Persistent mucus plugs in proximal airways are consequential for airflow limitation in asthma

BACKGROUNDInformation about the size, airway location, and longitudinal behavior of mucus plugs in asthma is needed to understand their role in mechanisms of airflow obstruction and to rationally design muco-active treatments.METHODSCT lung scans from 57 patients with asthma were analyzed to quantify mucus plug size and airway location, and paired CT scans obtained 3 years apart were analyzed to determine plug behavior over time. Radiologist annotations of mucus plugs were incorporated in an image-processing pipeline to generate size and location information that was related to measures of airflow.RESULTSThe length distribution of 778 annotated mucus plugs was multimodal, and a 12 mm length defined short ("stubby", ≤12 mm) and long ("stringy", >12 mm) plug phenotypes. High mucus plug burden was disproportionately attributable to stringy mucus plugs. Mucus plugs localized predominantly to airway generations 6-9, and 47% of plugs in baseline scans persisted in the same airway for 3 years and fluctuated in length and volume. Mucus plugs in larger proximal generations had greater effects on spirometry measures than plugs in smaller distal generations, and a model of airflow that estimates the increased airway resistance attributable to plugs predicted a greater effect for proximal generations and more numerous mucus plugs.CONCLUSIONPersistent mucus plugs in proximal airway generations occur in asthma and demonstrate a stochastic process of formation and resolution over time. Proximal airway mucus plugs are consequential for airflow and are in locations amenable to treatment by inhaled muco-active drugs or bronchoscopy.TRIAL REGISTRATIONClinicaltrials.gov; NCT01718197, NCT01606826, NCT01750411, NCT01761058, NCT01761630, NCT01716494, and NCT01760915.FUNDINGAstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Sanofi-Genzyme-Regeneron, and TEVA provided financial support for study activities at the Coordinating and Clinical Centers beyond the third year of patient follow-up. These companies had no role in study design or data analysis, and the only restriction on the funds was that they be used to support the SARP initiative.

Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI

Purpose

To assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (DLSS), to infer myocardial velocity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease.

Materials and Methods

In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis.

Results

Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively.

Conclusion

The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.Keywords: Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2023.

Early-stage COVID-19 pandemic observations on pulmonary embolism using nationwide multi-institutional data harvesting

We introduce a multi-institutional data harvesting (MIDH) method for longitudinal observation of medical imaging utilization and reporting. By tracking both large-scale utilization and clinical imaging results data, the MIDH approach is targeted at measuring surrogates for important disease-related observational quantities over time. To quantitatively investigate its clinical applicability, we performed a retrospective multi-institutional study encompassing 13 healthcare systems throughout the United States before and after the 2020 COVID-19 pandemic. Using repurposed software infrastructure of a commercial AI-based image analysis service, we harvested data on medical imaging service requests and radiology reports for 40,037 computed tomography pulmonary angiograms (CTPA) to evaluate for pulmonary embolism (PE). Specifically, we compared two 70-day observational periods, namely (i) a pre-pandemic control period from 11/25/2019 through 2/2/2020, and (ii) a period during the early COVID-19 pandemic from 3/8/2020 through 5/16/2020. Natural language processing (NLP) on final radiology reports served as the ground truth for identifying positive PE cases, where we found an NLP accuracy of 98% for classifying radiology reports as positive or negative for PE based on a manual review of 2,400 radiology reports. Fewer CTPA exams were performed during the early COVID-19 pandemic than during the pre-pandemic period (9806 vs. 12,106). However, the PE positivity rate was significantly higher (11.6 vs. 9.9%, p < 10^-4) with an excess of 92 PE cases during the early COVID-19 outbreak, i.e., ~1.3 daily PE cases more than statistically expected. Our results suggest that MIDH can contribute value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.

Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography

CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (r = 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively. The algorithm was further evaluated in a random sampling of 3195 CTPA examinations from January 2019 through May 2021. Beginning in January 2021, the scanning protocol was transitioned from bolus tracking to a timing bolus strategy. Automated analysis of these examinations showed that most suboptimal examinations following the change in protocol were performed using one scanner, highlighting the potential value of deep learning algorithms for quality improvement in the radiology department. Keywords: CT Angiography, Pulmonary Arteries © RSNA, 2022.

