Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images

Lee, Juhwan; Pereira, Gabriel Tensol Rodrigues; Motairek, Issam; Kim, Justin N.; Zimin, Vladislav N.; Dallan, Luís Augusto Palma; Hoori, Ammar; Al‐Kindi, Sadeer G.; Guagliumi, Giulio; Wilson, David L.

doi:10.3389/fcvm.2022.1079046

Cited by 3 publications

(1 citation statement)

References 58 publications

(109 reference statements)

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“…Considering the diffuse inflammatory nature of atherosclerosis, it is crucial to analyze the entire 3D lesion, specifically focusing on FC surface area. In another study, we also observed that FC surface area was a strong indicator for predicting the development of neo-atherosclerosis 64 . In contrast, no assessment of thin cap was a strong predictor of MACE.…”

Section: Discussionmentioning

confidence: 68%

Plaque characteristics derived from intravascular optical coherence tomography that predict major adverse cardiovascular events

Lee

Gharaibeh

Zimin

et al. 2023

Preprint

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Background: With its near histological resolution and its optical contrast, intravascular optical coherence tomography (IVOCT) is the only imaging modality that allows a unique assessment of microscopic plaque characteristics. This study aimed to investigate whether plaque characteristics derived from IVOCT could predict a long-term major adverse cardiovascular event (MACE). Methods: This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using OCTOPUS software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel and cholesterol crystal), were computed from the baseline IVOCT images (obtained before stenting). For IVOCT plaque features, the discriminatory power for predicting MACE was determined using univariate/multivariate logistic regression as assessed by area under the receiver operating characteristic curve (AUC). Results: Of 104 patients, MACE was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, maximum calcium angle, maximum calcium thickness, maximum FC angle, maximum FC area, and FC surface area were significantly associated with MACE (p<0.05). Additionally, cholesterol crystal and layered plaque showed a strong association with MACE (p<0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98-5.83, p<0.05) was identified as a significant determinant for MACE, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting MACE was 0.851 (95% CI 0.800-0.927, p<0.05). Luminal stenosis was not a strong predictor of the risk of MACE. Conclusions: Patients with MACE had distinct plaque characteristics in IVOCT. In particular, large FC surface areas were a risk factor. Interestingly, cap thickness, a commonly highlighted feature for lesion vulnerability, was less predictive than cap area. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

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Section: Discussionmentioning

confidence: 68%

Plaque characteristics derived from intravascular optical coherence tomography that predict major adverse cardiovascular events

Lee

Gharaibeh

Zimin

et al. 2023

Preprint

Self Cite

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Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images

Lee,

Kim,

Dallan

et al. 2024

Sci Rep

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Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

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Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death

Lee,

Gharaibeh,

Zimin

et al. 2024

Bioengineering

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This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98–5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800–0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

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Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images

Cited by 3 publications

References 58 publications

Plaque characteristics derived from intravascular optical coherence tomography that predict major adverse cardiovascular events

Plaque characteristics derived from intravascular optical coherence tomography that predict major adverse cardiovascular events

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images

Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death

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