Characterization of mesothelioma and tissues present in contrast-enhanced thoracic CT scans.
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ABSTRACT:
SUBMITTER: Corson N
PROVIDER: S-EPMC3041808 | biostudies-other | 2011 Feb
REPOSITORIES: biostudies-other
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Project description:Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.
| S-EPMC11291756 | biostudies-literature
Project description:BACKGROUND:18F-FDG PET/CT is a key molecular imaging modality to noninvasively assess and differentiate benign and malignant cardiac tumors. However, few benign cardiac tumors can be characterized by increased 18F-FDG uptake, which makes differential diagnosis difficult. This study sought to retrospectively evaluate whether combined 18F-FDG PET/CT with thoracic contrast-enhanced CT (CECT) helps in assessing primary cardiac tumors in adult patients, compared with CECT or PET/CT alone. METHODS:Forty-six consecutive patients who were diagnosed as primary cardiac tumors were enrolled. All patients underwent 18F-FDG PET/CT followed by thoracic CECT before biopsy or surgery. Visual qualitative interpretation and quantitative analysis were performed, and diagnostic performance was evaluated. RESULTS:More than half (16/29) of benign tumors exhibited with mild 18F-FDG uptake. There were significant differences in 18F-FDG uptake and the degree of absolute enhancement between benign and malignant tumors (P < 0.001). The combination of two modalities improved the specificity from 79 to 93%, the positive predictive value from 73 to 89%, and the accuracy of diagnosis from 85 to 93%. There were significant differences between PET/CT alone or thoracic CECT alone and combined modalities (P = 0.034 and P = 0.026, respectively). The combination with the optimal SUVmax cutoff value generated 94% sensitivity, 100% specificity, 97% negative predictive values, 100% positive predictive values, and 98% accuracy rates. CONCLUSIONS:Combining 18F-FDG PET/C with thoracic CECT significantly improved specificity and accuracy compared to CECT or PET/CT alone in detecting tumors. This combination of diagnostic imaging is effective in differentiating malignant from benign masses.
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Project description:ObjectivesTo develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans.Material and methodsThe method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs.ResultsThe agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability.ConclusionThis fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice.Key points• Fully automatic method to assess thoracic aorta diameters. • High agreement between fully automatic method and manual segmentations. • Method is suitable for non-ECG-gated CT and can therefore be used in screening.
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Project description:BackgroundAbdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical.PurposeTo perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT.Materials and methodsThe training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations.ResultsOn CTC scans, automated Agatston scoring correlated highly with manual assessment (R2 = 0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R2 = 0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments.ConclusionFully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies.
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Project description:Contrast flow and enhancement patterns seen on thoracic CT angiography (CTA) can often be challenging and may often reveal more than is immediately apparent. A non-diagnostic CTA following the initial contrast injection can be secondary to many causes; these include both extrinsic factors, such as injection technique/equipment failure (iv cannula, power injector), and intrinsic, patient-related factors. Contrast pressure and flow graphs often contain useful information regarding the etiology of a non-diagnostic scan. Understanding these graphs will help the radiologist plan a repeat contrast injection to overcome the deficiencies of the first injection and thus obtain a diagnostic scan. The current review article outlines normal and abnormal intravenous contrast dynamics, discusses how to recognize etiologies of non-diagnostic scans, and ultimately addresses techniques to overcome obstacles towards obtaining normal contrast opacification of the target vessel. In addition, there are some life-threatening findings, which unless sought for, may remain hidden in plain sight. Key Points • Using contrast enhancement and flow patterns to identify the cause of a non-diagnostic CTA.• Recognize life threatening causes of altered contrast dynamics such as cardiac asystole.• Non-target vessel opacification may hold key to underlying pathophysiology.
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Project description:Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout (n = 216) and external (n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout (n = 53) and external (n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022.
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Project description:BackgroundPartial volume averaging and tilt relative to the scan plane on transverse images limit the accuracy of airway wall thickness measurements on CT scan, confounding assessment of the relationship between airway remodeling and clinical status in COPD. The purpose of this study was to assess the effect of partial volume averaging and tilt corrections on airway wall thickness measurement accuracy and on relationships between airway wall thickening and clinical status in COPD.MethodsAirway wall thickness measurements in 80 heavy smokers were obtained on transverse images from low-dose CT scan using the open-source program Airway Inspector. Measurements were corrected for partial volume averaging and tilt effects using an attenuation- and geometry-based algorithm and compared with functional status.ResultsThe algorithm reduced wall thickness measurements of smaller airways to a greater degree than larger airways, increasing the overall range. When restricted to analyses of airways with an inner diameter < 3.0 mm, for a theoretical airway of 2.0 mm inner diameter, the wall thickness decreased from 1.07 ± 0.07 to 0.29 ± 0.10 mm, and the square root of the wall area decreased from 3.34 ± 0.15 to 1.58 ± 0.29 mm, comparable to histologic measurement studies. Corrected measurements had higher correlation with FEV₁, differed more between BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index scores, and explained a greater proportion of FEV1 variability in multivariate models.ConclusionsCorrecting for partial volume averaging improves accuracy of airway wall thickness estimation, allowing direct measurement of the small airways to better define their role in COPD.
