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Background Definitions for central lung cancer have been ambiguous in guidelines, causing difficulty in selecting candidates for invasive mediastinal staging among patients with radiologically node-negative, early-stage lung cancer. Research Question What is the optimal definition for central lung cancer, which is robust to inter-reader and institutional variation, to select candidates for invasive mediastinal staging among those with clinical T1N0M0 lung cancer? Study Design and Methods Two retrospective cohorts were evaluated for the associations of central lung cancer according to 13 definitions based on chest CT with occult nodal metastasis. Univariable and multivariable ordinal logistic regression analyses were performed with the pathological N category as an ordinal outcome. Robust definitions, which retained statistical significance across multi-reader, dual-institutional datasets, were identified. For these definitions, binary diagnostic performance and inter-reader agreement were investigated. Results In the two cohorts, 807 patients (median age, 63 years; interquartile range, 56-71 years; 410 women; 33 pN1, 48 pN2, and 1 pN3) and 510 patients (median age, 65 years; interquartile range, 58-71 years; 267 women; 33 pN1, 20 pN2, and no pN3) were included, respectively. Three definitions robust to inter-reader variation and dataset heterogeneity were identified: definition 7 (concentric lines arising from the midline, inner one-third, medial margin; adjusted odds ratio [OR], 2.01; 95% confidence interval [CI], 1.13-3.51; P=0.02), definition 10 (location index-based inner one-third, center; adjusted OR, 3.60; 95% CI, 1.49-8.25; P=0.003), and definition 12 (location index-based inner one-third, medial margin; adjusted OR, 3.57; 95% CI, 1.91-6.52; P Interpretation Three definitions exhibited robust associations with occult nodal metastasis. However, selecting candidates for invasive mediastinal staging solely based on a central tumor location would be suboptimal.

To identify the agreement on Lung CT Screening Reporting and Data System 4X categorization between radiologists and an expert-adjudicated reference standard and to investigate whether training led to improvement of the agreement measures and diagnostic potential for lung cancer. Category 4 nodules in the Korean Lung Cancer Screening Project were identified retrospectively, and each 4X nodule was matched with one 4A or 4B nodule. An expert panel re-evaluated the categories and determined the reference standard. Nineteen radiologists were asked to determine the presence of CT features of malignancy and 4X categorization for each nodule. A review was performed in two sessions, and training material was given after session 1. Agreement on 4X categorization between radiologists and the expert-adjudicated reference standard and agreement between radiologist-assessed 4X categorization and lung cancer diagnosis were evaluated. The 48 expert-adjudicated 4X nodules and 64 non-4X nodules were evenly distributed in each session. The proportion of category 4X decreased after training (56.4% ± 16.9% vs. 33.4% ± 8.0%; p 0.05). All agreement measures between radiologist-assessed 4X categorization and lung cancer diagnosis increased in session 2 (p < 0.05). Radiologist training can improve reader agreement on 4X categorization, leading to enhanced diagnostic performance for lung cancer. • Agreement on 4X categorization between radiologists and an expert-adjudicated reference standard was initially poor, but improved significantly after training. • The mean proportion of 4X categorization by 19 radiologists decreased from 56.4% ± 16.9% in session 1 to 33.4% ± 8.0% in session 2. • All agreement measures between the 4X categorization and lung cancer diagnosis increased significantly in session 2, implying that appropriate training and guidance increased the diagnostic potential of category 4X.

Objectives To quantify the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis (IPF) using the Gaussian curvature analysis for evaluating disease severity and predicting survival. Methods We retrospectively included 104 IPF patients and 52 controls who underwent baseline chest CT scans. Normal lungs below − 500 HU were segmented, and the boundary was three-dimensionally reconstructed using in-house software. Gaussian curvature analysis provided histogram features on the heterogeneity of the fibrosis boundary. We analyzed the correlations between histogram features and the gender-age-physiology (GAP) and CT fibrosis scores. We built a regression model to predict diffusing capacity of carbon monoxide (DLCO) using the histogram features and calculated the modified GAP (mGAP) score by replacing DLCO with the predicted DLCO. The performances of the GAP, CT-GAP, and mGAP scores were compared using 100 repeated random-split sets. Results Patients with moderate-to-severe IPF had more numerous Gaussian curvatures at the fibrosis boundary, lower uniformity, and lower 10th to 30th percentiles of Gaussian curvature than controls or patients with mild IPF (all p < 0.0033). The 20th percentile was most significantly correlated with the GAP score (r = − 0.357; p < 0.001) and the CT fibrosis score (r = − 0.343; p = 0.001). More numerous Gaussian curvatures, higher entropy, lower uniformity, and 10th to 30th percentiles (p < 0.001–0.041) were associated with mortality. The mGAP score was comparable to the GAP and CT-GAP scores for survival prediction (mean C-indices, 0.76 vs. 0.79 vs. 0.77, respectively). Conclusions Gaussian curvatures of fibrosis boundaries became more heterogeneous as the disease progressed, and heterogeneity was negatively associated with survival in IPF. Key Points • Gaussian curvature of the fibrotic lung boundary was more heterogeneous in patients with moderate-to-severe IPF than those with mild IPF or normal controls. • The 20th percentile of the Gaussian curvature of the fibrosis boundary was linearly correlated with the GAP score and the CT fibrosis score. • A modified GAP score that replaced the diffusing capacity of carbon monoxide with a composite measure using histogram features of the Gaussian curvature of the fibrosis boundary showed a comparable ability to predict survival to both the GAP and the CT-GAP score. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-020-07594-y.

To validate and compare the performance of the Brock model and Lung CT Screening Reporting and Data System (Lung-RADS) on nodules detected by baseline CT screening. We performed a secondary analysis of the Korean Lung Cancer Screening Project (K-LUCAS; ClinicalTrials.gov , NCT03394703), a nationwide, multicenter, prospective cohort study. From April 2017 to December 2018, low-dose CT screening was performed on high-risk subjects. Discrimination and calibration of Brock models 2a and 2b (i.e., full model without and with spiculation, respectively) were assessed, and discrimination was compared with that of Lung-RADS, which utilized subjective assessment categories 2b (b stands for benign) and 4X. Of the 13,150 subjects, 4578 were eligible (median age 62 years; 4458 men; 9929 nodules including 40 lung cancers). Areas under the receiver operating characteristic curve were 0.96 (IQR 0.92–0.99) for Brock model 2a, 0.96 (IQR 0.92–0.99) for Brock model 2b, and 0.95 (IQR 0.91–0.99) for Lung-RADS (p = 0.32 and p = 0.34, respectively). At an equivalent cutoff of 5%, Brock model 2b (sensitivity 87.5% [35/40]; specificity 93.6% [9259/9889]; positive predictive value [PPV] 5.3% [35/665]; negative predictive value [NPV] 99.9% [9259/9264]) and Lung-RADS (sensitivity 87.5% [35/40]; specificity 93.3% [9222/9889]; PPV 5.0% [35/702]; NPV 99.9% [9222/9227]) performed similarly well (all p > 0.05). The calibration performance of both Brock models 2a and 2b was poor (both p < 0.001). Lung-RADS, when reinforced with visual assessment–based categories, has a similar diagnostic performance to the Brock model for baseline CT scans. • Brock model 2b and Lung CT Screening Reporting and Data System (Lung-RADS) demonstrated a similar discrimination performance for lung cancer in the baseline CT screening (areas under the receiver operating characteristic curve 0.96 vs. 0.95; p = 0.34). • When visual assessment–based categories were removed from Lung-RADS, specificity and positive predictive value were lower than those of Brock model 2b (p = 0.001 and p = 0.02, respectively). • The Brock model showed poor calibration (p < 0.001).

