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Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

ABSTRACT: BACKGROUND:The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases. PURPOSE:To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans. MATERIALS AND METHODS:Three fully automated machine learning algorithms were used to assess CCS, level of liver steatosis and emphysema percentage in the lung. The algorithms were used on low-dose computed tomography scans acquired from 12,332 participants in NLST. RESULTS:In a multivariate analysis, association between the three algorithm scores and CVD mortality have shown an OR of 1.72 (p = 0.003), 2.62 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively, and an OR of 1.12 (p = 0.044) for level of liver steatosis. Similar results were shown for the incidence of CVD, OR of 1.96 (p < 0.0001), 4.94 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively. Also, emphysema percentage demonstrated an OR of 0.89 (p < 0.0001). Similar results are shown for univariate analyses of the algorithms. CONCLUSION:The three automated machine learning algorithms could help physicians to assess the incidence and risk of CVD mortality in this specific population. Application of these algorithms to existing LDCT scans can provide valuable health care information and assist in future research.

SUBMITTER: Stemmer A

PROVIDER: S-EPMC7398499 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Stemmer Amos A Shadmi Ran R Bregman-Amitai Orna O Chettrit David D Blagev Denitza D Orlovsky Mila M Deutsch Lisa L Elnekave Eldad E

PloS one 20200803 8

<h4>Background</h4>The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases.<h4>Purpose</h4>To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiova ...[more]

PMID: 32745082

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Project description:BackgroundPancreatic perfusion computed tomography (CT) imaging is increasingly used for neoplastic grading, predicting prognosis, and evaluating the response to therapy. To optimize the clinical pancreatic CT perfusion imaging methods, we evaluated 2 different CT scanning protocols concerning pancreas perfusion parameters.MethodsA retrospective study was conducted on 40 patients who underwent whole pancreas CT perfusion scanning in The First Affiliated Hospital of Zhengzhou University. Of these 40 patients, 20 patients in group A underwent continuous perfusion scanning, while 20 patients in group B underwent intermittent perfusion scanning. For group A, continuous axial scanning was performed 25 times, and the total scan time was 50 s. For group B, arterial phase helical perfusion scanning was performed 8 times, and then venous phase helical perfusion scanning was performed 15 times, with a total scan time of 64.6 to 70.0 s. A comprehensive list of perfusion parameters between different parts of the pancreas and the 2 groups were compared. The effective radiation dose for the 2 scanning methods was analyzed.ResultsThe parameter of the mean slope of increase (MSI) at different pancreatic parts in group A differed (P=0.028). The pancreas head had the lowest value, and the tail had the highest (about a 20% difference). In group A compared to group B, the blood volume of the pancreatic head was smaller (15.256±2.925 vs. 16.953±3.602), the positive enhanced integral was smaller (0.307±0.050 vs. 0.344±0.060) and the permeability surface was larger (34.205±9. 612 vs. 24.377±8.413); the blood volume of the pancreatic neck was smaller (13.940±2.691 vs. 17.173±3.918), the positive enhanced integral was smaller (0.304±0.088 vs. 0.361±0.051) and the permeability surface was larger (34.898±11.592 vs. 25. 794±8.149); the blood volume of the pancreatic body was smaller (16.142±4.006 vs. 18.401±2.513), the positive enhanced integral was smaller (0.305±0.093 vs. 0.342±0.048) and the permeability surface was larger (28.861±10.448 vs. 22.158±6. 017); the blood volume of the pancreatic tail was smaller (16.446±3.709 vs. 17.374±3.781), the positive enhanced integral was smaller (0.304±0.057 vs. 0.350±0.073) and the permeability surface was larger (27.823±8.228 vs. 21.509±7.768) (P<0.05). The effective radiation dose in the intermittent scan mode was slightly lower at 16.657±2.259 mSv than in the continuous scan mode (17.973±3.698 mSv).ConclusionsDifferent CT scanning intervals had a significant influence on whole pancreas blood volume, permeability surface, and positive enhanced integral. These demonstrate the high sensitivity of intermittent perfusion scanning for identifying perfusion abnormalities. Therefore, for the diagnosis of pancreatic diseases, intermittent pancreatic CT perfusion may be more advantageous.

Project description:Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain ([Formula: see text], [Formula: see text]). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of [Formula: see text]. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with 10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for quantifying cardiac mechanics.

Project description:ImportanceThe American College of Radiology (ACR) has recognized the importance of minimizing radiation doses used for lung cancer screening (LCS) computed tomography (CT). However, without standard protocols, doses could still be unnecessarily high, reducing screening margin of benefit.ObjectiveTo characterize LCS CT radiation doses and identify factors explaining variation.Design, setting, and participantsWe prospectively collected LCS examination dose metrics, from 2016 to 2017, at US institutions in the University of California, San Francisco International Dose Registry. Institution-level factors were collected through baseline survey. Mixed-effects linear and logistic regression models were estimated using forward variable selection. Results are presented as percentage excess dose and odds ratios (ORs) with 95% confidence intervals (CIs). The analysis was conducted between 2018 and 2019.Main outcomes and measuresLog-transformed measures of (1) mean volume CT dose index (CTDIvol, mGy), reflecting the average radiation dose per slice; (2) mean effective dose (ED, mSv), reflecting the total dose received and estimated future cancer risk; (3) proportion of CT scans using radiation doses above ACR benchmarks (CTDIvol >3 mGy, ED >1 mSv); and (4) proportion of CT scans using radiation doses above 75th percentile of registry doses (CTDIvol >2.7 mGy, ED >1.4 mSv).ResultsData were collected for 12 529 patients undergoing LCS CT scans performed at 72 institutions. Overall, 7232 participants (58%) were men, and the median age was 65 years (interquartile range [IQR], 60-70). Of 72 institutions, 15 (21%) had median CTDIvol and 47 (65%) had median ED above ACR guidelines. Institutions allowing any radiologists to establish protocols had 44% higher mean CTDIvol (mean dose difference [MDD], 44%; 95% CI, 19%-69%) and 27% higher mean ED (MDD, 27%; 95% CI, 5%-50%) vs those limiting who established protocols. Institutions allowing any radiologist to establish protocols had higher odds of examinations exceeding ACR CTDIvol guidelines (OR, 12.0; 95% CI, 2.0-71.4), and 75th percentile of registry CTDIvol (OR, 19.0; 95% CI, 1.9-186.7) or ED (OR, 8.5; 95% CI, 1.7-42.9). Having lead radiologists establish protocols resulted in lower odds of doses exceeding ACR ED guidelines (OR, 0.01; 95% CI, 0.001-0.1). Employing external vs internal medical physicists was associated with increased odds of exceeding ACR CTDIvol guidelines (OR, 6.1; 95% CI, 1.8-20.8). Having medical physicists establish protocols was associated with decreased odds of exceeding 75th percentile of registry CTDIvol (OR, 0.09; 95% CI, 0.01-0.59). Institutions reporting protocol updates as needed had 27% higher mean CTDIvol (MDD, 27%; 95% CI, 8%-45%).Conclusions and relevanceFacilities varied in LCS CT radiation dose distributions. Institutions limiting protocol creation to lead radiologists and having internal medical physicists had lower doses.

Dataset Information

Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Publications

Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

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