Summary Organizing pneumonia (OP) is a histopathologic pattern of response to lung injury. Fibrin is a marker of acute microvascular injury, and variable amounts of intraalveolar fibrin are seen in ...OP; however, its relevance to clinical outcomes is unclear. We examined lung wedge biopsies of 26 patients with cryptogenic organizing pneumonia (COP), assessed the amount of fibrin associated with airspace organization, and correlated fibrin levels with other histologic, clinical, and radiographic findings. Seven patients with COP had disease relapse. Patients with multifocal fibrin deposits or acute fibrinous and organizing pneumonia (collectively, “high fibrin”) showed a higher rate of OP relapse compared to those with no or focal fibrin (60% versus 6%, P < .05). Patients with radiographic evidence of disease involving all three lung zones (upper, middle, and lower) also showed higher rates of relapse compared to those in whom disease was limited to one or two zones (41% versus 0%, P = .055). In patients with both pathologic evidence of high fibrin and radiographic evidence of three-zone disease, OP relapse could be predicted with a sensitivity of 86% and specificity of 84% (positive predictive value of 67% and negative predictive value of 94%). The presence of high levels of intraalveolar fibrin in lung biopsies and radiographic evidence of disease involving all three lung zones is associated with increased risk of relapse in patients with COP, and these features may help identify patients who may benefit from more intensive steroid therapy.
Lung cancer screening (LCS) with low-dose CT (LDCT) was implemented in the United States following the National Lung Screening Trial (NLST). The real-world benefits of implementing LCS are yet to be ...determined with outcome-oriented data. The study objective is to investigate the characteristics and outcomes of screening-detected lung cancers.
This single-institution retrospective study included LCS patients between June 2014 and December 2019. Patient demographics, number of screening rounds, imaging features, clinical workup, disease extent, histopathology, treatment, complications, and mortality outcomes of screening-detected lung cancers were extracted and compared with NLST data.
LCS LDCTs (7,480) were performed on 4,176 patients. The cancer detection rate was 3.8%, higher than reported by NLST (2.4%,
< 0.0001), and cancers were most often found in patients ≥65 years (62%), older than those in NLST (41%,
< 0.0001). The patients' ethnicity was similar to NLST,
= 0.87. Most LCS-detected cancers were early stage I tumors (71% vs. 54% in NLST,
< 0.0001). Two thirds of cancers were detected in the first round of screening (67.1%) and were multifocal lung cancers in 15%. As in NLST, the complication rate after invasive workup or surgery was low (24% vs. 28% in NLST,
= 0.32). Over a median follow-up of 3.3 years, the mortality rate was 0.45%, lower than NLST (1.33%,
< 0.0001).
LCS implementation achieved a higher cancer detection rate, detection of early-stage cancers, and more multifocal lung cancers compared with the NLST, with low complications and mortality.
The real-world implementation of LCS has been successful for detection of lung cancer with favorable outcomes.
Iodine uptake in the lungs and intrathoracic lesions on postcontrast dual-energy computed tomography is used for evaluation of pulmonary embolism-related perfusion defects and pulmonary infarctions. ...It has been applied in characterization and treatment response assessment of lung and mediastinal abnormalities. We report a new imaging artifact or faulty image postprocessing in a commercially available rapid kV switching technique of dual-energy computed tomography, which can confound its clinical utility for evaluation of iodine uptake.
To assess feasibility of automated segmentation and measurement of tracheal collapsibility for detecting tracheomalacia on inspiratory and expiratory chest CT images.
Our study included 123 patients ...(age 67 ± 11 years; female: male 69:54) who underwent clinically indicated chest CT examinations in both inspiration and expiration phases. A thoracic radiologist measured anteroposterior length of trachea in inspiration and expiration phase image at the level of maximum collapsibility or aortic arch (in absence of luminal change). Separately, another investigator separately processed the inspiratory and expiratory DICOM CT images with Airway Segmentation component of a commercial COPD software (IntelliSpace Portal, Philips Healthcare). Upon segmentation, the software automatically estimated average lumen diameter (in mm) and lumen area (sq.mm) both along the entire length of trachea and at the level of aortic arch. Data were analyzed with independent t-tests and area under the receiver operating characteristic curve (AUC).
