Comparison of visual assessment and computer image analysis of intracoronary thrombus type by optical coherence tomography

RESEARCH ARTICLE Comparison of visual assessment and computer image analysis of intracoronary thrombus type by optical coherence tomography Timo P. Kaivosoja 1 , Shengnan Liu 2 , Jouke Dijkstra 2 , Heini Huhtala 3 , Tej Sheth 4 , Olli A. KajanderID 1 * 1Heart Hospital, Tampere University Hospital and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland, 2Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, 3Faculty of Social Sciences, University of Tampere, Tampere, Finland, 4McMaster University and Population Health Research Institute, Hamilton Health Sciences, Hamilton, Canada *[email protected] Abstract Background Analysis of intracoronary thrombus type by optical coherence tomography (OCT) imaging is highly subjective. We aimed to compare a newly developed image analysis method to subjective visual classification of thrombus type identified by OCT. Methods Thirty patients with acute ST elevation myocardial infarction were included. Thrombus type visually classified by two independent readers was compared with analysis using QCUCMS software. Results Repeatability of the computer-based measurements was good. By using a ROC, area under curve values for discrimination of white and red thrombi were 0.92 (95% confidence intervals (CI) 0.83–1.00) for median attenuation, 0.96 (95% CI 0.89–1.00) for mean backscatter and 0.96 (95% CI 0.89–1.00) for mean grayscale intensity. Median attenuation of 0.57 mm -1 (sensitivity 100%, specificity 71%), mean backscatter of 5.35 (sensitivity 92%, specificity 94%) and mean grayscale intensity of 120.1 (sensitivity 85%, specificity 100%) were identified as the best cut-off values to differentiate between red and white thrombi. Conclusions Attenuation, backscatter and grayscale intensity of thrombi in OCT images differentiated red and white thrombi with high sensitivity and specificity. Measurement of these continuous parameters can be used as a less user-dependent method to characterize in vivo thrombi. The clinical significance of these findings needs to be tested in further studies. PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kaivosoja TP, Liu S, Dijkstra J, Huhtala H, Sheth T, Kajander OA (2018) Comparison of visual assessment and computer image analysis of intracoronary thrombus type by optical coherence tomography. PLoS ONE 13(12): e0209110. https:// doi.org/10.1371/journal.pone.0209110 Editor: Elisabetta Ricottini, Campus Bio-Medico University of Rome, ITALY Received: August 21, 2018 Accepted: November 28, 2018 Published: December 17, 2018 Copyright: ©2018 Kaivosoja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: OAK received academic research funding from The competitive research fund of Pirkanmaa Hospital District, Finland, www.tays.fi/en-US. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Introduction Intracoronary thrombus is a frequent finding in patients with acute coronary syndromes undergoing invasive angiography and percutaneous coronary intervention (PCI). Research on the clinical significance of thrombus type is ongoing, and with more detailed understanding of the thrombotic process, the goal is to facilitate the development of tailored therapies for patients with acute coronary syndromes. Optical coherence tomography (OCT) is a high-resolution intravascular imaging technique with excellent contrast between the vessel lumen and intravascular structures [1]. In a consensus paper, the evidence level for image interpretation of intracoronary thrombus using OCT has been considered to be high [2]. The ability of OCT to produce images allowing differentiation between thrombus types has been validated against histology in post-mortem samples of human coronary