Experienced Thoracic Radiologist Research Articles

Meta-Analysis of Interobserver Agreement in Assessment of Interstitial Lung Disease Using High-Resolution CT.

Background High-resolution CT (HRCT) is central to the assessment of interstitial lung disease (ILD), and accurate classification of disease has important implications for patients. Evaluation of imaging features can be challenging, even for experienced thoracic radiologists. Previous work has provided equivocal evidence on the interpretation of HRCT features at ILD-related imaging. Purpose To perform a meta-analysis to assess the level of agreement among expert thoracic radiologists in interpreting ILD-related imaging. Materials and Methods A systematic literature search from January 2000 to October 2023 of the Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases was performed for articles reporting assessments of interobserver agreement between thoracic radiologists for evaluation of ILD findings, such as severity and progression of disease, presence of features such as honeycombing and ground-glass opacification, and classification based on the 2011 and 2018 American Thoracic Society/European Respiratory Society/Japanese Respiratory Society/Asociación Latinoamericana del Tórax (ATS/ERS/JRS/ALAT) guidelines for idiopathic pulmonary fibrosis (IPF). Meta-analysis was performed using a random-effects model to obtain pooled κ or intraclass correlation coefficient (ICC) values as measures of interobserver agreement. Results The final analysis included 13 studies consisting of 6943 images and 146 radiologists. In 10 studies assessing agreement of specific radiologic findings in ILD, the pooled κ value was 0.56 (95% CI: 0.43, 0.70). In eight studies, the assessed interobserver agreement of the ATS/ERS/JRS/ALAT diagnostic guidelines for IPF based on usual interstitial pneumonia (UIP) patterns, the pooled κ value was 0.61 (95% CI: 0.48, 0.74). One study reported a κ value of 0.87 for ILD progression. Seven studies assessing ILD severity could not be pooled; the individual κ values for ILD severity ranged from 0.64 to 0.90, and ICC values ranged from 0.63 to 0.96. Conclusion There was moderate agreement between thoracic radiologists when assessing ILD features and UIP pattern diagnosis but little evidence on agreement of disease severity, extent, or progression. Meta-analysis registry no. PROSPERO CRD42022361803 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Humbert in this issue.

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

Lung adenocarcinoma size as a predictor of distant metastasis: A CT scan-based measurement.

Previous studies have associated tumor size with metastasis and prognosis in lung carcinoma; however, a precise cut-off for predicting distant metastasis in lung adenocarcinoma remains unclear. The aim of this study was to determine the cut-off point for predicting distant metastasis in lung adenocarcinoma. A cross-sectional study was conducted at Dr. Moewardi Hospital, Surakarta, Indonesia, from January 2022 to September 2023. Total sampling was employed, involving patients over 18 years old with a confirmed diagnosis of lung adenocarcinoma based on lung computed tomography (CT) scan findings, who had not yet received chemotherapy and had confirmed metastasis outside the lung. The study's dependent variable was the incidence of distant metastasis, while the independent variable was lung adenocarcinoma size. Two experienced thoracic radiologists measured lung adenocarcinoma size by assessing the longest axis using chest multi-slice computed tomography (MSCT) in the lung window setting. Receiver operating characteristic (ROC) curve analysis determined the optimal tumor size cut-off for predicting distant metastasis. Of 956 thoracic cancer patients, 108 were diagnosed with lung adenocarcinoma. After applying the inclusion and exclusion criteria, 89 patients were eligible. In the present study, tumor size predicted 68.1% of distant metastasis cases, with a cut-off point of 7.25 cm, yielding a sensitivity of 61.9% and a specificity of 61.5%. Tumors >7.25 cm had a 2.60-fold higher risk of distant metastasis compared to smaller tumors, with larger tumors more likely to spread to various sites. In conclusion, lung adenocarcinomas larger than 7.25 cm have a 2.60-fold increased risk of distant metastasis, making tumor size a crucial predictive factor. The study provides valuable insights for radiologists and can improve diagnosis accuracy and treatment planning by emphasizing tumor size as a key factor in managing lung adenocarcinoma.

MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis

The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar–Bowker test. A total of 8578 samples from 6123 patients (mean age 66 ± 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89–0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7–58.7%) and the consensus of all five radiologists (59.3%, P < .001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.