To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer. In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed. The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67–0.84), which was comparable to those of board-certified radiologists (AUC, 0.73–0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03–1.11; p

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. MATERIALS AND METHODS: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. RESULTS: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). CONCLUSION: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

Background Low-dose CT screening for lung cancer in a tuberculosis-endemic country may be less effective because of false-positive results caused by tuberculosis sequelae. Purpose To evaluate the impact of tuberculosis sequelae at CT screening according to the American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) using data from the Korean Lung Cancer Screening Project (K-LUCAS). Materials and Methods This is a secondary analysis of K-LUCAS (ClinicalTrials.gov identifier NCT03394703), a nationwide Asian population-based, multicenter, prospective cohort study. Participants at high risk for lung cancer were enrolled between April 2017 and December 2018. Associations of tuberculosis sequelae with a positive screening result for lung cancer (defined as Lung-RADS categories 3 or 4) and diagnosis of lung cancer were analyzed with multivariable logistic regression. The diagnostic performance of Lung-RADS in predicting lung cancer was compared between participants with and participants without tuberculosis sequelae by using the χ

To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model and expert radiologists. This study included 525 patients (309 women; median, 62 years) to develop models, and an independent cohort of 101 patients (57 women; median, 66 years) was used for validation. A size-based logistic model and deep learning models using 2.5-dimension (2.5D) and three-dimension (3D) CT images were developed to discriminate IAC from less invasive pathologies. Overall performance, discrimination, and calibration were assessed. Diagnostic performances of the three thoracic radiologists were compared with those of the deep learning model. The overall performances of the deep learning models (Brier score, 0.122 for the 2.5D DenseNet and 0.121 for the 3D DenseNet) were superior to those of the size-based logistic model (Brier score, 0.198). The area under the receiver operating characteristic curve (AUC) of the 2.5D DenseNet (0.921) was significantly higher than that of the 3D DenseNet (0.835; p = 0.037) and the size-based logistic model (0.836; p = 0.009). At equally high sensitivities of 90%, the 2.5D DenseNet showed significantly higher specificity (88.2%; all p

Our study aimed to investigate the independent prognostic values of consolidation-to-tumor ratio (CTR) and tumor disappearance ratio (TDR) after adjustment for the conventional prognostic factors and the eighth edition clinical T category for patients with resected lung adenocarcinomas.This retrospective study included 691 patients (281 men and 410 women; median age, 63 years) with resected lung adenocarcinomas (clinical T1N0M0). The prognostic implications for disease-free survival (DFS) of CTR and TDR in continuous and categorical forms were analyzed using multivariable-adjusted Cox regression analysis, including multiple clinico-radiological prognostic factors and the clinical T category based on the solid portion measurement. Analysis was performed for the total study population and for two part-solid nodule subgroups (cT1mi/cT1a to cT1c and cT1mi/cT1a to cT1b, respectively).For the total study population, CTR and TDR were not selected in the multivariable Cox regression models, which indicated that these are not independent prognostic factors. Age (adjusted HR: 1.026; P = 0.022) and clinical T category (adjusted HR for cT1b: 3.475; P = 0.019; adjusted HR for cT1c: 9.938; P 0.001) were independently associated with DFS. For the part-solid nodule subgroups, multivariable-adjusted HRs for CTR and TDR were not statistically significant (all P 0.05).CTR and TDR were not independent prognostic factors. Preoperative prognostication based on clinical T category would be sufficient without further stratification according to CTR or TDR.

BackgroundPathologic visceral pleural invasion (pVPI) leads to upstaging from T1 to T2. However, it is unclear whether the CT features for pVPI can be reliably used as a clinical T2 descriptor for preoperative staging.PurposeTo validate the diagnostic accuracy and analyze the prognostic value of CT findings for the prediction of pVPI in patients with resected node-negative lung adenocarcinoma.Materials and MethodsThis retrospective cohort study included clinical T1N0M0 adenocarcinomas resected between 2009 and 2015. The diagnostic CT findings suggestive of pVPI were evaluated by a thoracic radiologist. The accuracy of diagnostic CT findings in relation to pVPI and accuracy for disease-free survival (DFS) were evaluated by using test performance metrics and multivariable Cox regression analysis, respectively.ResultsThe authors analyzed 695 patients (median age, 63 years; 411 women). Data for pVPI were not available in six patients. The accuracy of CT features for pVPI ranged from 62.7% (432 of 689 patients) to 72.3% (498 of 689 patients). Positive predictive values ranged from 44.1% (173 of 392 patients) to 56.4% (88 of 156 patients), which indicated that about half of the CT-based predictions were false-positive. Multivariable Cox regression models showed that none of the combinations of CT findings were independent predictors of DFS (adjusted hazard ratios, 1.40, 1.48, 1.06, and 1.21 for each combination; P > .05 for all). In addition, pVPI was not an independent prognostic factor (adjusted hazard ratio, 1.27; P = .26), whereas age and clinical T category were independent prognostic factors in all Cox models (P < .05 for all).ConclusionCT features of pathologic visceral pleural invasion (pVPI) have an accuracy of 62.7%-72.3%. CT features of pVPI were not independent prognostic factors for disease-free survival in clinical T1 lung adenocarcinomas. This argues against the use of CT features of visceral pleural invasion as T2 descriptors in the clinical staging of lung cancer.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Nishino in this issue.

Background To investigate the prognostic impact of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement for the overall survival (OS) of patients with surgically treated lung adenocarcinomas. Methods A total of 689 patients with stage I–III lung adenocarcinomas (male:female = 334:355; median age, 64 years) underwent complete surgical resection between 2007 and 2013. The prognostic impact of EGFR mutation and ALK rearrangement on OS was analyzed using Cox regression analysis. Certain clinicopathological prognostic factors (i.e., age, sex, smoking status, nodule type, solid portion size, pathologic stage, adenocarcinoma subtype, and history of adjuvant chemotherapy) were included for adjustments of the hazard ratio (HR). Results EGFR mutation was observed in 438 patients (64%) and ALK rearrangement was seen in 28 patients (4%). Multivariable‐adjusted Cox regression demonstrated that the prognostic effect of EGFR mutation on OS differed by age (HR, exp.[−5.199 + 0.064*age]). The adjusted HR for EGFR mutation was 0.14 (95% CI: 0.05–0.36; P 0.05). Conclusions EGFR mutation was independently prognostic of the long‐term outcomes of patients with surgically treated lung adenocarcinomas. A more favorable prognostic effect was seen in younger than in older patients. ALK rearrangement was not associated with OS.

