskinas, Konstantinos C.; Shibutani, Hiroki; Losdat, Sylvain; Ziemer, Paulo G.P.; Blanco, Pablo J.; Levine, Molly B.; Bourantas, Christos V.; Räber, Lorenz.
In: Cardiovascular Revascularization Medicine, Vol. 54, 2023, p. 33-38.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Effect of high-intensity statin therapy on atherosclerosis (IBIS-4)
T2 - Manual versus automated methods of IVUS analysis
AU - Bass, Ronald D.
AU - García-García, Héctor M.
AU - Ueki, Yasushi
AU - Holmvang, Lene
AU - Pedrazzini, Giovanni
AU - Roffi, Marco
AU - Koskinas, Konstantinos C.
AU - Shibutani, Hiroki
AU - Losdat, Sylvain
AU - Ziemer, Paulo G.P.
AU - Blanco, Pablo J.
AU - Levine, Molly B.
AU - Bourantas, Christos V.
AU - Räber, Lorenz
N1 - Publisher Copyright: © 2023
PY - 2023
Y1 - 2023
N2 - Aims: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods. Methods: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm. Results: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of −0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of −1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of −2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of −3.74 % (p < 0.001). Conclusions: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard.
AB - Aims: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods. Methods: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm. Results: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of −0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of −1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of −2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of −3.74 % (p < 0.001). Conclusions: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard.
KW - Coronary artery disease
KW - Intravascular ultrasound
KW - Lumen segmentation
KW - Machine learning
KW - Vessel segmentation
U2 - 10.1016/j.carrev.2023.04.007
DO - 10.1016/j.carrev.2023.04.007
M3 - Journal article
C2 - 37087308
AN - SCOPUS:85152935718
VL - 54
SP - 33
EP - 38
JO - Cardiovascular Revascularization Medicine
JF - Cardiovascular Revascularization Medicine
SN - 1553-8389
ER -
ID: 362890296