arteries [3]. It is widely accepted in the OCT community that thrombi in OCT images can be classified as high-backscattering with a signal-free shadowing (‘red’, erythrocyte-rich) or low-backscattering (‘white’, thrombocyterich) [2]. This encourages further validation in clinical patients. However, valid analysis of different thrombus types in vivo by OCT is hindered by the high degree of subjectivity of measurement and the lack of validated methodology. The aim of the present study is to develop a computer image analysis -based method for the assessment of thrombus morphology in OCT images that can be used for further in vivo validation. Computer algorithms determining backscatter, attenuation and intensity of the OCT signal are tested and compared to the present standard method, which is consensus classification of thrombus type by two independent analysts. Our hypothesis is that applying computer image analysis to assess thrombus type is feasible and can reduce observer variation in comparison to subjective evaluation. Patients and methods Patients The study was approved by the Ethical board at Pirkanmaa Hospital District, approval number R10131. Written informed consent was obtained from all participants. The studied series included 30 patients enrolled in an OCT substudy of the TOTAL trial. The TOTAL trial was an international, multicentre, randomized trial of routine thrombectomy (using the Export catheter, Medtronic Cardiovascular, Santa Rosa, CA, USA) compared with PCI alone in STEMI patients treated with PCI (n = 10732) [4,5]. A prospective OCT sub-study (n = 214) of TOTAL trial evaluated thrombus burden in patients with symptoms of myocardial ischaemia lasting for �30 min and definite electrocardiographic changes indicating STEMI who were referred for primary PCI and randomized within 12 h of symptom onset [6]. Participation in the study required restoration of TIMI 2–3 flow after the first device. Patients were excluded from the OCT sub-study if they were in cardiogenic shock or had known renal failure. Informed consent was obtained from all individual participants included in the study. Of the patients in the OCT substudy who were randomized to thrombectomy, 72 had good quality OCT pullbacks available preceding balloon predilatation. Out of these, 30 patients with the largest maximal thrombus areas were selected for the present study (for detailed patient flow-chart, see S1 Fig). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 2 / 15 OCT imaging OCT imaging was performed using the Ilumien OCT system and C7 Dragonfly catheter (St Jude Medical, Minnesota, MN, USA). The radio-opaque distal marker of the OCT catheter was positioned 1–2 cm distal to the target lesion or stent and contrast was injected either manually or by automatic injector, according to local practice. Adequate image quality was checked and additional images were obtained, if the image quality was sub-optimal. OCT imaging raw data was exported in digital format for off-line analysis. Optical coherence tomography image analysis Data on thrombus cross-sectional areas at the culprit lesion was obtained from previous analysis (6). For each patient, three OCT cross-sections with the largest thrombus areas were analyzed. Visual assessment of thrombus type Visual thrombus characteristics were evaluated by two independent readers (T.P.K. and O.A.K.) to assess the inter-observer reproducibility. One observer (T.P.K.) repeated the analysis after >4 week period to assess the intra-observer reproducibility. Thrombus was graded by a six-stage scale, where value 1 represents purely white thrombus and value 6 purely red thrombus, creating thrombus attenuation score (TAS). In accordance to published standards [1,2], thrombus was considered white or mostly white, when the abluminal border