Publications by authors named "A V Vladzymyrskyy"

12 Publications

A simplified cluster model and a tool adapted for collaborative labeling of lung cancer CT scans.

Comput Methods Programs Biomed 2021 Jul 18;206:106111. Epub 2021 Apr 18.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Petrovka str., 24, Moscow, 127051, Russia; Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Vavilova str., 44/2, Moscow, 119333, Russia. Electronic address:

Background And Objective: Lung cancer is the most common type of cancer with a high mortality rate. Early detection using medical imaging is critically important for the long-term survival of the patients. Computer-aided diagnosis (CAD) tools can potentially reduce the number of incorrect interpretations of medical image data by radiologists. Datasets with adequate sample size, annotation, and truth are the dominant factors in developing and training effective CAD algorithms. The objective of this study was to produce a practical approach and a tool for the creation of medical image datasets.

Methods: The proposed model uses the modified maximum transverse diameter approach to mark a putative lung nodule. The modification involves the possibility to use a set of overlapping spheres of appropriate size to approximate the shape of the nodule. The algorithm embedded in the model also groups the marks made by different readers for the same lesion. We used the data of 536 randomly selected patients of Moscow outpatient clinics to create a dataset of standard-dose chest computed tomography (CT) scans utilizing the double-reading approach with arbitration. Six volunteer radiologists independently produced a report for each scan using the proposed model with the main focus on the detection of lesions with sizes ranging from 3 to 30 mm. After this, an arbitrator reviewed their marks and annotations.

Results: The maximum transverse diameter approach outperformed the alternative methods (3D box, ellipsoid, and complete outline construction) in a study of 10,000 computer-generated tumor models of different shapes in terms of accuracy and speed of nodule shape approximation. The markup and annotation of the CTLungCa-500 dataset revealed 72 studies containing no lung nodules. The remaining 464 CT scans contained 3151 lesions marked by at least one radiologist: 56%, 14%, and 29% of the lesions were malignant, benign, and non-nodular, respectively. 2887 lesions have the target size of 3-30 mm. Only 70 nodules were uniformly identified by all the six readers. An increase in the number of independent readers providing CT scans interpretations led to an accuracy increase associated with a decrease in agreement. The dataset markup process took three working weeks.

Conclusions: The developed cluster model simplifies the collaborative and crowdsourced creation of image repositories and makes it time-efficient. Our proof-of-concept dataset provides a valuable source of annotated medical imaging data for training CAD algorithms aimed at early detection of lung nodules. The tool and the dataset are publicly available at https://github.com/Center-of-Diagnostics-and-Telemedicine/FAnTom.git and https://mosmed.ai/en/datasets/ct_lungcancer_500/, respectively.
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http://dx.doi.org/10.1016/j.cmpb.2021.106111DOI Listing
July 2021

Chest MRI of patients with COVID-19.

Magn Reson Imaging 2021 06 13;79:13-19. Epub 2021 Mar 13.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia. Electronic address:

During the pandemic of novel coronavirus infection (COVID-19), computed tomography (CT) showed its effectiveness in diagnosis of coronavirus infection. However, ionizing radiation during CT studies causes concern for patients who require dynamic observation, as well as for examination of children and young people. For this retrospective study, we included 15 suspected for COVID-19 patients who were hospitalized in April 2020, Russia. There were 4 adults with positive polymerase chain reaction (PCR) test for COVID-19. All patients underwent magnetic resonance imaging (MRI) examinations using MR-LUND PROTOCOL: Single-shot Fast Spin Echo (SSFSE), LAVA 3D and IDEAL 3D, Echo-planar imaging (EPI) diffusion-weighted imaging (DWI) and Fast Spin Echo (FSE) T2 weighted imaging (T2WI). On T2WI changes were identified in 9 (60,0%) patients, on DWI - in 5 (33,3%) patients. In 5 (33,3%) patients lesions of the parenchyma were visualized on T2WI and DWI simultaneously. At the same time, 4 (26.7%) patients had changes in lung tissue only on T2WI. (P(McNemar) = 0,125; OR = 0,00 (95%); kappa = 0,500). In those patients who had CT scan, the changes were comparable to MRI. The results showed that in case of CT is not available, it is advisable to conduct a chest MRI for patients with suspected or confirmed COVID-19. Considering that T2WI is a fluid-sensitive sequence, if imaging for the lung infiltration is required, we can recommend the abbreviated MRI protocol consisting of T2 and T1 WI. These data may be applicable for interpreting other studies, such as thoracic spine MRI, detecting signs of viral pneumonia of asymptomatic patients. MRI can detect features of viral pneumonia.
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http://dx.doi.org/10.1016/j.mri.2021.03.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955570PMC
June 2021

[Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks].