BackgroundIn this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer computed tomography (CT) scans to distinguish between malignant and benign solid lung nodules.Material/MethodsCT images from 202 patients with 210 lesions (malignant: 127, benign: 83) manifesting as solid lung nodules from January 2016 to December 2020 from 3 institutions were retrospectively collected, and each nodule was histopathologically confirmed. Two experienced thoracic radiologists reviewed all images and determined the regions of interest (ROIs) in the three-dimensional (3D) images layer-by-layer. We divided the lesions and images into training and testing sets at a ratio of 7: 3. The Inception V3 model was pretrained by the training dataset. Five-fold cross-validation was used to choose the optimal model. Receiver operator characteristic curves (ROC curves) for methods to evaluate the performance of the models were drafted.ResultsIn the validation set, the AUC, accuracy, sensitivity, and specificity of Inception V3 model (lesion-level) were 0.999, 0.989, 0.983, and 1.0, respectively, which is higher than the image-level (0.997, 0.933, 0.922, and 0.948, respectively). The Inception V3 model (lesion-level) performed better than the image-level but there was no significant difference between the models (P>0.05). The ResNet50 model based on image level achieved AUC, accuracy, sensitivity, and specificity of 0.963, 0.926, 0.916, and 0.944, respectively, which is lower than that of Inception V3.ConclusionsOur study developed a novel deep learning model based on noncontrast and thin-layer CT scans to classify benign vs malignant lung nodules, and the Inception V3 model greatly improved the differentiation accuracy and specificity.

Baseline peripheral T-cell composition in relation to radiographic phenotypes of immune-related pneumonitis.

2545 Background: Pneumonitis is one of the most morbid complications from immune checkpoint inhibitor (ICI) treatment, but pathogenic mechanisms are unclear and no biomarkers permit pre-treatment risk assessment. We sought to characterize peripheral T cell subsets of pneumonitis patients on the single cell level. Methods: Blood was collected before and during ICI treatment in 24 patients. Cells were processed for single cell RNA sequencing (scRNAseq) employing CITEseq methodology using multiplexed cell surface markers labelled with a cocktail of oligonucleotide-tagged Total-Seq anti-human antibodies against CD4, CD8, CD45RA and CD27 followed by Chromium 10X sequencing. Principal Component Analysis was performed with iCellR, K-nearest-neighbor-based Network graph drawing Layout, and PhenoGraph clustering to assign cell types. CT scans were performed per standard of care and were reviewed by an experienced thoracic radiologist. Results: Seven of 24 patients developed pneumonitis; 9 did not experience an immune-related adverse event, and the remainder experienced arthritis (4), thyroiditis (3), or neurotoxicity (1). Pneumonitis patients had expanded proportions of TH2 TCF7+ T cells at baseline as compared to the other patients. Radiographically, two patients’ pneumonitis manifested as Chronic Hypersensitivity Pneumonitis (CHP), and four had Organized Pneumonia (OP). At baseline, CHP patients had significantly lower levels of CD8+ TCM cells (CXCR3+), double-positive T cells, gamma-delta T cells, and higher levels of naïve-like CD4+ TN TCF7+LEF1+ and CD4+ TH1/2 CXCR3+GATA3+ cells compared to OP. Gene expression levels also distinguished between these radiographic phenotypes. Conclusions: The peripheral T cell composition of patients who developed pneumonitis was distinct from those who did not in our cohort and unique by radiographic manifestation, suggesting potential pathogenic mechanisms and a prelude to circulating predictive markers of ICI toxicity.

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for CT imaging.

Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patient-based lung phantoms that accurately represent textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D-printing solution to create patient-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-of-interest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.