Our study aimed at evaluating the prognostic implications of lung and mediastinal CT display window settings for solid portion measurements on the eighth-edition lung cancer staging system’s clinical T (cT) categorization. We retrospectively analyzed 691 surgically treated patients from 2009 to 2015 for clinical stage IA lung adenocarcinomas. Solid portions were measured at the lung and mediastinal window settings, respectively, and cT categories were determined for each measurement (cTlung and cTmediastinum). The prognostic power of the two cT factors for disease-free survival (DFS) was assessed using Cox regression, and concordance indices (C-indices) were compared using the Student t test. Subsequently, the patients were split into training and validation cohorts to calculate optimal cutoffs for the cT categorization of mediastinal window–based solid portions (cToptimal) and validate its prognostic performance. Both cTlung ((cT1b: adjusted HR, 3.547; p = 0.017), (cT1c: adjusted HR, 9.439; p 0.05). The prognostic performances of the cT categorizations at the lung and mediastinal windows were not significantly different. The current cT categorization based on the lung window measurement is appropriate as it stands. • Discriminatory power of the eighth-edition clinical T category was not significantly affected by the CT display window settings. • Given the facts that the lung window setting enables more sensitive detection of the solid portions and higher correlation with the pathological invasive components, our findings may support adherence to the usage of the lung window setting for the solid portion measurement per the current recommendations.

Background and purpose To validate a computed tomography (CT)-based deep learning prognostication model, originally developed for a surgical cohort, in patients with primary lung cancer undergoing stereotactic ablative radiotherapy (SABR). Materials and methods This retrospective study identified patients with clinical stage T1-2N0M0 lung cancer treated with SABR between 2013 and 2018. The outcomes were local recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS). The discrimination performance of the model, which extracted a quantitative score of cumulative risk for an adverse event up to 900 days, was evaluated using time-dependent receiver operating characteristic curve analysis. Multivariable Cox regression were performed to investigate the independent prognostic value of the model output adjusting for clinical factors including age, sex, smoking status, and clinical T category. Results In total, 135 patients (median age, 78 years; 101 men; 78 [57.8%] adenocarcinomas and 57 [42.2%] squamous cell carcinomas) were evaluated. Most patients (117/135) were treated with 48–60 Gy in four fractions. Median biologically effective dose was 150.0 Gy (interquartile range, 126.9, 150.0 Gy). For LRFS, the area under the curve (AUC) was 0.72 (95% confidence interval [CI]: 0.58, 0.87). The AUCs were 0.70 (95% CI: 0.60, 0.81) for DFS and 0.66 (95% CI: 0.54, 0.77) for OS. Model output was associated with LRFS (adjusted hazard ratio [HR], 1.043; 95% CI: 1.003, 1.085; P = 0.04), DFS (adjusted HR, 1.03; 95% CI: 1.01, 1.05; P = 0.008), and OS (adjusted HR, 1.025; 95% CI: 1.002, 1.047; P = 0.03). Conclusion This study showed external validity and transportability of the CT-based deep learning prognostication model for radiotherapy candidates.

Central lung cancer is an adverse prognostic factor for disease-free survival in patients with node-negative early-stage adenocarcinomas and can be determined either subjectively or quantitatively ...

Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss’ kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.

Pulmonary structures provide morphological basis for the vital functions to our body, which needs adequate airway flow, respiratory dynamics, lung ventilation, gas exchange, and lung perfusion closely interlinked with one another. For the CT evaluation of these functions, three-dimensional lung volumetry using the density mask techniques, dual-energy CT techniques, and cine and four-dimensional CT techniques may be used. To provide accurate and reproducible answers to clinical questions, we should first understand sources of variation influencing the results of these pulmonary functional CT exams. Then, we should know basic concepts, imaging techniques, and clinical applications of pulmonary functional CT. In addition to pulmonary anatomic details, pulmonary functional CT gives us new insights into lung physiology and ultimately opens new avenues in functional lung imaging.

Background The growth rate of thymic epithelial tumors (TETs) and thymic cysts was investigated to determine whether they can be differentiated and clinico‐radiological predictors of interval growth was identified. Methods This retrospective study included 122 patients with pathologically proven thymic cysts (n = 56) or TETs (n = 66) who underwent two serial chest computed tomography scans at least eight weeks apart. Average diameters and attenuation were measured, volume‐doubling times (VDTs) were calculated, and clinical characteristics were recorded. VDTs were compared using the log‐rank test. Predictors of growth were analyzed using the log‐rank test and Cox regression analysis. Results The frequency of growth did not differ significantly between TETs and thymic cysts (P = 0.279). The VDT of thymic cysts (median 324 days) was not significantly different from that of the TETs (median 475 days; P = 0.808). Water attenuation (≤ 20 Hounsfield units) predicted growth in thymic cysts (P = 0.016; hazard ratio 13.2, 95% confidence interval 1.6–107.3), while lesion size (> 17.2 mm) predicted growth in TETs (P = 0.008 for size, P = 0.029 for size*time). For the growing lesions, the positive and negative predictive values of water attenuation for thymic cysts were 93% and 80%, respectively. Conclusion The frequencies of interval growth and VDTs were indistinguishable between TETs and thymic cysts. Water attenuation and lesion size predicted growth in thymic cysts and TETs, respectively. Among the growing lesions, water attenuation was a differential feature of thymic cysts.

Background Deep learning models have the potential for lung cancer prognostication, but model output as an independent prognostic factor must be validated with clinical risk factors. Purpose To develop and validate a preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinoma. Materials and Methods In this retrospective study, a deep learning model was trained to extract prognostic information from preoperative CT examinations. Data set 1 for training, tuning, and internal validation consisted of patients with T1-4N0M0 adenocarcinoma resected between 2009 and 2015. Data set 2 for external validation included patients with clinical T1-2aN0M0 (stage I) adenocarcinomas resected in 2014. Discrimination was assessed by using Harrell C index and benchmarked against the clinical T category. The Greenwood-Nam-D'Agostino test was used for model calibration. The multivariable-adjusted hazard ratios (HRs) were analyzed with clinical prognostic factors by using the Cox regression. Results Evaluated were 800 patients (median age, 64 years; interquartile range, 56-70 years; 450 women) in data set 1 and 108 patients (median age, 63 years; interquartile range, 57-71 years; 60 women) in data set 2. The C indexes were 0.74-0.80 in the internal validation and 0.71-0.78 in the external validation, both comparable with the clinical T category (0.78 in the internal validation and 0.74 in the external validation; all P > .05). The model exhibited good calibration in all data sets (P > .05). Multivariable Cox regression revealed that model outputs were independent prognostic factors (hazard ratio [HR] of the categorical output, 2.5 [95% confidence interval {CI}: 1.03, 5.9; P = .04] in the internal validation and 3.6 [95% CI: 1.6, 8.5; P = .003] in the external validation). Other than the deep learning model, only smoking status (HR, 3.4; 95% CI: 1.4, 8.5; P = .007) contributed further to prediction of disease-free survival for patients after resection of clinical stage I adenocarcinomas. Conclusion A deep learning model for chest CT predicted disease-free survival for patients undergoing an operation for clinical stage I lung adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.