of the thrombus or the vessel wall behind it could be seen. Conversely, the more the thrombus caused shadowing and poor visibility of the structures behind it, the redder it was scored. In addition, we applied only two additional rules. If a cross-section contained multiple fragments with different visual appearances, TAS was defined for the largest fragment. In the case of multiple same-sized thrombi, TAS was defined for the most strongly attenuating part of the thrombus. Computer image analysis of thrombus A special version of QCU-CMS version 4.69 software (Leiden University Medical Center, Leiden, The Netherlands) was used by two independent analysts. First, the thrombus areas were manually traced. The software then determined attenuation coefficient, backscatter term and grayscale intensity values for these areas and calculated several statistical parameters, including median, mean, standard deviation and 5 th , 10 th , 90 th and 95 th percentile values. Two patient cases with white and red thrombus showing the method are shown in Fig 1. To avoid the inclusion of noise pixels and dark areas (low attenuation) at the abluminal side of the thrombus, we experimented with different minimum threshold values for attenuation. For the present analyses, attenuation was analyzed using both 0.15 mm -1 and 0.5 mm -1 threshold values. The light attenuation coefficient and backscatter term have been determined based on a “depth-resolved” model [7]. The attenuation coefficient describes the total decreasing rate of light when it is traveling though the tissue. OCT images are generated by receiving the backscattered light. The backscatter coefficient describes the efficiency of tissue scattering light backwards. The backscatter term is estimated to be related to the backscatter coefficient. More information about the estimation can be referred to the technical paper [7]. The advantage of this method is its fast pixel-wise estimation, which requires no predefined delineation and is more flexible for post analysis. For each patient, a weighted average of each statistical parameter was calculated using the pixel count in each traced thrombus area. To study inter-observer reproducibility, two readers analyzed the same OCT cross-sections of 10 randomly selected patients independently. For Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 3 / 15 intra-observer comparison, one observer repeated the analyses on these 10 patients after a 4week period. For the repeated assessments, OCT raw data was re-imported into the QCUCMS software. Then the thrombus areas were manually re-traced in the same transversal frames. Statistical analysis Bland–Altman (B-A) analysis was used to estimate the bias resulting from interand intraobserver variation [8]. In the analysis, the mean values of observer 1 and 2 were plotted against the difference of measurements between the observers. The 95% limits of agreement (equal to ±1.96 SD) were determined for the variables. Mean relative difference for each variable was defined as the mean absolute difference divided by mean of observers 1 and 2. Intraclass correlation coefficients (ICCs) and their 95% confidence intervals were calculated based on absolute-agreement, 2-way mixed effect model. Spearman correlations were calculated between the continuous variables and TAS. Interand intraobserver reliability of thrombus classification was assessed using the kappa statistic (κ). Receiver-operating characteristic (ROC) curve analyses were performed to determine the best cutoff values (using highest Youden Index) of QCU-CMS parameters for discriminating red and white thrombi and the areas under the curve (AUCs), as well as the sensitivities and specificities of the diagnostic test. In addition, binary logistic regression analysis was used to evaluate