HRCT characteristics of severe emphysema patients: Interobserver variability among expert readers and comparison with quantitative software

Abstract Background and study aims: Endoscopy plays an essential role in managing patients with ulcerative colitis (UC), as it allows us to visualize and assess the severity of the disease. As such assessments are not always objective, different scores have been devised to standardize the findings. The main aim of this study was to assess the interobserver variability between the Mayo Endoscopy Score (MES), Ulcerative Colitis Endoscopy Index of Severity (UCEIS) and Ulcerative Colitis Colonoscopy Index of Severity (UCCIS) analyzing the severity of the endoscopic lesions in patients with ulcerative colitis. Patients and methods: This was a single-cohort observational study in which a colonoscopy was carried out on patients with UC, as normal clinical practice, and a video was recorded. The results from the video were classified according to the MES, UCEIS and UCCIS by three endoscopic specialists independently, and they were compared to each other. The Mayo Endoscopy Score (MES) was used to assess the clinical situation of the patient. The therapeutic impact was analyzed after colonoscopy was carried out. Results: Sixty-seven patients were included in the study. The average age was 51 (SD ± 16.7) and the average MES was 3.07 (SD ± 2.54). The weighted Kappa index between endoscopists A and B for the MES was 0.8; between A and C 0.52; and between B and C 0.49. The intraclass correlation coefficient for UCEIS was 0.92 among the three endoscopists (CI 95 %: 0.83–0.96) and 0.96 for UCCIS among the three endoscopists (CI 95 % 0.94–0.97). A change in treatment for 34.3 % of the patients was implemented on seeing the results of the colonoscopy. Conclusions: There was an adequate, but not perfect, correlation between the different endoscopists for MES, UCEIS, UCCIS. This was higher with the last two scores. Thus, there is still some subjectivity to be minimized through special training, on assessing the seriousness of the endoscopic lesions in patients with UC.

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M-BLUE protocol for coronavirus disease-19 (COVID-19) patients: interobserver variability and correlation with disease severity

Clinical Radiology ◽

10.1016/j.crad.2021.02.003 ◽

2021 ◽

Vol 76 (5) ◽

pp. 379-383

Author(s):

H. Xue ◽

C. Li ◽

L. Cui ◽

C. Tian ◽

S. Li ◽

...

Keyword(s):

Disease Severity ◽

Interobserver Variability

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Interobserver variability in target volume delineation in definitive radiotherapy for thoracic esophageal cancer: a multi-center study from China

Radiation Oncology ◽

10.1186/s13014-020-01691-4 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Xiao Chang ◽

Wei Deng ◽

Xin Wang ◽

Zongmei Zhou ◽

Jun Yang ◽

...

Keyword(s):

Esophageal Cancer ◽

Gross Tumor Volume ◽

Tumor Volume ◽

Interobserver Variability ◽

Target Volume ◽

Thoracic Esophageal Cancer ◽

Target Volume Delineation ◽

Cancer Centers ◽

Definitive Radiotherapy ◽

Volume Delineation

Abstract Purpose To investigate the interobserver variability (IOV) in target volume delineation of definitive radiotherapy for thoracic esophageal cancer (TEC) among cancer centers in China, and ultimately improve contouring consistency as much as possible to lay the foundation for multi-center prospective studies. Methods Sixteen cancer centers throughout China participated in this study. In Phase 1, three suitable cases with upper, middle, and lower TEC were chosen, and participants were asked to contour a group of gross tumor volume (GTV-T), nodal gross tumor volume (GTV-N) and clinical target volume (CTV) for each case based on their routine experience. In Phase 2, the same clinicians were instructed to follow a contouring protocol to re-contour another group of target volume. The variation of the target volume was analyzed and quantified using dice similarity coefficient (DSC). Results Sixteen clinicians provided routine volumes, whereas ten provided both routine and protocol volumes for each case. The IOV of routine GTV-N was the most striking in all cases, with the smallest DSC of 0.37 (95% CI 0.32–0.42), followed by CTV, whereas GTV-T showed high consistency. After following the protocol, the smallest DSC of GTV-N was improved to 0.64 (95% CI 0.45–0.83, P = 0.005) but the DSC of GTV-T and CTV remained constant in most cases. Conclusion Variability in target volume delineation was observed, but it could be significantly reduced and controlled using mandatory interventions.

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Interobserver Variability of Target Volumes Delineated in the Supine and Prone Positions Based on Computed Tomography Images for External-Beam Partial Breast Irradiation after Breast-Conserving Surgery: A Comparative Study

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2020.07.1154 ◽

2020 ◽

Vol 108 (3) ◽

pp. e75

Author(s):

T. Yu

Keyword(s):

Computed Tomography ◽

Comparative Study ◽

Interobserver Variability ◽

Breast Conserving Surgery ◽

Partial Breast Irradiation ◽

External Beam ◽

Breast Irradiation ◽

Computed Tomography Images ◽

Target Volumes

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Interobserver variability in the classification of childhood maculopapular cutaneous mastocytosis

Journal of the European Academy of Dermatology and Venereology ◽

10.1111/jdv.17345 ◽

2021 ◽

Author(s):

Antonio Torrelo ◽

Laura Vergara‐de‐la‐Campa ◽

José Manuel Azaña ◽

Shoshana Greenberger ◽

Joseph M. Lam ◽

...

Keyword(s):

Interobserver Variability ◽

Cutaneous Mastocytosis

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Automated analysis of 3D-echocardiography using spatially registered patient-specific CMR meshes

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.425 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

D Zhao ◽

E Ferdian ◽

GD Maso Talou ◽

GM Quill ◽

K Gilbert ◽

...

Keyword(s):

New Zealand ◽

Interobserver Variability ◽

Ground Truth ◽

Automated Analysis ◽

3D Echocardiography ◽

Training Data ◽

Patient Specific ◽

Manual Analysis ◽

Lv Mass ◽

3D Echo

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation. We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole. 100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated. Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance. (n = 20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

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