To investigate associations of the duration of voriconazole treatment and radiological response with relapse of invasive pulmonary aspergillosis (IPA) in immunocompromised patients, we explored the risk factors for IPA relapse after successful initial treatment. All patients with proven or probable IPA who had finished voriconazole treatment between 2005 and 2019 in a tertiary-care hospital were reviewed. IPA relapse was defined as re-diagnosis of proven or probable IPA at the same site within 1 year after treatment termination. Short course of voriconazole treatment was defined as a treatment less than 9 weeks, which is a median of the recommended minimum duration of therapy from the Infectious Disease Society of America. The radiological response was defined as a reduction in IPA burden by more than 50% on chest computed tomography. Of 87 patients who had completed voriconazole treatment, 14 (16.1%) experienced IPA relapse. Multivariable Cox regression identified that short voriconazole treatment duration (adjusted hazard ratio [aHR], 3.7; 95% confidence interval [CI], 1.1–12.3; P = 0.033) and radiological non-response (aHR, 4.6; 95% CI, 1.2–17.5; P = 0.026) were independently associated with relapse of IPA after adjusting for several clinical risk factors. Longer duration of therapy should be considered for those at higher risk of relapse.

Background Recent studies have suggested that chest CT scans could be used as a primary screening or diagnostic tool for coronavirus disease 2019 (COVID-19) in epidemic areas. Purpose To perform a meta-analysis to evaluate diagnostic performance measures, including predictive values of chest CT and initial reverse transcriptase polymerase chain reaction (RT-PCR). Materials and Methods Medline and Embase were searched from January 1, 2020, to April 3, 2020, for studies on COVID-19 that reported the sensitivity, specificity, or both of CT scans, RT-PCR assays, or both. The pooled sensitivity and specificity were estimated by using random-effects models. The actual prevalence (ie, the proportion of confirmed patients among those tested) in eight countries was obtained from web sources, and the predictive values were calculated. Meta-regression was performed to reveal the effect of potential explanatory factors on the diagnostic performance measures. Results The pooled sensitivity was 94% (95% confidence interval [CI]: 91%, 96%; I2 = 95%) for chest CT and 89% (95% CI: 81%, 94%; I2 = 90%) for RT-PCR. The pooled specificity was 37% (95% CI: 26%, 50%; I2 = 83%) for chest CT. The prevalence of COVID-19 outside China ranged from 1.0% to 22.9%. For chest CT scans, the positive predictive value (PPV) ranged from 1.5% to 30.7%, and the negative predictive value (NPV) ranged from 95.4% to 99.8%. For RT-PCR, the PPV ranged from 47.3% to 96.4%, whereas the NPV ranged from 96.8% to 99.9%. The sensitivity of CT was affected by the distribution of disease severity, the proportion of patients with comorbidities, and the proportion of asymptomatic patients (all P < .05). The sensitivity of RT-PCR was negatively associated with the proportion of elderly patients (P = .01). Conclusion Outside of China where there is a low prevalence of coronavirus disease 2019 (range, 1%-22.9%), chest CT screening of patients with suspected disease had low positive predictive value (range, 1.5%-30.7%). © RSNA, 2020 Online supplemental material is available for this article.

PURPOSE/OBJECTIVE(S) Consolidation immunotherapy becomes a new standard treatment after definitive chemoradiotherapy (CRT) in locally advanced non-small cell lung cancer (NSCLC). However, the efficacy of immunotherapy can be compromised by treatment-related immune suppression status. We aimed to evaluate clinical and dose-volumetric predictors of delayed lymphopenia at the time of consolidation immunotherapy after CRT in locally advanced NSCLC. MATERIALS/METHODS We retrospectively reviewed 318 patients with locally advanced non-small cell lung cancer who received definitive CRT from January 2012 to December 2020. Patients diagnosed between 2012 and 2018 were assigned as training set (n = 244), and rest were selected as validation set (n = 74). Absolute lymphocyte count (ALC) was recorded at baseline, during CRT, and 4 to 12 weeks after CRT. Dose-volume histogram parameters for planned target volume, whole body, heart, lung, great vessels, spleen, esophagus and thoracic vertebral bodies were evaluated. Dose-volume histogram parameters reported as mean, maximum and minimum dose with volume receiving 5 Gy (V5), 10 Gy (V10), 20 Gy (V20), 30 Gy (V30), 40 Gy (V40), 50 Gy (V50), and 60 Gy (V60). Overall survival (OS) was analyzed using Kaplan-Meier survival analysis, log-rank test, and Cox regression model. Area under the receiver operating characteristic curve (ROC) analysis and multivariate logistic regression were used to determine the predictors of severe lymphopenia. RESULTS Grade 3 lymphopenia (ALC nadir < 500/mm3) were 81.1%, 48.1%, and 11.4% cases during CRT, 0 to 4 weeks, and 4 to 12 weeks after CRT, respectively. Kaplan-Meier survival curves and multivariate cox regression analysis revealed that patients with delayed lymphopenia (grade 3 lymphopenia during 4 to 12 weeks after CRT) was associated with inferior overall survival (P < 0.0001). On multivariable logistic regression models, age (OR = 1.116; P = 0.018), baseline ALC (OR = 0.998; P = 0.010), albumin nadir during CRT (OR = 0.111, P = 0.001), and lung V5 (OR = 1.089, P < 0.001) were significant predictors for delayed lymphopenia. The prediction nomogram was developed according to the above four independent variables. The AUC value of the nomogram model was 0.928. In the validation set (n = 74) the performance was 0.859. CONCLUSION Delayed lymphopenia is an independent predictor of poor overall survival. Age, baseline ALC, albumin nadir during CRT, and lung V5 revealed as the dominant contributor to delayed lymphopenia at the time of consolidation immunotherapy after definitive chemoradiotherapy.

The objective of our study was to determine whether visceral pleural invasion (VPI) of T1-sized peripheral pulmonary adenocarcinomas manifesting as subsolid nodules (SSNs) abutting the pleural surface or associated with pleural tags can be predicted.Our study population consisted of 188 T1-sized peripheral pulmonary adenocarcinomas that appeared as SSNs (24 pure ground-glass nodules [GGNs] and 164 part-solid nodules) and underwent surgical resection between January 2007 and December 2013. Logistic regression analysis was performed to identify significant factors in predicting VPI.VPI occurred in 36 of 188 adenocarcinomas (19.1%). There were no cases of VPI in patients with pure GGNs. In part-solid nodules, there were significant differences regarding the presence of pleural contact, presence of pleural thickening, presence of solid portion abutting the pleura, nodule size, solid portion size, solid proportion, interface length, and length of the solid portion contacting the pleura (p0.05). Multivariate analysis revealed pleural contact (p0.001), pleural thickening (p = 0.003), solid proportion greater than 50% (p = 0.002), and nodule size greater than 20 mm (p = 0.015) as significant independent predictive features for VPI with adjusted odds ratios of 8.300, 3.966, 4.636, and 2.993, respectively.In part-solid nodules, the CT features of pleural contact, pleural thickening, solid proportion greater than 50%, and nodule size greater than 20 mm were shown to be significant indicators of VPI by T1-sized peripheral adenocarcinomas.