how the different continuous image analysis software parameters predicted the type of thrombus. Statistical analyses were performed with the Stata Statistical software: Release 13 (StataCorp, College Station, TX, USA) and the IBM SPSS 21 Statistics software (IBM, Armonk, NY, USA). Fig 1. Examples of thrombus assessment by the QCU-CMS image analysis software in OCT image frames of two patients with white (A) and red thrombus (D). After manual tracing of the thrombi (B,E), attenuation analysis was performed including regions with attenuation values above the designated threshold value (displayed in blue) (C,F). Accurate segmentation of the luminal border of the thrombus and the contrast-filled flow area of the vessel can be seen in C and F. �) OCT catheter, #) guidewire artefact, ¤) thrombus, §) vessel wall. OCT, optical coherence tomography. https://doi.org/10.1371/journal.pone.0209110.g001 Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 4 / 15 Results Baseline patient characteristics are shown in Table 1. Qualitative (visual) thrombus classification by two observers For the purpose of statistical comparison of the thrombus assessment methods, a six-stage scale consensus TAS was used to form a binary classification, where scores 1–3 equaled white and 4–6 red thrombus. There were 13 cases classified as white and 17 as red thrombus (S2 Fig). Kappa values for binary classification of the thrombi were 0.74 and 0.53, respectively, for interand intraobserver comparisons. Reproducibility of quantitative measurements using QCU-CMS software Results presented in Tables 2and 3show interand intraobserver variability of measurements in thrombus regions. Mean differences for most statistical parameters of thrombus Table 1. Baseline patient characteristics. N = 30 Age, years (mean ±SD) 57 ±10.1 Male sex, N (%) 22 (73.3) Risk factor profile, N (%) Hypertension 14 (46.6) Diabetes 4 (13.3) Current smoking 8 (26.7) Obesity (BMI �30) 14 (46.7) Prior MI 2 (6.7) Prior PCI 1 (3.3) Creatinine, μmol/L (mean ±SD) 82.0 ±16.6 Antithrombotic treatment, N (%) Upfront glycoprotein inhibitor IIb/IIIa use 11 (36.7) UFH prior to procedure 22 (73.3) Enoxaparin prior to procedure 2 (6.7) Bivalirudin prior to procedure 0 (0.0) Culprit vessel, N (%) RCA 16 (53.3) LAD 12 (40.0) LCx 2 (6.7) TIMI flow�, N (%) 0 15 (50.0) 1 4 (13.3) 2 3 (10.0) 3 8 (26.7) TIMI thrombus class�, N (%) <3 4 (13.3) �3 26 (86.7) �prior to thrombectomy. BMI, body mass index; LAD, left anterior descending coronary artery; LCx, left circumflex coronary artery; MI, myocardial infarction; PCI, percutaneous coronary intervention; RCA, right coronary artery; SD, standard deviation; TIMI, thrombolysis in myocardial infarction; UFH, unfractionated heparin. https://doi.org/10.1371/journal.pone.0209110.t001 Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 5 / 15 attenuation, backscatter and grayscale intensity were small, with narrow limits of agreement between observers. For all tested parameters, intraobserver variability was slightly smaller than interobserver variation. Limits of agreement for skewness and kurtosis of attenuation, backscatter and grayscale intensity were wider than for the other statistical parameters. B-A plot of median attenuation is shown in Fig 2. B-A plots of mean backscatter and mean grayscale intensity, respectively, are shown in S3 and S4 Figs. Comparison of visual TAS and measurements by QCU-CMS software There was an inverse relationship between several QCU-CMS parameters of attenuation (median values, r = -0.79, p<0.001); 10 th percentile values r = -0.71, p<0.001), backscatter (mean values r = -0.78, p<0.001; 10 th percentile value r = -0.79, p<0.001) and grayscale intensity (mean values r = -0.80, p<0.001; 10 th percentile values r = -0-80, p<0.001)) and the visual TAS. Distribution