Intracoronary near-infrared spectroscopy: an overview of the technology, histologic validation, and clinical applications

O’Brien, Andrew; Lacombe, A; Stickland, Aubrey; Madder, Ryan D.

doi:10.21542/gcsp.2016.18

Cited by 2 publications

(1 citation statement)

References 39 publications

(42 reference statements)

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“…The primary NIRS and IVUS findings were LCBI ≥ median, MaxLCBI 4mm ≥400 and MaxPB ≥70%, based on knowledge from previous studies 2 11 12. The LCBI and MaxLCBI 4mm were automatically provided by the NIRS-derived chemogram and the MaxPB was calculated by manual contouring of the external elastic membrane (EEM) and vessel lumen and defined as the area of plaque and media divided by EEM area 16 17. When combined NIRS-IVUS of the same coronary segments was performed both pre- and post-stent placement, the pre-stent imaging pullback was selected for the culprit analysis, and the post-stent imaging pullback was selected for the non-culprit follow-up analysis.…”

Section: Methodsmentioning

confidence: 99%

Intracoronary near-infrared spectroscopy and the risk of future cardiovascular events

et al. 2019

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ObjectivesThe objectives of this study were to investigate if findings by intracoronary near-infrared spectroscopy (NIRS) and intravascular ultrasound (IVUS) are associated with future cardiovascular events and if NIRS can differentiate culprit from non-culprit segments in patients with coronary artery disease.MethodsThe study included 144 patients with coronary artery disease undergoing percutaneous coronary intervention and combined NIRS-IVUS imaging at two Swedish hospitals. The NIRS-derived lipid core burden index (LCBI), the 4 mm segment with maximum LCBI (MaxLCBI4mm) and the IVUS-derived maximum plaque burden (MaxPB) were analysed within the culprit segment and continuous 10 mm non-culprit segments of the index culprit vessels. The association with future major adverse cardiovascular and cerebrovascular events (MACCE), defined as all-cause mortality, acute coronary syndrome requiring revascularisation and cerebrovascular events during follow-up was evaluated using multivariable Cox regressions. A receiver operating characteristic (ROC) analysis was performed to test the ability of NIRS to discriminate culprit against non-culprit segments.ResultsA non-culprit maxLCBI4mm ≥400 (HR: 3.67, 95% CI 1.46 to 9.23, p=0.006) and a non-culprit LCBI ≥ median (HR: 3.08, 95% CI 1.11 to 8.56, p=0.031) were both significantly associated with MACCE, whereas a non-culprit MaxPB ≥70% (HR: 0.61, 95% CI 0.08 to 4.59, p=0.63) was not. The culprit segments had larger lipid cores compared with non-culprit segments (MaxLCBI4mm 425 vs 74, p<0.001), and the ROC analysis showed that NIRS can differentiate culprit against non-culprit segments (c-statistics: 0.85, 95% CI 0.81 to 0.89).ConclusionA maxLCBI4mm ≥400 and LCBI ≥ median, assessed by NIRS in non-culprit segments of a culprit artery, were significantly associated with patient-level MACCE. NIRS furthermore adequately discriminated culprit against non-culprit segments in patients with coronary disease.

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Section: Methodsmentioning

confidence: 99%

Intracoronary near-infrared spectroscopy and the risk of future cardiovascular events

et al. 2019

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Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images

Zhang,

Broersen,

Sokooti

et al. 2024

Int J CARS

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Purpose The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology. Methods Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods. Results For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method. Conclusion The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

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Intracoronary near-infrared spectroscopy: an overview of the technology, histologic validation, and clinical applications

Cited by 2 publications

References 39 publications

Intracoronary near-infrared spectroscopy and the risk of future cardiovascular events

Intracoronary near-infrared spectroscopy and the risk of future cardiovascular events

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images

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