To retrospectively evaluate the feasibility of CT assessment-based direct surgical resection of part-solid nodules (PSNs) with solid components > 5 mm without preoperative percutaneous transthoracic needle biopsies (PTNBs). From January 2009–December 2014, 85 PSNs with solid components > 5 mm on CT were included. Preoperative PTNBs were performed for 41 PSNs (biopsy group) and CT assessment-based direct resections were performed for 44 PSNs (direct surgery group). Diagnostic accuracy and complication rates of the groups were compared. Pathological results of 83 PSNs excluding two indeterminate nodules included 76 adenocarcinomas (91.6%), two adenocarcinomas in situ (2.4%) and five benign lesions (6.0%). In the biopsy group, the overall sensitivity, specificity and accuracy for the diagnosis of adenocarcinoma were 78.9% (30/38), 100% (1/1) and 79.5% (31/39), respectively. Pneumothorax and haemoptysis occurred in 11 procedures (26.8%). In the direct surgery group, the respective values for the diagnosis of adenocarcinoma were 100% (38/38), 0% (0/6) and 86.4% (38/44), respectively. Seven pneumothoraces (15.9%); no haemoptysis occurred during localization procedures. There were no significant differences in diagnostic accuracy (P = 0.559) between the two groups. CT assessment-based direct resection can be reasonable for PSNs with solid part > 5 mm. • 91.6% of PSNs with solid component > 5 mm were adenocarcinomas. • PTNBs for PSNs with solid component > 5 mm had 79.5% accuracy. • CT-based resection for PSNs with solid component > 5 mm had 86.4% accuracy. • CT-based resection without biopsy can be a reasonable option in routine practice.

Background Invasive pulmonary aspergillosis (IPA) is a life-threatening opportunistic infection which usually occurs in immunocompromised patients. Recommended duration of voriconazole therapy is a minimum of 6-12 weeks for IPA, despite the lack of any firm evidence. In addition, risk factors for relapse of IPA are still unclear. Here, we explored risk factors for IPA relapse after initial treatment. Methods All patients with proven or probable IPA who had finished voriconazole treatment between 2005 and 2019 in a tertiary-care hospital were reviewed. IPA relapse was defined as re-diagnosis of proven or probable IPA at the same site within 1 year after treatment termination. Short course of voriconazole treatment was defined as a treatment less than 9 weeks, which is a median of the recommended minimum duration of therapy from the Infectious Disease Society of America. The radiological response was defined as a reduction in IPA burden by more than 50% on chest computed tomography (CT). Results Of 87 patients who had completed voriconazole treatment, 14 (16.1%) experienced IPA relapse. Multivariable Cox regression identified that short voriconazole treatment duration (adjusted hazard ratio [aHR], 3.7; 95% confidence interval [CI], 1.1–12.3; P=0.033) and radiological non-response (aHR, 4.6; 95% CI, 1.2–17.5; P=0.026) were independently associated with relapse of IPA after adjusting for several clinical risk factors. Conclusion Less improvement in CT, and short duration of voriconazole therapy were the independent risk factors for relapse after treatment of IPA. Longer duration of therapy should be considered for those at higher risk of relapse. Disclosures All Authors: No reported disclosures

To evaluate the calibration of a deep learning (DL) model in a diagnostic cohort and to improve model’s calibration through recalibration procedures. Chest radiographs (CRs) from 1135 consecutive patients (M:F = 582:553; mean age, 52.6 years) who visited our emergency department were included. A commercialized DL model was utilized to identify abnormal CRs, with a continuous probability score for each CR. After evaluation of the model calibration, eight different methods were used to recalibrate the original model based on the probability score. The original model outputs were recalibrated using 681 randomly sampled CRs and validated using the remaining 454 CRs. The Brier score for overall performance, average and maximum calibration error, absolute Spiegelhalter’s Z for calibration, and area under the receiver operating characteristic curve (AUROC) for discrimination were evaluated in 1000-times repeated, randomly split datasets. The original model tended to overestimate the likelihood for the presence of abnormalities, exhibiting average and maximum calibration error of 0.069 and 0.179, respectively; an absolute Spiegelhalter’s Z value of 2.349; and an AUROC of 0.949. After recalibration, significant improvements in the average (range, 0.015–0.036) and maximum (range, 0.057–0.172) calibration errors were observed in eight and five methods, respectively. Significant improvement in absolute Spiegelhalter’s Z (range, 0.809–4.439) was observed in only one method (the recalibration constant). Discriminations were preserved in six methods (AUROC, 0.909–0.949). The calibration of DL algorithm can be augmented through simple recalibration procedures. Improved calibration may enhance the interpretability and credibility of the model for users. • A deep learning model tended to overestimate the likelihood of the presence of abnormalities in chest radiographs. • Simple recalibration of the deep learning model using output scores could improve the calibration of model while maintaining discrimination. • Improved calibration of a deep learning model may enhance the interpretability and the credibility of the model for users.

There is controversy regarding the clinical T (cT) category of lung adenocarcinomas that manifest as part-solid nodules (PSNs). We aimed to validate the cT category and to evaluate the independent prognostic role of the nodule type (i.e., part-solid versus solid).We retrospectively evaluated the prognostic value of clinico-radiologic factors regarding the overall survival of patients with clinical stage IA lung adenocarcinomas that were resected between 2008 and 2014. cT Category, nodule type, and their interaction term were included in the multivariable Cox regression analysis with other variables. In addition, a mixture cure model analysis was performed to investigate the association between the covariates and long-term survival.A total of 744 patients (420 women; 362 PSNs; median age, 63 y) were included. The multivariable-adjusted hazard ratio (HR) of the nodule type was not significant (1.30, 95% confidence interval [CI]: 0.80-2.10, p = 0.291). However, the cT categories were significantly associated with overall survival (HR of cT1b, 2.33 [95% CI: 1.07-5.06, p = 0.033]; HR of cT1c, 5.74 [95% CI: 2.51-13.12, p0.001]). There were no interactions between the nodule type and the cT categories (all p0.05). The multivariable mixture cure model revealed that solid nodules were associated with a decreased probability of long-term survival (OR = 0.40, 95% CI: 0.18-0.92, p = 0.030). In addition, cT1c was a negative predictor of long-term survival (OR = 0.26, 95% CI: 0.07-0.94, p = 0.040).The cT categorization system is valid for PSNs and solid nodules. Nevertheless, PSNs are a prognostic factor associated with long-term survival.