Probl Endokrinol (Mosk) 2020 Oct 24;66(5):48-60. Epub 2020 Oct 24.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies.

Background: Pathological low-energy (LE) vertebral compression fractures (VFs) are common complications of osteoporosis and predictors of subsequent LE fractures. In 84% of cases, VFs are not reported on chest CT (CCT), which calls for the development of an artificial intelligence-based (AI) assistant that would help radiology specialists to improve the diagnosis of osteoporosis complications and prevent new LE fractures.

Aims: To develop an AI model for automated diagnosis of compression fractures of the thoracic spine based on chest CT images.

Materials And Methods: Between September 2019 and May 2020 the authors performed a retrospective sampling study of ССТ images. The 160 of results were selected and anonymized. The data was labeled by seven readers. Using the morphometric analysis, the investigators received the following metric data: ventral, medial and dorsal dimensions. This was followed by a semiquantitative assessment of VFs degree. The data was used to develop the Comprise-G AI mode based on CNN, which subsequently measured the size of the vertebral bodies and then calculates the compression degree. The model was evaluated with the ROC curve analysis and by calculating sensitivity and specificity values.

Results: Formed data consist of 160 patients (a training group - 100 patients; a test group - 60 patients). The total of 2,066 vertebrae was annotated. When detecting Grade 2 and 3 maximum VFs in patients the Comprise-G model demonstrated sensitivity - 90,7%, specificity - 90,7%, AUC ROC - 0.974 on the 5-FOLD cross-validation data of the training dataset; on the test data - sensitivity - 83,2%, specificity - 90,0%, AUC ROC - 0.956; in vertebrae demonstrated sensitivity - 91,5%, specificity - 95,2%, AUC ROC - 0.981 on the cross-validation data; for the test data sensitivity - 79,3%, specificity - 98,7%, AUC ROC - 0.978.

Conclusions: The Comprise-G model demonstrated high diagnostic capabilities in detecting the VFs on CCT images and can be recommended for further validation.
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http://dx.doi.org/10.14341/probl12605DOI Listing
October 2020

Diffusion processes modeling in magnetic resonance imaging.

Insights Imaging 2020 Apr 28;11(1):60. Epub 2020 Apr 28.

Central Institute of Traumatology and Orthopaedics named after N. N. Priorov, 10, ul. Priorova, Moscow, 127299, Russia.

Background: The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied.

Material And Method: To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T: cyclomethicone and caprylyl methicone. For quantitative assessment of our phantom, we performed DWI on 1.5T magnetic resonance scanner with various fat suppression techniques. We assessed water-in-oil emulsion as an extracorporeal source signal by simultaneously scanning a patient in whole-body DWI sequence.

Results: We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm/s. The ADC values of polymer solutions are well relevant to the mono-exponential equation with the mean relative difference of 0.91%.

Conclusion: The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.
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http://dx.doi.org/10.1186/s13244-020-00863-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188746PMC
April 2020

Telemedicine-based system for quality management and peer review in radiology.

Insights Imaging 2018 Jun 18;9(3):337-341. Epub 2018 May 18.

Research and Practical Center of Medical Radiology, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Objectives: Quality assurance is the key component of modern radiology. A telemedicine-based quality assurance system helps to overcome the "scoring" approach and makes the quality control more accessible and objective.

Methods: A concept for quality assurance in radiology is developed. Its realization is a set of strategies, actions, and tools. The latter is based on telemedicine-based peer review of 23,199 computed tomography (CT) and magnetic resonance imaging (MRI) images.

Results: The conception of the system for quality management in radiology represents a chain of actions: "discrepancies evaluation - routine support - quality improvement activity - discrepancies evaluation". It is realized by an audit methodology, telemedicine, elearning, and other technologies. After a year of systemic telemedicine-based peer reviews, the authors have estimated that clinically significant discrepancies were detected in 6% of all cases, while clinically insignificant ones were found in 19% of cases. Most often, problems appear in musculoskeletal records; 80% of the examinations have diagnostic or technical imperfections. The presence of routine telemedicine support and personalized elearning allowed improving the diagnostics quality. The level of discrepancies has decreased significantly (p < 0.05).

Conclusion: The telemedicine-based peer review system allows improving radiology departments' network effectiveness.

Main Messages: • "Scoring" approach to radiologists' performance assessment must be changed. • Telemedicine peer review and personalized elearning significantly decrease the number of discrepancies. • Teleradiology allows linking all primary-level hospitals to a common peer review network.
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http://dx.doi.org/10.1007/s13244-018-0629-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991002PMC
June 2018