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Lee J, Gharaibeh Y, Zimin VN, Kim JN, Hassani NS, Dallan LAP, Pereira GTR, Makhlouf MHE, Hoori A, Wilson DL. Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death. Bioengineering (Basel) 2024; 11:843. [PMID: 39199801 PMCID: PMC11351967 DOI: 10.3390/bioengineering11080843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 09/01/2024] Open
Abstract
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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Affiliation(s)
- Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
| | - Yazan Gharaibeh
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
| | - Vladislav N. Zimin
- Brookdale University Hospital Medical Center, 1 Brookdale Plaza, Brooklyn, NY 11212, USA;
| | - Justin N. Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
| | - Neda S. Hassani
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Luis A. P. Dallan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Gabriel T. R. Pereira
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Mohamed H. E. Makhlouf
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
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Lee J, Kim JN, Dallan LAP, Zimin VN, Hoori A, Hassani NS, Makhlouf MHE, Guagliumi G, Bezerra HG, Wilson DL. Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images. Sci Rep 2024; 14:4393. [PMID: 38388637 PMCID: PMC10884035 DOI: 10.1038/s41598-024-55120-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open
Abstract
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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Affiliation(s)
- Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Justin N Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Luis A P Dallan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Vladislav N Zimin
- Brookdale University Hospital Medical Center, 1 Brookdale Plaza, Brooklyn, NY, 11212, USA
| | - Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Neda S Hassani
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Mohamed H E Makhlouf
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Giulio Guagliumi
- Cardiovascular Department, Innovation District, Galeazzi San'Ambrogio Hospital, Milan, Italy
| | - Hiram G Bezerra
- Interventional Cardiology Center, Heart and Vascular Institute, University of South Florida, Tampa, FL, 33606, USA
| | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Salimi M, Roshanfar M, Tabatabaei N, Mosadegh B. Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine. J Pers Med 2023; 14:33. [PMID: 38248734 PMCID: PMC10817559 DOI: 10.3390/jpm14010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/08/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.
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Affiliation(s)
| | - Majid Roshanfar
- Department of Mechanical Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;
| | - Nima Tabatabaei
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada;
| | - Bobak Mosadegh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
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Kim JN, Gomez-Perez L, Zimin VN, Makhlouf MHE, Al-Kindi S, Wilson DL, Lee J. Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques. Bioengineering (Basel) 2023; 10:bioengineering10030360. [PMID: 36978751 PMCID: PMC10045206 DOI: 10.3390/bioengineering10030360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/07/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023] Open
Abstract
Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.
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Affiliation(s)
- Justin N. Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Lia Gomez-Perez
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Vladislav N. Zimin
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Mohamed H. E. Makhlouf
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Sadeer Al-Kindi
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
- Correspondence:
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Lee J, Kim JN, Gomez-Perez L, Gharaibeh Y, Motairek I, Pereira GTR, Zimin VN, Dallan LAP, Hoori A, Al-Kindi S, Guagliumi G, Bezerra HG, Wilson DL. Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning. Bioengineering (Basel) 2022; 9:648. [PMID: 36354559 PMCID: PMC9687448 DOI: 10.3390/bioengineering9110648] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 09/03/2024] Open
Abstract
Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.
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Affiliation(s)
- Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Justin N Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Lia Gomez-Perez
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Yazan Gharaibeh
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Issam Motairek
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Gabriel T R Pereira
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Vladislav N Zimin
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Luis A P Dallan
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Sadeer Al-Kindi
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Giulio Guagliumi
- Cardiovascular Department, Galeazzi San'Ambrogio Hospital, Innovation District Milan, 20157 Milan, Italy
| | - Hiram G Bezerra
- Interventional Cardiology Center, Heart and Vascular Institute, University of South Florida, Tampa, FL 33606, USA
| | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
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