To perform test-retest reproducibility analyses for deep learning–based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations, and to investigate the robustness of DLAD to simulated post-processing and positional changes. This retrospective study included patients with pulmonary nodules resected in 2017. Preoperative CRs without interval changes were used. Test-retest reproducibility was analyzed in terms of median differences of abnormality scores, intraclass correlation coefficients (ICC), and 95% limits of agreement (LoA). Factors associated with test-retest variation were investigated using univariable and multivariable analyses. Shifts in classification between the two CRs were analyzed using pre-determined cutoffs. Radiograph post-processing (blurring and sharpening) and positional changes (translations in x- and y-axes, rotation, and shearing) were simulated and agreement of abnormality scores between the original and simulated CRs was investigated. Our study analyzed 169 patients (median age, 65 years; 91 men). The median difference of abnormality scores was 1–2% and ICC ranged from 0.83 to 0.90. The 95% LoA was approximately ± 30%. Test-retest variation was negatively associated with solid portion size (β, − 0.50; p = 0.008) and good nodule conspicuity (β, − 0.94; p

To determine the negative predictive value (NPV) of non-specific benign results from cone-beam CT (CBCT)-guided transthoracic core-needle biopsy (TTNB) and identify predicting factors for false-negative for malignancies. From January 2009–December 2011, 1,108 consecutive patients with 1,116 lung lesions underwent CBCT-guided TTNB using an 18-gauge coaxial cutting needle. Among them, 226 patients with 226 TTNBs, initially diagnosed as non-specific benign, were included in this study. The medical charts, radiological or pathological follow-ups were reviewed to classify false-negative and true-negative results and to identify which variables were associated with false-negatives. Of 226 lesions, 24 (10.6%) were finally confirmed as malignancies and 202 (89.4%) as benign, of which the NPV was 89.4% (202/226). Multivariate analysis revealed that part-solid nodule (PSN) (odds ratio (OR), 3.95; P = 0.022), a biopsy result of ‘granulomatous inflammation’ (OR, 0.04; P = 0.022), and exact location of needle tip within targets (OR, 0.37; P = 0.045) were significantly associated with false-negatives among initial non-specific benign biopsy results. The NPV of the non-specific benign biopsy was 89.4%. PSN was a significant positive indicator, but a biopsy result of ‘granulomatous inflammation’ and exact location of needle tip within targets were significant negative indicators for false-negatives. • The negative predictive value of the non-specific benign biopsy was 89.4%. • A part-solid nodule is a significant predictor for false-negative biopsy (OR = 3.95). • Pathological diagnosis of granulomatous inflammation is a robust indicator for ‘true-negatives’. • Identifying needle tip within target lesions is a significant predictor for ‘true-negatives’.

To investigate whether “follow-up and surgical resection after interval growth” can negatively influence recurrence or mortality in patients with persistent part-solid nodules (PSNs) with solid components ≤5 mm. One hundred twenty five surgically resected persistent PSNs with solid components ≤5 mm in 125 individuals were evaluated. Of the 125 PSNs, 54 and 71 PSNs were categorized into interval growth and immediate surgery groups, respectively. Cox regression analysis was performed to evaluate the association of “follow-up until interval growth” with recurrence and survival, adjusted for initial clinical and CT features. In the interval growth group, 30 patients showed increased nodule size, 10 increased solid parts, and the remaining 14 both patterns. Five patients showed clinical stage shifts (stage T1a, initially, to T1b after interval growth). Post-operative disease recurrence occurred in six individuals (interval growth group, n = 2/54; immediate surgery group, n = 4/71), and four individuals died (interval growth group, n = 1/54; immediate surgery group, n = 3/71). There were no significant differences between these two groups in terms of recurrence-free survival (p = 0.451) and overall survival (p = 0.185). “Follow-up and surgical resection after interval growth” did not negatively influence the prognosis of patients with persistent PSNs with solid components ≤5 mm. • Incidences of post-operative recurrence or death in patients with PSN are low. • “Follow-up until interval growth” of PSNs does not negatively influence disease recurrence. • There was no survival disadvantage related with “follow-up until interval growth” in PSNs.

Lung cancer is the leading cause of cancer-related mortality worldwide. National Lung Screening Trial (NLST) demonstrated reduced lung cancer mortality of 20% for whom underwent annual low-dose CT screening (1). For the screening-detected nodules, size measurement is mandatory as the nodule size is directly proportional to the lung cancer risk (2). For the size estimation, unidimensional or bi-dimensional diameter measurement have been the standard in practice.

Percutaneous transthoracic needle biopsy (PTNB) is one of the essential diagnostic procedures for pulmonary lesions. Its role is increasing in the era of CT screening for lung cancer and precision medicine. The Korean Society of Thoracic Radiology developed the first evidence-based clinical guideline for PTNB in Korea by adapting pre-existing guidelines. The guideline provides 39 recommendations for the following four main domains of 12 key questions: the indications for PTNB, pre-procedural evaluation, procedural technique of PTNB and its accuracy, and management of post-biopsy complications. We hope that these recommendations can improve the diagnostic accuracy and safety of PTNB in clinical practice and promote standardization of the procedure nationwide.

Purpose To compare the prognostic performances of clinical T categorization between the longest diameter and average diameter at CT in patients who underwent surgical resection of non-small cell lung cancers (NSCLCs). Materials and Methods This study retrospectively determined clinical T categories based on the longest diameter (clinical Tlongest) and average diameter (clinical Taverage) in 1153 patients, including 651 men (median age, 67 years; interquartile range [IQR], 60-72 years) and 502 women (median age, 63 years; IQR, 55-70 years) who underwent preoperative chest CT and subsequent resection of NSCLCs (clinical T1 to clinical T4; N0M0) between 2009 and 2015. Prognostic performances for disease-free survival (DFS) were compared between clinical Tlongest and clinical Taverage by using the Harrell concordance indexes and Student t test. The effect of the average diameter on clinical T category shifts (downstaging) was also investigated by using the McNemar-Bowker test. Results Concordance indexes did not significantly differ between clinical Tlongest (0.72; 95% confidence interval [CI]: 0.67, 0.76) and clinical Taverage (0.70; 95% CI: 0.64, 0.75) (P = .12 for the comparison). In the clinical T1 subgroup analysis, concordance indexes were 0.77 (95% CI: 0.71, 0.83) for clinical Tlongest and 0.75 (95% CI: 0.69, 0.81) for clinical Taverage (P = .17 for the comparison). Use of the clinical Taverage resulted in significant downstaging in all clinical T categories (P < .001). Conclusion The prognostic performance of clinical T categorization was not significantly different between the longest and average diameter measurements. Clinical T categorization based on the longest tumor diameter at CT was demonstrated to be sufficient for risk stratification of surgically treated non-small cell lung cancers. © RSNA, 2019 Online supplemental material is available for this article.