Reader Perceptions and Impact of AI on CT Assessment of Air Trapping

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages: qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients: visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P < .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second-to-forced vital capacity ratio) (visual r = -0.26, semiquantitative r = -0.32, quantitative r = -0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords: Technology Assessment, Quantification © RSNA, 2021.

Deep Learning-Based 3D Segmentation of True Lumen, False Lumen, and False Lumen Thrombosis in Type-B Aortic Dissection

Patients with initially uncomplicated typeB aortic dissection (uTBAD) remain at high risk for developing late complications. Identification of morphologic features for improving risk stratification of these patients requires automated segmentation of computed tomography angiography (CTA) images. We developed three segmentation models utilizing a 3D residual U-Net for segmentation of the true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT). Model 1 segments all labels at once, whereas model 2 segments them sequentially. Best results for TL and FL segmentation were achieved by model 2, with median (interquartiles) Dice similarity coefficients (DSC) of 0.85 (0.77-0.88) and 0.84 (0.82-0.87), respectively. For FLT segmentation, model 1 was superior to model 2, with median (interquartiles) DSCs of 0.63 (0.40-0.78). To purely test the performance of the network to segment FLT, a third model segmented FLT starting from the manually segmented FL, resulting in median (interquartiles) DSCs of 0.99 (0.98-0.99) and 0.85 (0.73-0.94) for patent FL and FLT, respectively. While the ambiguous appearance of FLT on imaging remains a significant limitation for accurate segmentation, our pipeline has the potential to help in segmentation of aortic lumina and thrombosis in uTBAD patients.Clinical relevance- Most predictors of aortic dissection (AD) degeneration are identified through anatomical modeling, which is currently prohibitive in clinical settings due to the timeintense human interaction. False lumen thrombosis, which often develops in patients with type B AD, has proven to show significant prognostic value for predicting late adverse events. Our automated segmentation algorithm offers the potential of personalized treatment for AD patients, leading to an increase in long-term survival.

Aortic growth and development of partial false lumen thrombosis are associated with late adverse events in type B aortic dissection

BACKGROUND

Patients with medically treated type B aortic dissection (TBAD) remain at significant risk for late adverse events (LAEs). We hypothesize that not only initial morphological features, but also their change over time at follow-up are associated with LAEs.

MATERIALS AND METHODS

Baseline and 188 follow-up computed tomography (CT) scans with a median follow-up time of 4 years (range, 10 days to 12.7 years) of 47 patients with acute uncomplicated TBAD were retrospectively reviewed. Morphological features (n = 8) were quantified at baseline and each follow-up. Medical records were reviewed for LAEs, which were defined according to current guidelines. To assess the effects of changes of morphological features over time, the linear mixed effects models were combined with Cox proportional hazards regression for the time-to-event outcome using a joint modeling approach.

RESULTS

LAEs occurred in 21 of 47 patients at a median of 6.6 years (95% confidence interval [CI], 5.1-11.2 years). Among the 8 investigated morphological features, the following 3 features showed strong association with LAEs: increase in partial false lumen thrombosis area (hazard ratio [HR], 1.39; 95% CI, 1.18-1.66 per cm2 increase; P < .001), increase of major aortic diameter (HR, 1.24; 95% CI, 1.13-1.37 per mm increase; P < .001), and increase in the circumferential extent of false lumen (HR, 1.05; 95% CI, 1.01-1.10 per degree increase; P < .001).

CONCLUSIONS

In medically treated TBAD, increases in aortic diameter, new or increased partial false lumen thrombosis area, and increases of circumferential extent of the false lumen are strongly associated with LAEs.