of selected statistical parameters of attenuation, backscatter and grayscale intensity in the six-stage TAS is shown (Fig 3). For the distribution of the parameters using two-stage TAS, see S5 Fig. In these analyses, a minimum threshold 0.15 mm -1 was used for attenuation. Table 2. Interobserver variability of QCU-CMS measurements. Parameters Observer 1 Mean (SD) Observer 2 Mean (SD) Range Mean absolute difference (CI) Mean relative difference (%) Limits of agreement ICC (CI) Attenuation (mm -1 ) Median 0.61 (0.12) 0.57 (0.13) 0.35–0.77 0.03 (-0.02–0.08) 5.8 -0.11–0.17 0.91 (0.64–0.98) Mean 0.72 (0.09) 0.69 (0.11) 0.49–0.83 0.03 (-0.02–0.08) 4.3 -0.10–0.16 0.88 (0.55–0.97) Standard deviation 0.42 (0.09) 0.42 (0.07) 0.32–0.54 0.001 (-0.02–0.02) 0.2 -0.07–0.07 0.96 (0.84–0.99) Skewness 1.36 (0.50) 1.46 (0.40) 0.89–2.18 -0.10 (-0.26–0.06) -7.2 -0.55–0.19 0.93 (0.73–0.98) Kurtosis 3.01 (2.42) 3.72 (2.45) 0.55–7.21 -0.72 (-2.21–0.78) -21.4 -4.90–3.47 0.77 (0.14–0.94) 5 th percentile 0.26 (0.06) 0.23 (0.06) 0.17–0.38 0.02 (-0.01–0.04) 8.6 -0.04–0.08 0.90 (0.52–0.98) 10 th percentile 0.30 (0.07) 0.28 (0.07) 0.19–0.46 0.02 (-0.01–0.05) 8.3 -0.06–0.11 0.90 (0.58–0.97) 90 th percentile 1.27 (0.16) 1.24 (0.17) 1.00–1.53 0.03 (-0.03–0.09) 2.3 -0.14–0.20 0.92 (0.71–0.98) 95 th percentile 1.51 (0.20) 1.48 (0.19) 1.21–1.77 0.03 (-0.04–0.10) 1.9 -0.17–0.23 0.93 (0.75–0.98) Backscatter Median 5.18 (0.22) 5.09 (0.28) 4.77–5.49 0.10 (-0.01–0.20) 1.9 -0.20–0.40 0.87 (0.46–0.97) Mean 5.19 (0.18) 5.10 (0.23) 4.79–5.46 0.10 (0.01–0.18) 1.8 -0.15–0.34 0.86 (0.36–0.97) Standard deviation 0.61 (0.09) 0.66 (0.07) 0.47–0.72 -0.04 (-0.07–-0.02) -6.9 -0.10–0.01 0.89 (-0.09–0.98) Skewness -0.08 (0.29) -0.22 (0.44) -0.74–0.31 0.15 (-0.05–0.34) 96.7 -0.39–0.68 0.83 (0.37–0.96) Kurtosis -0.13 (0.49) 0.44 (1.24) -0.70–2.37 -0.57 (-1.16–0.02) -183 -2.22–1.08 0.70 (-0.05–0.92) 5 th percentile 4.22 (0.26) 4.07 (0.26) 3.77–4.70 0.15 (0.05–0.24) 3.5 -0.11–0.41 0.87 (0.05–0.97) 10 th percentile 4.41 (0.25) 4.28 (0.28) 3.95–4.93 0.13 (0.04–0.21) 2.9 -0.12–0.37 0.90 (0.26–0.98) 90 th percentile 5.99 (0.16) 5.94 (0.18) 5.65–6.26 0.05 (-0.02–0.11) 0.8 -0.14–0.23 0.91 (0.65–0.98) 95 th percentile 6.17 (0.16) 6.13 (0.17) 5.85–6.41 0.04 (-0.02–0.10) 0.7 -0.14–0.21 0.92 (0.69–0.98) Grayscale Median 67.19 (32.99) 63.86 (35.43) 28.16–147.83 3.32 (-2.53–9.19) 5.1 -13.05–19.71 0.99 (0.94–1.00) Mean 104.65 (26.87) 100.04 (31.37) 69.76–166.69 4.61 (-2.28–11.49) 4.5 -14.64–23.85 0.97 (0.88–0.99) Standard deviation 106.03 (17.46) 103.02 (15.95) 80.03–134.78 3.02 (-3.78–9.81) 2.9 -15.98–22.01 0.91 (0.67–0.98) Skewness 2.51 (0.88) 2.65 (0.72) 1.72–3.99 -0.14 (-0.41–0.14) -5.3 -0.91–0.63 0.94 (0.76–0.98) Kurtosis 12.49 (11.67) 14.62 (10.83) 4.31–38.17 -2.13 (-6.88–2.62) -15.7 -15.40–11.14 0.91 (0.64–0.98) 5 th percentile 16.59 (13.34) 14.74 (11.59) 6.66–48.92 1.85 (0.08–3.61) 11.8 -3.10–6.79 0.99 (0.91–1.00) 10 th percentile 21.77 (16.88) 20.14 (16.17) 8.83–65.09 1.63 (-0.02–3.27) 7.8 -2.97–6.22 0.99 (0.96–1.00) 90 th percentile 239.64 (40.18) 230.74 (48.77) 166.58–312.48 8.90 (-4.40–22.20) 3.8 -28.28–46.09 0.94 (0.80–0.99) 95 th percentile 315.19 (44.98) 305.05 (53.64) 228.63–408.65 10.15 (-6.64–26.94) 3.3 -36.78–57.08 0.94 (0.75–0.98) N = 10. CI, confidence interval; SD, standard deviation; ICC, intra-class correlation coefficient. https://doi.org/10.1371/journal.pone.0209110.t002 Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 6 / 15 The same thrombus cross-sectional areas were analyzed using a higher minimum threshold (0.5 mm -1 ) for attenuation. This resulted in exclusion of an average of 36% (minimum 5.3%, maximum 69.8%) of the pixels of each patient previously included with the 0.15 mm -1 threshold. In this analysis, only low percentiles of attenuation values (for 10 th percentile values, r = 