Influence of using recommended radiological criteria on MDCT-angiography diagnosis of single isolated subsegmental pulmonary embolism

We assessed the rate of false-positive diagnoses of MDCT-pulmonary angiography (MDCT-A) in patients with single isolated subsegmental pulmonary embolism (SISSPE). All patients who underwent MDCT-A between 2006 and 2017 for ruling out acute pulmonary embolism (PE) and received an initial diagnosis of SISSPE were included. The MDCT-A of these patients were reviewed retrospectively by four experienced thoracic radiologists, who applied radiological criteria recommended by the American College of Chest Physicians Antithrombotic Guidelines (ACCP 2016) for the diagnosis of SISSPE. Data extracted from medical records were history of venous thromboembolism (VTE), alternative diagnoses, other diagnostic studies for VTE, anticoagulation, bleeding complications, and VTE over the following 3months. Of 3839 patients undergoing MDCT-A, PE was found in 1021 (26.6%) and SISSPE in 59 (1.5% overall and 5.8% of all patients with PE). An alternative diagnosis to PE was made on the basis of CT in 33 (55.9%) patients. Forty-one (69.5%) patients received anticoagulants, and major life-threatening bleeding complications occurred in 2, with one death. Recurrent PE was not documented in any patient with SISSPE. In the retrospective assessment of the 59 cases of SISSPE, 21 were negative for PE, with a false-positive rate of 35.6% (21/59); so the percentage of SISSPE cases after the revision was 3.7% of all patients with PE; 11 of these 21 patients received anticoagulation. Radiologists should be aware of the high rate of false-positives when making the diagnosis of SISSPE on MDCT-A without using strict diagnostic criteria. Misdiagnosis exposes patients to unnecessary anticoagulation. • Radiologist should be aware of the high rate of false-positive diagnoses of single isolated subsegmental pulmonary embolism (SISSPE) in MDCT-pulmonary angiography (MDCT-A) performed for ruling out pulmonary embolism. • Misdiagnosis of SISSPE in MDCT-A can be reduced by using strict diagnostic radiological criteria recommended by the American College of Chest Physicians Antithrombotic Guidelines. • Unnecessary anticoagulation therapy with potential severe bleeding complications may result from misdiagnosis of SISSPE.

Quantitative lung perfusion blood volume using dual energy CT-based effective atomic number (Zeff ) imaging.

Iodine material images (aka iodine basis images) generated from dual energy computed tomography (DECT) have been used to assess potential perfusion defects in the pulmonary parenchyma. However, iodine material images do not provide the needed absolute quantification of the pulmonary blood pool, as materials with effective atomic numbers (Zeff ) different from those of basis materials may also contribute to iodine material images, thus confounding the quantification of perfusion defects. (i) To demonstrate the limitations of iodine material images in pulmonary perfusion defect quantification and (ii) to develop and validate a new quantitative biomarker using effective atomic numbers derived from DECT images. The quantitative relationship between the perfusion blood volume (PBV) in pulmonary parenchyma and the effective atomic number (Zeff ) spatial distribution was studied to show that the desired quantitative PBV maps are determined by the spatial maps of Zeff as , where a, b, and β are three constants. Namely, quantitative is determined by Zeff images instead of the iodine basis images. Perfusion maps were generated for four human subjects to demonstrate the differences between conventional iodine material image-based PBV (PBViodine ) derived from two-material decompositions and the proposed method. Among patients with pulmonary emboli, the proposed maps clearly show the perfusion defects while the PBViodine maps do not. Additionally, when there are no perfusion defects present in the derived PBV maps, no pulmonary emboli were diagnosed by an experienced thoracic radiologist. Effective atomic number-based quantitative PBV maps provide the needed sensitive and specific biomarker to quantify pulmonary perfusion defects.

Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs.

To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs. A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement. An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation. This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening. • Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.

Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features

To evaluate the inter-observer consistency for subsolid pulmonary nodule radiomic features. Subsolid nodules were selected by reviewing radiology reports of CT examinations performed December 1, 2015 to April 1, 2016. Patients with CTs at two time points were included in this study. There were 55 patients with subsolid nodules, of whom 14 had two nodules. Of 69 subsolid nodules, 66 were persistent at the second time point, yielding 135 lesions for segmentation. Two thoracic radiologists and an imaging fellow segmented the lesions using a semi-automated volumetry algorithm (Syngo.via Vb20, Siemens). Coefficient of variation (CV) was used to assess consistency of 91 quantitative measures extracted from the subsolid nodule segmentations, including first and higher order texture features. The accuracy of segmentation was visually graded by an experienced thoracic radiologist. Influencing factors on radiomic feature consistency and segmentation accuracy were assessed using generalized estimating equation analyses and the Exact Mann-Whitney test. Mean patient age was 71 (38-93 years), with 39 women and 16 men. Mean nodule volume was 1.39mL, range .03-48.2mL, for 135 nodules. Several radiomic features showed high inter-reader consistency (CV<5%), including entropy, uniformity, sphericity, and spherical disproportion. Descriptors such as surface area and energy had low consistency across inter-reader segmentations (CV>10%). Nodule percent solid component and attenuation influenced inter-reader variability of some radiomic features. The presence of contrast did not significantly affect the consistency of subsolid nodule radiomic features. Near perfect segmentation, within 5% of actual nodule size, was achieved in 68% of segmentations, and very good segmentation, within 25% of actual nodule size, in 94%. Morphologic features including nodule margin and shape (each p <0.01), and presence of air bronchograms (p = 0.004), bubble lucencies (p = 0.02) and broad pleural contact (p < 0.01) significantly affected the probability of near perfect segmentation. Stroke angle (p = 0.001) and length (p < 0.001) also significantly influenced probability of near perfect segmentation. The inter-observer consistency of radiomic features for subsolid pulmonary nodules varies, with high consistency for several features, including sphericity, spherical disproportion, and first and higher order entropy, and normalized non-uniformity. Nodule morphology influences the consistency of subsolid nodule radiomic features, and the accuracy of subsolid nodule segmentation.

Histopathologic and Radiologic Assessment of Unused Donor Lungs: When to Decline or Not?

Donor organ shortage, in combination with a 70% decline rate of offered donor lungs, results in significant wait list mortality and time with poor quality of life. Assessing donor lungs for transplant suitability is based on donors' history, gas exchange, chest X-ray, bronchoscopy, and in situ visual inspection; but donor lung acceptance remains subjective. We performed an in depth histopathologic and radiologic assessment of retrieved but unused donor lungs. We assessed 62 donor lungs not used for transplantation for various reasons between 2010 and 2019. These lungs were air-inflated, frozen, scanned with computed tomography, systematically sampled on 4 different locations, assessed macroscopically and microscopically by two experienced lung pathologists and by an experienced thoracic radiologist, all blinded for the reason of decline. Reasons for non-use are summarized in the figure. Thirty-nine (63%) lungs were not used for allograft-related reasons. In 13/39 (33%) lungs, the reason for decline could not be confirmed by histologic assessment (emboli, n=7; pneumonia, n=3; contusion, n=2; emphysema, n=1), in an additional 8/39 (21%) lungs, histologic abnormalities were only considered focal and mild (emphysema, n=5; pneumonia, n=3). In 16/39 (41%) lungs, radiology could not confirm the reason for decline. Twenty-three (37%) of 62 unused donor lungs were not transplanted due to extra pulmonary causes, of which 3 (13%) lungs displayed severe histologic abnormalities (pneumonia, n=2; emphysema, n=1), in addition to mild emphysema in 9 (39%) lungs and mild bronchopneumonia in 1 lung (4%). Radiology revealed ground-glass opacities in 8/23 (35%) and emphysema in 4/23 (17%) of these donor lungs. Histologic and radiologic assessment of unused donor lungs revealed substantial discrepancy with the clinical reason for decline. Prior chest CT imaging and frozen sections in selected cases might contribute to adequate donor assessment and augment current yield of donor lungs.

A comparison of ultra-high-resolution CT target scan versus conventional CT target reconstruction in the evaluation of ground-glass-nodule-like lung adenocarcinoma