CT-based True- and False-Lumen Segmentation in Type B Aortic Dissection Using Machine Learning

Purpose

To develop a segmentation pipeline for segmentation of aortic dissection CT angiograms into true and false lumina on multiplanar reformations (MPRs) perpendicular to the aortic centerline and derive quantitative morphologic features, specifically aortic diameter and true- or false-lumen cross-sectional area.

Materials and Methods

An automated segmentation pipeline including two convolutional neural network (CNN) segmentation algorithms was developed. The algorithm derives the aortic centerline, generates MPRs orthogonal to the centerline, and segments the true and false lumina. A total of 153 CT angiograms obtained from 45 retrospectively identified patients (mean age, 50 years; range, 22-79 years) were used to train (n = 103), validate (n = 22), and test (n = 28) the CNN pipeline. Accuracy was evaluated by using the Dice similarity coefficient (DSC). Segmentations were then used to derive the maximal diameter of test-set patients and cross-sectional area profiles of the true and false lumina.

Results

The segmentation pipeline yielded a mean DSC of 0.873 ± 0.056 for the true lumina and 0.894 ± 0.040 for the false lumina of test-set cases. Automated maximal diameter measurements correlated well with manual measurements (R ² = 0.95). Profiles of cross-sectional diameter, true-lumen area, and false-lumen area over several follow-up examinations were derived.

Conclusion

A segmentation pipeline was used to accurately identify true and false lumina on CT angiograms of aortic dissection. These segmentations can be used to obtain diameter and other morphologic parameters for surveillance and risk stratification.Supplemental material is available for this article.© RSNA, 2020.

Left Atrial Volume as a Biomarker of Atrial Fibrillation at Routine Chest CT: Deep Learning Approach

Purpose

To test the performance of a deep learning (DL) model in predicting atrial fibrillation (AF) at routine nongated chest CT.

Materials and Methods

A retrospective derivation cohort (mean age, 64 years; 51% female) consisting of 500 consecutive patients who underwent routine chest CT served as the training set for a DL model that was used to measure left atrial volume. The model was then used to measure atrial size for a separate 500-patient validation cohort (mean age, 61 years; 46% female), in which the AF status was determined by performing a chart review. The performance of automated atrial size as a predictor of AF was evaluated by using a receiver operating characteristic analysis.

Results

There was good agreement between manual and model-generated segmentation maps by all measures of overlap and surface distance (mean Dice = 0.87, intersection over union = 0.77, Hausdorff distance = 4.36 mm, average symmetric surface distance = 0.96 mm), and agreement was slightly but significantly greater than that between human observers (mean Dice = 0.85 [automated] vs 0.84 [manual]; P = .004). Atrial volume was a good predictor of AF in the validation cohort (area under the receiver operating characteristic curve = 0.768) and was an independent predictor of AF, with an age-adjusted relative risk of 2.9.

Conclusion

Left atrial volume is an independent predictor of the AF status as measured at routine nongated chest CT. Deep learning is a suitable tool for automated measurement.© RSNA, 2019See also the commentary by de Roos and Tao in this issue.

Cross-sectional imaging of thoracic traumatic aortic injury

Aortic injury remains a major contributor to morbidity and mortality from acute thoracic trauma. While such injuries were once nearly uniformly fatal, the advent of cross-sectional imaging in recent years has facilitated rapid diagnosis and triage, greatly improving outcomes. In fact, cross-sectional imaging is now the diagnostic test of choice for traumatic aortic injury (TAI), specifically computed tomography angiography (CTA) in the acute setting and CTA or magnetic resonance angiography (MRA) in follow-up. In this review, we present an up-to-date discussion of acute traumatic thoracic aortic injury with a focus on optimal and emerging CT/MR techniques, imaging findings of TAI, and potential pitfalls.