0.59, p<0.001) had an inverse relationship with visual TAS. Median (r = -0.05, p = 0.78) or 95 th percentile values (r = 0.34, p = 0.07) of attenuation values were not statistically significantly related to TAS (S6 Fig). In addition, using ratio of attenuation and intensity measurements, a new ‘normalized’ variable was constructed. The ratio of 95 th percentile of attenuation and median intensity had a direct relationship with TAS (r = 0.86, p<0.001) (S7 Fig). ROC analysis A ROC curve analysis was applied to assess the best parameters to distinguish between different types of thrombus. The AUCs represented the ability of QCU-CMS software parameters to differentiate red and white thrombi (Table 4). The best cutoff values to diagnose red thrombus Table 3. Intraobserver variability of QCU-CMS measurements. Parameters Observer 1 first time Mean (SD) Observer 1 second time Mean (SD) Range Mean absolute difference (CI) Mean relative difference (%) Limits of agreement ICC (CI) Attenuation (mm -1 ) Median 0.61 (0.12) 0.59 (0.13) 0.37–0.75 0.02 (-0.03–0.06) 3.3 -0.11–0.14 0.93 (0.74–0.98) Mean 0.72 (0.09) 0.70 (0.11) 0.51–0.84 0.02 (-0.02–0.06) 2.8 -0.10–0.13 0.92 (0.69–0.98) Standard deviation 0.42 (0.09) 0.41 (0.08) 0.31–0.54 0.001 (-0.01–0.02) 0.2 -0.04–0.05 0.98 (0.94–1.00) Skewness 1.36 (0.50) 1.41 (0.43) 0.75–2.14 -0.05 (-0.24–0.14) -3.6 -0.58–0.48 0.92 (0.68–0.98) Kurtosis 3.01 (2.42) 3.64 (2.81) 0.52–6.99 -0.64 (-2.48–1.21) -19.2 -5.79–4.52 0.69 (-0.24–0.92) 5 th percentile 0.26 (0.06) 0.25 (0.06) 0.18–0.39 0.01 (-0.01–0.02) 3.9 -0.03–0.05 0.97 (0.90–0.99) 10 th percentile 0.30 (0.07) 0.29 (0.07) 0.20–0.45 0.01 (-0.01–0.03) 3.4 -0.05–0.06 0.97 (0.88–0.99) 90 th percentile 1.27 (0.16) 1.25 (0.17) 1.02–1.54 0.02 (-0.03–0.07) 1.6 -0.12–0.16 0.95 (0.82–0.99) 95 th percentile 1.51 (0.20) 1.48 (0.21) 1.23–1.79 0.03 (-0.03–0.08) 2.0 -0.13–0.18 0.96 (0.86–0.99) Backscatter Median 5.18 (0.22) 5.13 (0.29) 4.78–5.47 0.05 (-0.06–0.16) 1.0 -0.25–0.35 0.91 (0.65–0.98) Mean 5.19 (0.18) 5.14 (0.25) 4.80–5.46 0.04 (0.05–0.13) 0.8 -0.20–0.29 0.92 (0.69–0.98) Standard deviation 0.61 (0.09) 0.62 (0.09) 0.43–0.69 -0.01 (-0.03–-0.01) -1.6 -0.06–0.04 0.98 (0.92–1.00) Skewness -0.08 (0.29) -0.10 (0.31) -0.47–0.29 0.02 (-0.10–0.14) -22.2 -0.32–0.36 0.92 (0.68–0.98) Kurtosis -0.13 (0.49) 0.02 (0.56) -0.76–0.86 -0.15 (-0.44–0.13) -136 -0.94–0.64 0.83 (0.37–0.96) 5 th percentile 4.22 (0.26) 4.16 (0.32) 3.78–4.77 0.05 (-0.04–0.14) 1.2 -0.19–0.29 0.95 (0.82–0.99) 10 th percentile 4.41 (0.25) 4.36 (0.31) 3.96–4.94 0.05 (-0.05–0.14) 1.1 -0.21–0.30 0.95 (0.80–0.99) 90 th percentile 5.99 (0.16) 5.96 (0.19) 5.66–6.27 0.03 (-0.03–0.09) 0.5 -0.13–0.19 0.94 (0.79–0.99) 95 th percentile 6.17 (0.16) 6.14 (0.18) 5.86–6.43 0.03 (-0.02–0.08) 0.5 -0.12–0.32 0.95 (0.81–0.99) Grayscale Median 67.19 (32.99) 70.05 (36.06) 30.67–151.52 -2.86 (-8.00–2.28) -4.2 -17.23–11.51 0.99 (0.96–1.00) Mean 104.65 (26.87) 107.50 (29.72) 73.22–170.61 -2.85 (-9.16–3.45) -1.3 -20.48–14.77 0.98 (0.91–0.99) Standard deviation 106.03 (17.46) 107.19 (13.59) 91.86–136.54 -1.16 (-7.88–5.56) -1.1 -19.95–17.64 0.91 (0.63–0.98) Skewness 2.51 (0.88) 2.52 (0.70) 1.63–3.99 -0.003 (-0.33–0.32) -0.1 -0.91–0.90 0.92 (0.70–0.98) Kurtosis 12.49 (11.67) 12.84 (10.82) 3.81–38.65 -0.35 (-5.00–4.30) -2.8 -13.35–12.65 0.92 (0.66–0.98) 5 th percentile 16.59 (13.34) 17.19 (14.43) 7.17–54.29 -0.60 (-1.97–0.76) -3.6 -4.41–3.21 1.00 (0.98–1.00) 10 th percentile 21.77 (16.88) 22.96 (18.30) 9.50–69.53 -1.20 (-2.97–0.58) -5.4 -6.15–3.76 0.99 (0.98–1.00) 90 th percentile 239.64 (40.18) 244.76 (38.86) 195.65–319.26 -5.12 (-19.99–9.75) -2.1 -46.69–36.46 0.93 (0.73–0.98) 95 th percentile 315.19 (44.98) 320.95 (41.95) 273.50–414.96 -5.76 (-24.41–12.90) -1.8 -57.91–46.39 0.91 (0.63–0.98) N = 10. CI, confidence interval; SD, standard deviation; ICC, intra-class