Publications by authors named "N V Ledikhova"

3 Publications

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

Comput Methods Programs Biomed 2021 Apr 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
April 2021

Re: Controversy in coronaViral Imaging and Diagnostics (COVID).

Clin Radiol 2020 11 22;75(11):871-872. Epub 2020 Aug 22.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russia.

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http://dx.doi.org/10.1016/j.crad.2020.07.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442928PMC
November 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