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Project description:Two-dimensional ultrasound (US) and color doppler flow imaging are associated with certain limitations in the preprocedural evaluation and design of the puncture path for biopsies of thoracic lesions, such as a poorly defined boundary between the tumor and the atelectatic lesions in central lung cancer with atelectasis. Contrast-enhanced ultrasound (CEUS) can be valuable in the preoperative evaluation of the biopsy site and in increasing the accuracy of the biopsy. The present study investigated the value of clinical application of CEUS in US-guided core needle biopsy (US-CNB) in improving the diagnostic accuracy in thoracic lesions. A total of 120 patients with first-stage thoracic lesions from the Affiliated Tumor Hospital of Guangxi Medical University who underwent US-CNB were recruited and randomnly assigned to a conventional US group (n=66) and a CEUS group (n=54). All patients underwent preoperative evaluation and US-guided puncture of thoracic lesions. The intergroup differences in sonographic features, biopsy duration, biopsy success rate and complications were assessed. The CEUS group had a higher rate of detection of necrotic tissue (40.7% vs. 16.7%; ?2=8.633; P=0.003) and change of initial puncture path (48.1%) compared with the US group. In central lung cancer with atelectasis, the ability to distinguish between tumor and atelectasis was higher in the CEUS group compared with the conventional US group (31.5 vs. 7.6%; ?2=11.336; P=0.001). In addition, the CEUS group had a higher puncture success (96.3 vs. 80.3%; ?2=6.946; P=0.008) and a lower complication rate (3.7% vs. 18.2%; ?2=6.041; P=0.014) compared with the US group. CEUS can identify necrotic areas and occult tumors within atelectatic lung tissue and can be used for guiding puncture biopsy of thoracic lesions to improve the diagnostic accuracy with greater comparative clinical utility than conventional US. Pre-biopsy CEUS is especially useful for patients undergoing repeated US-CNB and those with hypovascular lesions, atelectasis or necrosis.
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Project description:Greater kyphosis angles lead to increased loading on vertebral bodies in computational models. However, results about the relationship between severity of kyphosis and incident vertebral fracture (VF) risk have been conflicting. Therefore, the aim of this study was to evaluate associations between 1) prevalent VFs and severity of kyphosis, and 2) severity of kyphosis and incident VF risk in smokers with or without chronic obstructive pulmonary disease (COPD). Former and current smokers with or without COPD were included. CT scans were made at baseline, 1-year, and 3-year follow-up. VFs were evaluated on superposed sagittal CT reconstructions. Kyphosis was measured as the angle between the lines above T4 and below T9 or T12 . We included 1239 subjects (mean age 61.3 ± 8.0 years, 61.1% male, 80.6% with COPD), of whom 253 (20.4%) had a prevalent VF and 294 (23.7%) an incident VF within 3 years. Presence, number, and severity of prevalent VFs were associated with a greater kyphosis angle. The mean increase in kyphosis angle within 3 years was small but significantly greater in subjects with incident VFs compared with those without (2.2 ± 4.1 versus 1.2 ± 3.9 degrees, respectively, for T4 to T12 angle, p < 0.001). After adjustment for bone attenuation (BA) and prevalent VFs, baseline kyphosis angle was associated with incident VFs within 1 and 3 years (angle T4 to T12 per +1 SD, hazard ratio [HR] = 1.34 [1.12-1.61] and HR 1.29 [1.15-1.45], respectively). Our data showed that a greater kyphosis angle at baseline was independently associated with increased risk of incident VFs within 1 and 3 years, supporting the theory that greater kyphosis angle contributes to higher biomechanical loads in the spine. © 2019 American Society for Bone and Mineral Research.
| S-EPMC6946934 | biostudies-literature
Project description:PurposeTo evaluate the feasibility of using contrast material-enhanced computed tomographic (CT) measurements of hepatic fractional extracellular space (fECS) and macromolecular contrast material (MMCM) uptake to measure severity of liver fibrosis.Materials and methodsAll procedures were approved by and executed in accordance with University of California, San Francisco, institutional animal care and use committee regulations. Twenty-one rats that received intragastric CCl(4) for 0-12 weeks were imaged with respiratory-gated micro-CT by using both a conventional contrast material and a novel iodinated MMCM. Histopathologic hepatic fibrosis was graded qualitatively by using the Ishak fibrosis score and quantitatively by using morphometry of the fibrosis area. Hepatic fECS and MMCM uptake were calculated for each examination and correlated with histopathologic findings by using uni- and multivariate linear regressions.ResultsIshak fibrosis scores ranged from a baseline of 0 in untreated animals to a maximum of 5. Histopathologic liver fibrosis area increased from 0.46% to 3.5% over the same interval. Strong correlations were seen between conventional contrast-enhanced CT measurements of fECS and both the Ishak fibrosis scores (R(2) = 0.751, P < .001) and the fibrosis area (R(2) = 0.801, P < .001). Strong negative correlations were observed between uptake of MMCM in the liver and Ishak fibrosis scores (R(2) = 0.827, P < .001), as well as between uptake of MMCM in the liver and fibrosis area (R(2) = 0.643, P = .001). Multivariate linear regression analysis showed a trend toward independence for fECS and MMCM uptake in the prediction of Ishak fibrosis scores, with an R(2) value of 0.86 (P = .081 and P = .033, respectively).ConclusionContrast-enhanced CT measurements of fECS and MMCM uptake are individually capable of being used to estimate the degree of early hepatic fibrosis in a rat model.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112452/-/DC1.
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