Publications by authors named "I A Blokhin"

29 Publications

A phantom study to optimise the automatic tube current modulation for chest CT in COVID-19.

Eur Radiol Exp 2021 05 28;5(1):21. Epub 2021 May 28.

Radiation Protection of Patients Unit, International Atomic Energy Agency, Vienna, Austria.

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic. The expert organisations recommend more cautious use of thoracic computed tomography (CT), opting for low-dose protocols. We aimed at determining a threshold value of automatic tube current modulation noise index below which there is a chance to miss an onset of ground-glass opacities (GGO) in COVID-19. A team of radiologists and medical physicists performed 25 phantom CT studies using different automatic tube current modulation settings (Exposure3D technology). We then conducted a retrospective evaluation of the chest CT images from 22 patients with COVID-19 and calculated the density difference between the GGO and unaffected tissue. Finally, the results were matched to the phantom study results to determine the minimum noise index threshold value. The minimum density difference at the onset of COVID-19 was 252 HU (p < 0.001). This was found to correspond to the Exposure 3D noise index of 36. We established the noise index threshold of 36 for the Canon scanner without iterative reconstructions, allowing for a decrease in the dose-length product by 80%. The proposed protocol needs to be validated in a prospective study.
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http://dx.doi.org/10.1186/s41747-021-00218-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159722PMC
May 2021

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

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

Med Image Anal 2021 07 1;71:102054. Epub 2021 Apr 1.

Skolkovo Institute of Science and Technology, Moscow, Russia. Electronic address:

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.
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http://dx.doi.org/10.1016/j.media.2021.102054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015379PMC
July 2021

Alcohol use disorder causes global changes in splicing in the human brain.

Transl Psychiatry 2021 01 5;11(1). Epub 2021 Jan 5.

Center for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.

Alcohol use disorder (AUD) is a widespread disease leading to the deterioration of cognitive and other functions. Mechanisms by which alcohol affects the brain are not fully elucidated. Splicing constitutes a nuclear process of RNA maturation, which results in the formation of the transcriptome. We tested the hypothesis as to whether AUD impairs splicing in the superior frontal cortex (SFC), nucleus accumbens (NA), basolateral amygdala (BLA), and central nucleus of the amygdala (CNA). To evaluate splicing, bam files from STAR alignments were indexed with samtools for use by rMATS software. Computational analysis of affected pathways was performed using Gene Ontology Consortium, Gene Set Enrichment Analysis, and LncRNA Ontology databases. Surprisingly, AUD was associated with limited changes in the transcriptome: expression of 23 genes was altered in SFC, 14 in NA, 102 in BLA, and 57 in CNA. However, strikingly, mis-splicing in AUD was profound: 1421 mis-splicing events were detected in SFC, 394 in NA, 1317 in BLA, and 469 in CNA. To determine the mechanism of mis-splicing, we analyzed the elements of the spliceosome: small nuclear RNAs (snRNAs) and splicing factors. While snRNAs were not affected by alcohol, expression of splicing factor heat shock protein family A (Hsp70) member 6 (HSPA6) was drastically increased in SFC, BLA, and CNA. Also, AUD was accompanied by aberrant expression of long noncoding RNAs (lncRNAs) related to splicing. In summary, alcohol is associated with genome-wide changes in splicing in multiple human brain regions, likely due to dysregulation of splicing factor(s) and/or altered expression of splicing-related lncRNAs.
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http://dx.doi.org/10.1038/s41398-020-01163-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790816PMC
January 2021

Quantitative parameters of MRI and F-FDG PET/CT in the prediction of breast cancer prognosis and molecular type: an original study.

Am J Nucl Med Mol Imaging 2020 15;10(6):279-292. Epub 2020 Dec 15.

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow Moscow 109029, Russia.

The purpose of this work is to evaluate the quantitative parameters of magnetic resonance imaging (MRI), particularly diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) as well as positron-emission tomography, combined with computer tomography (PET/CT), with F-fluorodesoxyglucose, in the prediction of breast cancer molecular type. We studied the correlation between a set of parameters in the invasive ductal carcinoma of the breast, not otherwise specified (IDC-NOS) as it is the most common invasive breast tumor. The parameters were as follows: apparent diffusion coefficient (ADC) in DWI, positive enhancement integral (PEI) in DCE, maximum standardized uptake value (SUVmax) in F-FDG PET/CT, tumor size, grade, and Ki-67 index, level of lymph node metastatic lesions. We also evaluated the probability of a statistically significant difference in mean ADC, PEI, and SUVmax values for patient groups with different Nottingham prognostic index (NPI) and molecular tumor type. Statistically significant correlations between SUVmax, tumor size, and NPI, mean and minimal ADC values with Ki-67 and molecular tumor type were found. The PEI showed a correlation with the NPI risk level and was characterized by a relationship with the magnitude of the predicted NPI risk and regional lymph node involvement. The prognostic model created in our work allows for NPI risk group prediction. The SUVmax, ADC and PEI are non-invasive prognostic markers in the invasive breast cancer of no specific type. The correlation between ADC values and the expression of some tumor receptors can be used for in vivo molecular tumor type monitoring and treatment adjustment.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724282PMC
December 2020