correlation coefficient. https://doi.org/10.1371/journal.pone.0209110.t003 Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 7 / 15 in each class of variables were <5.35 for mean backscatter (AUC = 0.959, sensitivity 92%, specificity 94%), <120.1 for mean grayscale intensity (AUC = 0.959, sensitivity 85%, specificity 100%), <0.57 mm -1 for median attenuation (AUC = 0.919, sensitivity 100%, specificity 71%) and >0.022 mm -1 for 95 th percentile of attenuation/median grayscale intensity (AUC = 0.950, sensitivity 88%, specificity 85%) (Fig 4). In an additional analysis, using binary logistic regression, best predictors of thrombus type were unchanged. AUCs for different statistical variables of attenuation measurements from the second analysis using a higher 0.5 mm -1 threshold were low (in range of 0.50–0.60) except for 5 th percentile and 10 th percentile of attenuation (AUCs 0.878 and 0.851, respectively). Fig 2. Intraand interobserver variability of measurement of thrombus attenuation in OCT images using image analysis software. Scatterplot (left) and BlandAltman plot (right) of intraobserver (A) and interobserver (B) comparison for median attenuation. OCT, optical coherence tomography; SD, standard deviation. https://doi.org/10.1371/journal.pone.0209110.g002 Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 8 / 15 Discussion As a first step in order to resolve the clinical significance and predictive potential of OCTbased thrombus analysis in clinical patients, a robust method to translate the traditional visual classification into reproducibly measured variables is needed. Validated methodology to assess different thrombus types by OCT imaging has not been available, and previously published data has relied on subjective analysis methods based on observers’ visual perception of the thrombus type [9,10]. In this methodological study, we measured attenuation, backscatter and grayscale intensity of in vivo thrombi using newly developed image analysis software features. For methodological validation, we assessed repeatability of the measurements and the correlation between traditional visual thrombus classification and the computer software-based analysis. We were able to demonstrate for the first time the use of image analysis software to assess in vivo thrombi in OCT images. Measurements of attenuation, backscatter and grayscale intensity of the thrombi using the software were highly repeatable and there was good agreement between two observer consensus visual evaluation of thrombus type and several parameters measured using the software on the OCT images. Visual assessment of thrombus type in OCT images is per se highly observer-dependent. There is not much previous data on the repeatability of the operator-based assessment of thrombus type and there is also a lack of standardized definitions and cut-off values differentiating red and white thrombus. We observed that the visual classification, as expected, was only moderately reproducible. Of note, the present analysis was performed off-line by experienced analysts and reproducibility outside the OCT core lab setting is most probably even lower. In the present study, we showed that computer-based image analysis of in vivo thrombus can be Fig 3. Relationship of thrombus attenuation score and parameters measured by image analysis software in thrombus areas in OCT images. Scatterplots for median attenuation (A), 10 th percentile of attenuation (B), mean backscatter (C), 10 th percentile of backscatter (D), mean grayscale intensity (E) and 10 th percentile of grayscale intensity (F). OCT, optical coherence tomography. https://doi.org/10.1371/journal.pone.0209110.g003 Analysis of intracoronary thrombus by OCT PLOS ONE | https://doi.org/10.1371/journal.pone.0209110 December 17, 2018 9 / 15