The aim of this study was to determine whether the clinical value of scanned computed tomography (CT) images is higher when using ultra-high-resolution CT (U-HRCT) target scanning than conventional CT target reconstruction scanning in the evaluation of ground-glass-nodule (GGN)-like lung adenocarcinoma. A total of 91 consecutive patients with isolated GGN-like lung adenocarcinoma were included in this study from April 2017 to June 2018. U-HRCT and conventional CT scans were conducted in all enrolled patients. Two experienced thoracic radiologists independently assessed image quality and made diagnoses. Based on the pathological results, the accuracies of U-HRCT target scanning and conventional CT target reconstruction for detecting morphological features on CT, including spiculation of GGNs, bronchial vascular bundles, solid components in the nodules, burr, vacuole, air bronchial signs, and fissure distortion, were calculated. All statistical analyses were performed using SPSS 17.0 software. Enumeration data were tested using the Chi-square test. A P value of <0.05 was considered statistically significant. When both techniques were compared with the pathological findings, the detection rate for CT images obtained using U-HRCT target scanning and conventional CT target reconstruction with regard to the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules were 78% vs. 61.5%, 72.5% vs. 54.9%, 65.9% vs. 49.5%, respectively. The presence of the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules in U-HRCT target scanning was significantly higher than that in conventional CT target reconstruction (all P<0.05). However, no significant difference was observed between the two techniques with regard to the burr, vacuole, air bronchial signs, and fissure distortion (all P>0.05). When viewing GGNs, the detection rate was higher for U-HRCT target scanning than for conventional CT target reconstruction, and this improvement significantly enhanced the diagnostic accuracy of early lung adenocarcinoma.

Variabilities in Reference Standard by Radiologists and Performance Assessment in Detection of Pulmonary Embolism in CT Pulmonary Angiography.

Annotating lesion locations by radiologists' manual marking is a key step to provide reference standard for the training and testing of a computer-aided detection system by supervised machine learning. Inter-reader variability is not uncommon in readings even by expert radiologists. This study evaluated the variability of the radiologist-identified pulmonary emboli (PEs) to demonstrate the importance of improving the reliability of the reference standard by a multi-step process for performance evaluation. In an initial reading of 40 CTPA PE cases, two experienced thoracic radiologists independently marked the PE locations. For markings from the two radiologists that did not agree, each radiologist re-read the cases independently to assess the discordant markings. Finally, for markings that still disagreed after the second reading, the two radiologists read together to reach a consensus. The variability of radiologists was evaluated by analyzing the agreement between two radiologists. For the 40 cases, 475 and 514 PEs were identified by radiologists R1 and R2 in the initial independent readings, respectively. For a total of 545 marks by the two radiologists, 81.5% (444/545) of the marks agreed but 101 marks in 36 cases differed. After consensus, 65 (64.4%) and 36 (35.6%) of the 101 marks were determined to be true PEs and false positives (FPs), respectively. Of these, 48 and 17 were false negatives (FNs) and 14 and 22 were FPs by R1 and R2, respectively. Our study demonstrated that there is substantial variability in reference standards provided by radiologists, which impacts the performance assessment of a lesion detection system. Combination of multiple radiologists' readings and consensus is needed to improve the reliability of a reference standard.

Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists

Risk models have been developed that include the subject's pretest risk profile and imaging findings to predict the risk of cancer in an objective way. We assessed the accuracy of the Vancouver Lung Cancer Risk Prediction Model compared with that of trainee and experienced radiologists using a subset of size-matched nodules from the National Lung Screening Trial (NLST). One hundred cases from the NLST database were selected (size range, 4-20mm), including 20 proven cancers and 80 size-matched benign nodules. Three experienced thoracic radiologists and three trainee radiologists were asked to estimate the likelihood of cancer in each case, first independently, and then with knowledge of the model's risk prediction. The results generated by the model alone also were estimated using receiver operating characteristic (ROC) analysis. The area under the ROC curve (AUC) for each viewing condition was calculated, and statistical significance in their differences was tested by using the Dorfman-Berbaum-Metz method. Human observers were more accurate (AUC value of 0.85 ± 0.05 [SD]) than was the model (0.77 ± 0.06) in estimating the risk of malignancy (P= .0010), and use of the model did not improve their accuracy (0.84 ± 0.06). Experienced radiologists performed better than did trainees. Human observers could distinguish benign from malignant nodule morphology more accurately than could the model, which relies mainly on nodule size for risk estimation. Experienced and trainee radiologists had superior ability to predict the risk of cancer in size-matched nodules from a screening trial compared with that of the Vancouver model, and use of the model did not improve their accuracy.