Of the 123 patients, 48 patients had tracheomalacia and 75 patients did not. Ratios of inspiration to expiration phases average lumen area and lumen diameter from the length of trachea had the highest AUC of 0.93 (95% CI = 0.88-0.97) for differentiating presence and absence of tracheomalacia. A decrease of ≥25% in average lumen diameter had sensitivity of 82% and specificity of 87% for detecting tracheomalacia. A decrease of ≥40% in the average lumen area had sensitivity and specificity of 86% for detecting tracheomalacia.
Automatic segmentation and measurement of tracheal dimension over the entire tracheal length is more accurate than a single-level measurement for detecting tracheomalacia.
•Automatic measurement of tracheal on CT images helps detection of tracheomalacia.•Average luminal change over entire trachea is a better predictor of tracheomalacia comparing to single section evaluation.•Automatic segmentation of trachea has high diagnostic performance for tracheomalacia detection.
Diffuse lung metastases have been reported in non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations. The purpose of our study was to compare the incidence of ...diffuse lung metastases in EGFR-mutant NSCLC and EGFR-wild type NSCLC and to assess other imaging features that may be associated with diffuse lung metastases in EGFR-mutant NSCLC. Two radiologists retrospectively reviewed pre-treatment imaging of metastatic NSCLC cases with known EGFR mutation status. We assessed the imaging features of the primary tumor and patterns of metastases. The cohort consisted of 217 patients (117 EGFR-mutant, 100 EGFR wild-type). Diffuse lung metastasis was significantly more common in EGFR-mutant NSCLC compared with wild-type (18% vs. 3%, p < 0.01). Among the EGFR-mutant group, diffuse lung metastases were inversely correlated with the presence of a nodule greater than 6 mm other than the primary lung lesion (OR: 0.13, 95% CI: 0.04–0.41, p < 0.01). EGFR mutations in NSCLC are associated with increased frequency of diffuse lung metastases. The presence of diffuse lung metastases in EGFR-mutant NSCLC is also associated with a decreased presence of other larger discrete lung metastases. EGFR mutations in NSCLC should be suspected in the setting of a dominant primary lung mass associated with diffuse lung metastases.
We evaluated and compared performance of an acute pulmonary embolism (PE) triaging artificial intelligence (PE-AI) model in suboptimal and optimal CT pulmonary angiography (CTPA).
In an IRB approved, ...retrospective study we identified 104 consecutive, suboptimal CTPA which were deemed as suboptimal for PE evaluation in radiology reports due to motion, artifacts and/or inadequate contrast enhancement. We enriched this dataset, with additional 226 optimal CTPA (over same timeframe as suboptimal CTPA) with and without PE. Two thoracic radiologists (ground truth) independently reviewed all 330 CTPA for adequacy (to assess PE down to distal segmental level), reason for suboptimal CTPA (artifacts or poor contrast enhancement), as well as for presence and location of PE. CT values (HU) were measured in the main pulmonary artery. Same attributes were assessed in 80 patients who had repeat or follow-up CTPA following suboptimal CTPA. All CTPA were processed with the PE-AI (Aidoc).
Among 104 suboptimal CTPA (mean age ± standard deviation 56 ± 15 years), 18/104 (17%) were misclassified as suboptimal for PE evaluation in their radiology reports but relabeled as optimal on ground truth evaluation. Of 226 optimal CTPA, 47 (21%) were reclassified as suboptimal CTPA. PEs were present in 97/330 CTPA. PE-AI had similar performance on suboptimal CTPA (sensitivity 100%; specificity 89%; AUC 0.89, 95% CI 0.80–0.98) and optimal CTPA (sensitivity 96%; specificity 92%; AUC 0.87, 95% CI 0.81–0.93).
Suboptimal CTPA examinations do not impair the performance of PE-AI triage model; AI retains clinically meaningful sensitivity and high specificity regardless of diagnostic quality.
•Motion artifacts and poor contrast enhancement are the most common causes of suboptimality in CTPAs.•The assessed AI algorithm had high sensitivity, specificity, and accuracy for detecting PE on optimal CTPAs.•The AI algorithm did not miss any PE on suboptimal CTPA although the prevalence of PE was very low (14%).
To compare visualization of subtle normal and abnormal findings at computed tomography (CT) of the chest for diffuse lung disease with images reconstructed with filtered back projection and adaptive ...statistical iterative reconstruction (ASIR) techniques.