Background: Performance of deep-learning based automated detection (DLAD) algorithms in systematic screening for active pulmonary tuberculosis is unknown. We aimed to validate DLAD algorithm for detection of active pulmonary tuberculosis and any radiologicallyidentifiable relevant abnormality on chest radiographs (CRs) in this setting. Methods: We performed out-of-sample testing of a trained DLAD algorithm, using CRs from 19,686 asymptomatic individuals (male: 19,475, female: 211; mean ± standard deviation: 21.3 ± 1.9 years) as part of systematic screening for tuberculosis between January 2013 and July 2018. Area under the receiver operating characteristic curves (AUC) of DLAD for diagnosis of tuberculosis and any relevant abnormalities were measured. Accuracy measures including sensitivities, specificities, positive predictive values (PPVs), negative predictive values (NPVs) were calculated at predefined operating thresholds (high sensitivity threshold, 0·16; high specificity threshold, 0·46). Findings: All five CRs with active pulmonary tuberculosis were correctly classified as having abnormal findings by DLAD with specificities of 0·959 and 0·997, PPVs of 0·006 and 0·068, and NPVs of both 1·000 at high sensitivity and high specificity thresholds, respectively. With high specificity thresholds, DLAD showed comparable diagnostic measures for tuberculosis to the pooled radiologists (P values > 0·05). For the detection of any radiologically-identifiable relevant abnormality (n=28), DLAD showed AUC value of 0·967 (95% confidence interval, 0·938-0·996) with sensitivities of 0·821 and 0·679, specificities of 0·960 and 0·997, PPVs of 0·028 and 0·257, and NPVs of both 1·000 at high sensitivity and high specificity thresholds, respectively. Interpretation: In systematic screening for tuberculosis in a lowprevalence setting, DLAD algorithm demonstrated excellent diagnostic performance, comparable to the radiologists in the detection of active pulmonary tuberculosis. Funding Statement: This study was supported by the Seoul Research & Business Development Program (grant number: FI170002), and Lunit Inc. provided technical supports for this study. Declaration of Interests: There is a major research agreement between Seoul National University Hospital and Lunit Inc, in which roles of researchers and Lunit Inc. were described. However, the funder and Lunit Inc. did not have any role either in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication. Researchers (J.H.L., E.J.H., W.Y.L., S.L., J.R.A.) who controlled, manipulated and analyzed data, did not have any conflict of interest. Three authors (J.M.G., H.K., C.M.P.) received research grants from Lunit Inc. for outside of this study. Ethics Approval Statement: This retrospective study was approved by the institutional review board of The Armed Forces Medical Command of Korea, and the requirement for informed consent was waived.

Quantitative opacity mass and three-dimensional opacity volume on CT were quantifiable metrics affecting the visibility of COVID-19–related opacities on chest radiographs.

Objective To evaluate bronchovascular injuries as the causative occurrence for clinically significant hemoptysis after percutaneous transthoracic needle biopsy (PTNB). Materials and methods We included 111 consecutive patients who experienced hemoptysis after cone beam CT (CBCT)-guided PTNB from January 2014 through January 2017. Clinically significant hemoptysis was defined as hemoptysis causing hemodynamic instability or oxygen desaturation greater than 10% of baseline. The lesion characteristics were evaluated on CT images. The penetration of bronchovascular structures along the trajectory of the introducer needle and potential penetration at the firing of the biopsy gun were assessed on CBCT images. The cutting injury of bronchovascular structures was histopathologically assessed in biopsy specimens. The associated factors for clinically significant hemoptysis were assessed using logistic regression analyses. Results Seventeen patients (15.3%; 95%CI, 9.7%-23.2%) had clinically significant hemoptysis. On univariate analysis, the open bronchus sign (P = .004), nodule consistency (P = .012), potential penetration of a pulmonary vessel or bronchus 1 mm or larger at firing (P = .008 and P = .038, respectively), and a cutting injury of a pulmonary vessel 1 mm or larger (P = .007) or a bronchial structure (P = .041) were associated with clinically significant hemoptysis. Multivariate analysis found the following significant associated factors: potential penetration of a pulmonary vessel 1 mm or larger at firing (OR, 3.874; 95%CI, 1.072-13.997; P = .039) and cutting injury of a pulmonary vessel 1 mm or larger (OR, 6.920; 95%CI, 1.728-27.711; P = .006) or a bronchial structure (OR 4.604; 95%CI, 1.194-17.755; P = .027). Conclusion Potential penetration and cutting injury of bronchovascular structures 1mm or larger at firing were independently associated with clinically significant hemoptysis after PTNB.

The aim was to investigate the effect of clinico-radiologic variables, including total tumor (Ttotal) size and clinical T category, on the prognosis of patients with stage IA (T1N0M0) lung adenocarcinomas appearing as part-solid nodules (PSNs). This institutional review board-approved retrospective study included 506 patients (male:female = 200:306; median age, 62 years) with PSNs of the adenocarcinoma spectrum in clinical stage IA who underwent standard lobectomy at a single tertiary medical center. Prognostic stratification of the patients in terms of disease-free survival was analyzed with variables including age, sex, Ttotal size, solid portion size, clinical T category, and tumor location using univariate and subsequent multivariate Cox regression analysis. Subgroup analysis was performed to reveal the effect of the Ttotal size at each clinical T category. Multivariate Cox regression analysis demonstrated that Ttotal size*cT1b [interaction term; hazard ratio (HR) = 1.091; 95% confidence interval (CI): 1.015, 1.173; p = 0.019] and cT1c (HR = 68.436; 95% CI: 2.797, 1674.415; p = 0.010) were independent risk factors for the tumor recurrence. When patients with cT1b were dichotomized based on a Ttotal size cutoff of 3.0 cm, PSNs with Ttotal > 3.0 cm showed a significantly worse outcome (HR = 3.796; 95% CI: 1.006, 14.317; p = 0.049). No significant difference was observed in the probability of recurrence between cT1b with Ttotal > 3.0 cm and cT1c (p = 0.915). Ttotal size is a significant prognostic factor in adenocarcinoma patients in cT1b without lymph node or distant metastasis. PSNs in cT1b with Ttotal > 3.0 cm have a comparable risk of lung cancer recurrence to those in cT1c. • Current T descriptor was a powerful prognostic factor in stage IA adenocarcinomas appearing as part-solid nodules. • Total tumor size further stratified risk of recurrence of adenocarcinomas in cT1b. • Upstaging of tumors in cT1b with total tumor size > 3.0 cm may be more appropriate.

Highlights • A 2-month-old male infant presenting with only peripheral facial palsy was diagnosed with a primitive neuroectodermal tumor of the skull base. • Primitive neuroectodermal tumors located in the skull base are rarely detected in infants. • After subtotal resection of the tumor, asymmetrical facial expression showed mild improvement., Introduction Peripheral facial palsy is rarely caused by primary neoplasms, which are mostly constituted of tumors of the central nervous system, head and neck, and leukemia. Presentation of case A 2-month-old male infant presented with asymmetric facial expression for 3 weeks. Physical examination revealed suspicious findings of right peripheral facial palsy. Computed tomography of the temporal bone revealed a suspicious bone tumor centered in the right petrous bone involving surrounding bones with extension into the middle ear cavity and inner ear. Subtotal resection of the tumor was performed due to crucial structures adjacent the mass. Histopathology and immunohistochemistry of the resected tumor was consistent with primitive neuroectodermal tumor. Conclusion We report a rare case of a primitive neuroectodermal tumor located at the skull base presenting with only peripheral facial palsy.