Indolent thyroid cancer: knowns and unknowns

Thyroid cancer incidence is rapidly increasing due to increased detection and diagnosis of indolent thyroid cancer, i.e. cancer that is likely to be clinically insignificant. Clinical, radiologic, and pathologic features predicting indolent behavior of thyroid cancer are still largely unknown and unstudied. Existing clinicopathologic staging systems are useful for providing prognosis in the context of treated thyroid cancer but are not designed for and are inadequate for predicting indolent behavior. Ultrasound studies have primarily focused on discrimination between malignant and benign nodules; some studies show promising data on using sonographic features for predicting indolence but are still in their early stages. Similarly, molecular studies are being developed to better characterize thyroid cancer and improve the yield of fine needle aspiration biopsy, but definite markers of indolent thyroid cancer have yet to be identified. Nonetheless, active surveillance has been introduced as an alternative to surgery in the case of indolent thyroid microcarcinoma, and protocols for safe surveillance are in development. As increased detection of thyroid cancer is all but inevitable, increased research on predicting indolent behavior is needed to avoid an epidemic of overtreatment.

Indolent thyroid cancer: knowns and unknowns

Thyroid cancer incidence is rapidly increasing due to increased detection and diagnosis of indolent thyroid cancer, i.e. cancer that is likely to be clinically insignificant. Clinical, radiologic, and pathologic features predicting indolent behavior of thyroid cancer are still largely unknown and unstudied. Existing clinicopathologic staging systems are useful for providing prognosis in the context of treated thyroid cancer but are not designed for and are inadequate for predicting indolent behavior. Ultrasound studies have primarily focused on discrimination between malignant and benign nodules; some studies show promising data on using sonographic features for predicting indolence but are still in their early stages. Similarly, molecular studies are being developed to better characterize thyroid cancer and improve the yield of fine needle aspiration biopsy, but definite markers of indolent thyroid cancer have yet to be identified. Nonetheless, active surveillance has been introduced as an alternative to surgery in the case of indolent thyroid microcarcinoma, and protocols for safe surveillance are in development. As increased detection of thyroid cancer is all but inevitable, increased research on predicting indolent behavior is needed to avoid an epidemic of overtreatment.

Spatial spectral imaging as an adjunct to the Bethesda classification of thyroid fine-needle aspiration specimens

BACKGROUND

Thyroid fine-needle aspiration (FNA) biopsy, the preoperative diagnostic standard of care for patients with thyroid nodules, has limitations. Spectral imaging captures visible light information that is beyond the capability of the human eye, potentially increasing the accuracy of FNA biopsy. In the current study, the authors demonstrated the feasibility of using spectral imaging in combination with automated spatial analysis based on trainable pattern recognition as an adjunct test for thyroid FNA classification by developing an algorithm that distinguishes between images of papillary thyroid carcinoma (PTC) and benign goiter (BG).

METHODS

A multispectral camera was used to capture spectral images representing 100 cases of PTC and BG. Used in conjunction with commercial software, 10 cases were used as a training set to develop a "classifier," a classification algorithm that segments digitized multispectral images into regions of PTC, BG, and "nonfeature." This algorithm was used to generate a screening test and a diagnostic test that were validated on an independent set of images representing 30 cases of PTC and 30 cases of BG.

RESULTS

The area under the receiver operating characteristic for the PTC/BG classifier was 0.90. The screening test had a sensitivity of 0.93 and a specificity of 0.73. The diagnostic test had a sensitivity of 0.70 and a specificity of 0.90.

CONCLUSIONS

The authors developed image classification tests that distinguish between FNAs of PTC and BG, demonstrating the potential value of spatial spectral imaging as an adjunct test for the classification of thyroid FNA samples. The data support prospective testing to determine the value of the PTC/BG classifier in routine clinical use.

Polycation structure mediates expression of lyophilized polycation/pDNA complexes

Lyophilization of polycation/pDNA complexes provides stable, long-term storage of complexes prior to clinical use but also reduces gene delivery efficiency. We examined whether polycation structure mediates effects of lyophilization on gene expression. Linear and branched PEI of the same molecular weight were used as a model system. Interestingly, pDNA/linear PEI complexes led to much smaller effects on gene expression following lyophilization compared with branched PEI complexes. The effect of polycation structure correlated with changes in dissociation ability of pDNA/PEI complexes. These results will be useful for developing new gene delivery vehicles.