In this HIPAA-compliant, institutional review board-approved study, 24 patients underwent 64-section multi-detector row CT of the chest for evaluation of diffuse lung disease. Scanning parameters included a pitch of 0.984:1 and 120 kVp in thin-section mode, with 2496 views per rotation compared with 984 views acquired for normal mode. The 0.625-mm-thick images were reconstructed with filtered back projection, ASIR, and ASIR high-definition (ASIR-HD) kernels. Two thoracic radiologists independently assessed the filtered back projection, ASIR, and ASIR-HD images for small anatomic details (interlobular septa, centrilobular region, and small bronchi and bronchioles), abnormal findings (reticulation, tiny nodules, altered attenuation, bronchiectasis), image quality (graded by using a six-point scale, where 1 = excellent image quality, and 5 = interpretation impossible), image noise, and artifacts. Data were tabulated for statistical testing.
For visualization of normal and pathologic structures, CT image series reconstructed with ASIR-HD were rated substantially better than those reconstructed with filtered back projection and ASIR (P < .001). ASIR-HD images were superior to filtered back projection images in 15 of 24 (62%) patients for visualization of normal structures and in 24 of 24 (100%) patients for pathologic findings. ASIR-HD was superior to ASIR in three of 24 (12%) images for normal anatomic findings and in seven of 24 (29%) images for pathologic evaluation. None of the images in the three groups were rated as unacceptable for noise (P < .001).
ASIR-HD reconstruction results in superior visualization of subtle and tiny anatomic structures and lesions in diffuse lung disease compared with ASIR and filtered back projection reconstructions.
Immune-related pneumonitis is a potentially fatal complication of treatment with immune checkpoint inhibitors (ICIs). Interstitial lung disease (ILD) is associated with increased risk for ...pneumonitis, but the impact of interstitial abnormalities (ILA) in the absence of ILD has not been extensively assessed. We examined the relationship between ILA on pretreatment chest computed tomography (CT) scans and risk of pneumonitis in patients with non-small-cell lung cancer (NSCLC).
This retrospective cohort study included consecutive adult patients who received ICI for NSCLC between January 2013 and January 2020 at our institution. Two thoracic radiologists blinded to clinical outcomes independently reviewed pre-ICI chest CTs to identify and categorize ILA using previously published definitions. We used uni- and multivariable analysis adjusted for age, radiation, and smoking status to assess for associations between ILA, clinicopathologic characteristics, and symptomatic (CTCAE grade ≥2) pneumonitis.
Of 475 patients who received ICI treatment and met inclusion criteria, baseline ILA were present in 78 (16.4%) patients, most commonly as a subpleural nonfibrotic pattern. In total, 43 (9.1%) of 475 patients developed symptomatic pneumonitis. Pneumonitis occurred in 16.7% of patients with ILA compared to 7.6% patients without ILA (P < .05). Presence of ground glass and extent of lung parenchymal involvement were associated with an increased risk of pneumonitis. On multivariable analysis, baseline ILA remained associated with increased risk of symptomatic pneumonitis (OR 2.2, 95% CI, 1.0-4.5).
Baseline ILAs are associated with the development of symptomatic pneumonitis in patients with NSCLC treated with ICI. Additional studies are needed to validate these observations.
Purpose
Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may ...be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome.
Methods
We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients).
Results
AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman’s rank correlation 0.837,
p
<
0.001
). Using AI-based scores produced significantly higher (
p
<
0.05
) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI 0.729, 0.886) in predicting ICU admission and AUC = 0.741 (95% CI 0.640, 0.837) in mortality estimation on the two datasets.
Conclusions
Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.
The efficient and accurate interpretation of radiologic images is paramount.
To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader ...performance and efficiency in interpreting chest radiograph abnormalities.
This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray MIMIC-CXR) and Massachusetts General Hospital (MGH).
The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding.
A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean SD age, 63 16 years; 133 men 53.2%)-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 95% CI, 0.732-0.882 vs 0.567 95% CI, 0.524-0.611; pneumonia, 0.887 95% CI, 0.834-0.928 vs 0.673 95% CI, 0.632-0.714; pleural effusion, 0.872 95% CI, 0.808-0.921 vs 0.889 95% CI, 0.862-0.917; pneumothorax, 0.988 95% CI, 0.932-1.000 vs 0.792 95% CI, 0.756-0.827). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001).
These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.
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