Chest computed tomography imaging improves potential lung donor assessment

ObjectiveChest computed tomography (CT) imaging is being increasingly used for potential lung donor assessment. However, the efficacy of CT imaging in this setting remains unknown. We hypothesize that chest CT imaging independently affects the decision-making process in donor lung utilization. MethodsWe conducted a retrospective cohort study of all adult donation after brain death donors managed through our local organ procurement organization from June 2011 to November 2016. An experienced thoracic radiologist independently reviewed donor chest CT and chest x-ray images in a blinded, standardized manner to determine the presence of structural lung disease (eg, emphysema, interstitial lung disease [ILD]) and acute abnormalities (eg, traumatic lung injury [TLI]). Distinct models of lung utilization were fit to groups with initial partial pressure of oxygen (iPaO2) ≤300 mm Hg (suboptimal) and iPaO2 >300 mm Hg (optimal). ResultsThe organ procurement organization managed 753 donors during the study period, with a lung utilization rate ([lung donors/all organ donors] × 100) of 36.5% (275 of 753). Four hundred forty-five (59.1%) donors received chest CT imaging, revealing emphysema (13.7%), ILD (2.5%), and TLI (7.2%). In univariate analysis, findings of TLI (odds ratio [OR], 2.23; 95% confidence interval [CI], 1.08-4.61) were positively associated with lung utilization, whereas findings of emphysema (OR, 0.18; CI, 0.08-0.40) were negatively associated with utilization. In multivariate analysis, CT findings of emphysema (OR, 0.21; CI 0.08-0.54) remained negatively associated with utilization. No potential donors with CT findings of ILD became lung donors. After controlling for chest x-ray findings, chest CT imaging findings of structural lung disease remained negatively associated with utilization (P = .0001). Lung utilization rate in the suboptimal and optimal iPaO2 populations was 35.1% and 41.4%, respectively, and CT findings of emphysema had a significant association with nonutilization in both groups. ConclusionsIn the evaluation of potential lung donors, chest CT imaging findings of structural lung disease, such as emphysema and ILD, have a significant negative association with lung utilization. Our findings suggest that chest CT imaging might be an important adjunct to conventional lung donor assessment criteria.

Detection of Subsolid Nodules in Lung Cancer Screening

The aim of this study was to compare computer-aided diagnosis (CAD) and visual reading for the detection of subsolid nodules (SSNs) in volumetrl measuremic low-dose computed tomography (LDCT) for lung cancer screening. Prospective visual detection (VD) and manuaent of SSN were performed in the 2303 baseline volumetric LDCTs of the Multicenter Italian Lung Detection trial. Baseline and 2- and 4-year LDCTs underwent retrospective CAD analysis, subsequently reviewed by 2 experienced thoracic radiologists. The reference standard was defined by the cumulative number of SSNs detected by any reading method between VD and CAD. The number of false-positive CAD marks per scan (FP/scan) was calculated. The positive predictive value of CAD was quantified per nodule (PPV) and per screenee (PPV). The sensitivity and negative predictive value were compared between CAD and VD. The longitudinal 3-time-point sensitivity of CAD was calculated in the subgroup of persistent SSNs seen by VD (ratio between the prevalent SSNs detected by CAD through 3 time points and the total number of persistent prevalent SSNs detected by VD) to test the sensitivity of iterated CAD analysis during a screening program. Semiautomatic characteristics (diameter, volume, and mass; both for whole nodule and solid component) were compared between SSN detected CAD-only or VD-only to investigate whether either reading method could suffer from specific sensitivity weakness related to SSN features. Semiautomatic and manual diameters were compared using Spearman ρ correlation and Bland-Altman plot. Computer-aided diagnosis and VD detected a total of 194 SSNs in 6.7% (155/2,303) of screenees at baseline LDCT. The CAD showed mean FP/scan of 0.26 (604/2,303); PPV 22.5% (175/779) for any SSN, with 54.4% (37/68) for PSN and 19.4% for NSN (138/711; P < 0.001); PPV 25.6% (137/536). The sensitivity of CAD was superior to that of VD (88.4% and 34.2%, P < 0.001), as well as negative predictive value (99.2% and 95.5%, P < 0.001). The longitudinal 3-time-point sensitivity of CAD was 87.5% (42/48). There was no influence of semiautomatic characteristics on the performance of either reading method. The diameter of the solid component in PSN was larger by CAD compared with manual measurement. At baseline, CAD detected 3 of 4 SSNs, which were first overlooked by VD and subsequently evolved to lung cancer. Computer-aided diagnosis and VD as concurrent reading methods showed complementary performance, with CAD having a higher sensitivity, especially for PSN, but requiring visual confirmation to reduce false-positive calls. Computer-aided diagnosis and VD should be jointly used for LDCT reading to reduce false-negatives of either lone method. The semiautomatic measurement of solid core showed systematic shift toward a larger diameter, potentially resulting in an up-shift within Lung CT Screening Reporting and Data System classification.

Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX.

Reference Values for Central Airway Dimensions on CT Images of Children and Adolescents.

The purpose of this study was to acquire normative data on central airway dimensions on chest CT scans in the pediatric population. Chest CT findings reported as normal by a radiologist were collected retrospectively at 10 international centers. An experienced and independent thoracic radiologist reevaluated all CT scans for image quality and for normal findings. Semiautomated image analysis was performed to measure dimensions of the trachea and right and left main bronchi at inspiration. Intrathoracic tracheal length was measured from carina to thorax inlet. Cross-sectional area and short and long axes were measured perpendicular to the longitudinal airway axis starting from the carina every centimeter upward for the trachea and every 0.5 cm downward for the main bronchi. The effects on airway diameters of age, sex, intrathoracic tracheal length, and distance from the carina were investigated by use of mixed-effects models analysis. Among 1160 CT scans collected, 388 were evaluated as normal by the independent radiologist with sufficient image quality and adequate inspiratory volume level. Central airways were successfully semiautomatically analyzed in 294 of 388 CT studies. Age, sex, intrathoracic tracheal length, and distance from carina were all significant predictors in the models for tracheal and right and left main bronchial diameters (p < 0.001). The central airway dimensions increased with age up to 20 years, and dimensions were larger in male than in female adolescents. Normative data were determined for the central airways of children and adolescents. Central airway dimensions depended on distance from the carina and on intrathoracic tracheal length.

Feasibility of free breathing Lung MRI for Radiotherapy using non-Cartesian k-space acquisition schemes.

To test a free-breathing MRI protocol for anatomical and functional assessment during lung cancer radiotherapy by assessing two non-Cartesian acquisition schemes based on T1 weighted 3D gradient recall echo sequence: (i) stack-of stars (StarVIBE) and (ii) spiral (SpiralVIBE) trajectories. MR images on five healthy volunteers were acquired on a wide bore 3T scanner (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Anatomical image quality was assessed on: (1) free breathing (StarVIBE), (2) the standard clinical sequence (volumetric interpolated breath-hold examination, VIBE) acquired in a 20 second (s) compliant breath-hold and (3) 20 s non-compliant breath-hold. For functional assessment, StarVIBE and the current standard breath-hold time-resolved angiography with stochastic trajectories (TWIST) sequence were run as multiphase acquisitions to replicate dynamic contrast enhancement (DCE) in one healthy volunteer. The potential application of the SpiralVIBE sequence for lung parenchymal imaging was assessed on one healthy volunteer. Ten patients with lung cancer were subsequently imaged with the StarVIBE and SpiralVIBE sequences for anatomical and structural assessment. For functional assessment, free-breathing StarVIBE DCE protocol was compared with breath-hold TWIST sequences on four prior lung cancer patients with similar tumour locations. Image quality was evaluated independently and blinded to sequence information by an experienced thoracic radiologist. For anatomical assessment, the compliant breath-hold VIBE sequence was better than free-breathing StarVIBE. However, in the presence of a non-compliant breath-hold, StarVIBE was superior. For functional assessment, StarVIBE outperformed the standard sequence and was shown to provide robust DCE data in the presence of motion. The ultrashort echo of the SpiralVIBE sequence enabled visualisation of lung parenchyma. The two non-Cartesian acquisition sequences, StarVIBE and SpiralVIBE, provide a free-breathing imaging protocol of the lung with sufficient image quality to permit anatomical, structural and functional assessment during radiotherapy. Advances in knowledge: Novel application of non-Cartesian MRI sequences for lung cancer imaging for radiotherapy. Illustration of SpiralVIBE UTE sequence as a promising sequence for lung structural imaging during lung radiotherapy.