We aimed to identify the impact of radiation dose and iterative reconstruction (IR) on measurement of pulmonary nodules by chest computed tomography (CT).CT scans were performed on a chest phantom containing various nodules (diameters of 3, 5, 8, 10, and 12 mm; +100, -630 and -800 HU for each diameter) at 80, 100, 120 kVp and 10, 20, 50, 100 mAs (a total of 12 radiation dose settings). Each CT was reconstructed using filtered back projection, iDose4, and iterative model reconstruction (IMR). Thereafter, two radiologists measured the diameter and attenuation of the nodules. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. Influence of radiation dose and reconstruction algorithm on measurement error and objective image quality metrics was analyzed using generalized estimating equations.The 80 kVp, 10 mAs CT scan was not feasible for the measurement of 3 mm sized simulated ground-glass nodule (GGN); otherwise, diameter measurement error was not significantly influenced by radiation dose (P0.05). IR did not have a significant impact on diameter measurement error for simulated solid nodules (P0.05). However, for simulated GGNs, IMR was associated with significantly decreased relative diameter measurement error (P0.001). Attenuation measurement error was not significantly influenced by either radiation dose or reconstruction algorithm (P0.05). Objective image quality was significantly better with IMR (P0.05).Nodule measurements were not affected by radiation dose except for 3 mm simulated GGN on 80 kVp, 10 mAs dose setting. However, for GGNs, IMR may help reduce diameter measurement error while improving image quality.

To investigate the natural course of persistent pulmonary subsolid nodules (SSNs) with solid portions ≤5 mm and the clinico-radiological features that influence interval growth over follow-ups. From 2005 to 2013, the natural courses of 213 persistent SSNs in 213 patients were evaluated. To identify significant predictors of interval growth, Kaplan-Meier analysis and Cox proportional hazard regression analysis were performed. Among the 213 nodules, 136 were pure ground-glass nodules (GGNs; growth, 18; stable, 118) and 77 were part-solid GGNs with solid portions ≤5 mm (growth, 24; stable, 53). For all SSNs, lung cancer history (p = 0.001), part-solid GGNs (p

To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model. This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity. Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively). Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations. • Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules. • Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules. • Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.

This study aimed to investigate the diagnostic advantage of nodule mass in differentiating invasive pulmonary adenocarcinomas (IPAs) among pure ground-glass nodules (pGGNs) over other volumetric measurements. Another aim of this study was to analyse the correlation between volumetric measurements on computed tomography (CT) scans and the pathological invasive component size. This Institutional Review Board-approved retrospective study included 117 patients (men:women = 53:64; mean age, 57.3 years) with 117 pGGNs. Semi-automatic segmentation was performed for all nodules, and volumetric measurements, such as nodule volume, attenuation, mass, two-dimensional (2D) average diameter and three-dimensional (3D) longest diameter, were obtained. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performances of the volumetric parameters in discriminating IPAs. Spearman correlation coefficients were calculated between the volumetric measurements and the invasive component size. Area under the ROC curve for mass was 0.792 (95% CI, 0.691-0.872) in non-enhanced CT and 0.730 (95% CI, 0.607-0.832) in contrast-enhanced CT. Nodule mass was not superior to 2D average diameter for the differentiation of IPAs in both non-enhanced (0.792 vs 0.780; p = 0.501) CT and contrast-enhanced CT scans (0.730 vs 0.700; p = 0.319). The correlation between the volumetric measurements (mass, 3D longest diameter and 2D average diameter) and the invasive component size was moderate (Spearman’s rho, 0.401-0.422) in non-enhanced CT and weak (Spearman’s rho, 0.276-0.310) in contrast-enhanced CT. Nodule mass measurement had no strength over other volumetric parameters for the prediction of pathological invasiveness in the diagnosis of pGGNs. • Mass is not superior to other volumetric measurements for the diagnosis of pure ground-glass nodules. • Mass and two-dimensional average diameter exhibited comparable performance for the discrimination of invasive adenocarcinomas among pure ground-glass nodules. • The diagnostic performance of volumetric measurements was lower on contrast-enhanced CT scans. • The correlation between the volumetric measurements and the invasive component size was moderate on non-enhanced CT scans and weak on contrast-enhanced CT scans.

Objective To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). Materials and Methods We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Results Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, ≤ 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Conclusion Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

To compare the pulmonary subsolid nodule (SSN) classification agreement and measurement variability between filtered back projection (FBP) and model-based iterative reconstruction (MBIR). Low-dose CTs were reconstructed using FBP and MBIR for 47 patients with 47 SSNs. Two readers independently classified SSNs into pure or part-solid ground-glass nodules, and measured the size of the whole nodule and solid portion twice on both reconstruction algorithms. Nodule classification agreement was analyzed using Cohen’s kappa and compared between reconstruction algorithms using McNemar’s test. Measurement variability was investigated using Bland–Altman analysis and compared with the paired t-test. Cohen’s kappa for inter-reader SSN classification agreement was 0.541–0.662 on FBP and 0.778–0.866 on MBIR. Between the two readers, nodule classification was consistent in 79.8 % (75/94) with FBP and 91.5 % (86/94) with MBIR (p = 0.027). Inter-reader measurement variability range was -5.0–2.1 mm on FBP and -3.3–1.8 mm on MBIR for whole nodule size, and was -6.5–0.9 mm on FBP and -5.5–1.5 mm on MBIR for solid portion size. Inter-reader measurement differences were significantly smaller on MBIR (p = 0.027, whole nodule; p = 0.011, solid portion). MBIR significantly improved SSN classification agreement and reduced measurement variability of both whole nodules and solid portions between readers. • Low-dose CT using MBIR algorithm improves reproducibility in the classification of SSNs. • MBIR would enable more confident clinical planning according to the SSN type. • Reduced measurement variability on MBIR allows earlier detection of potentially malignant nodules.

BACKGROUND AND PURPOSE: Antiplatelet resistance is known to be associated with symptomatic ischemic complication after endovascular coil embolization. The purpose of our study was to evaluate the relationship between antiplatelet resistance and clinically silent thromboembolic complications using DWI in patients who underwent coil embolization for unruptured intracranial aneurysm. MATERIALS AND METHODS: Between October 2011 and May 2013, 58 patients with 62 unruptured aneurysms who were measured for antiplatelet response using VerifyNow assay and underwent elective coil embolization for an unruptured aneurysm with posttreatment DWI were enrolled. Diffusion-positive lesions were classified into 3 groups according to the number of lesions (n = 0 [grade 0], n < 6 [grade I], and n ≥ 6 [grade II]). The relationship between antiplatelet resistance and diffusion-positive lesions was analyzed. RESULTS: Sixty-two endovascular coiling procedures were performed on 58 patients. Clopidogrel resistance was revealed in 23 patients (39.7%) and diffusion-positive lesions were demonstrated in 28 patients (48.3%); these consisted of 19 (32.8%) grade I and 9 (15.5%) grade II lesions. Clopidogrel resistance was not relevant to the development of any diffusion-positive lesion (grade I and II, P = .789) but was associated with the development of multiple diffusion-positive lesions (grade II, P = .002). In the logistic regression prediction model, clopidogrel resistance showed significant correlation with the development of grade II lesions (P = .001). CONCLUSIONS: Multiple diffusion-positive lesions (≥6 in number) occurred more frequently in patients with clopidogrel resistance after endovascular coiling for unruptured aneurysms.

Obstructive sleep apnea syndrome is a sleep-related breathing disorder that is caused by obstruction of the upper airway. In this research, we explore a computer-aided diagnosis system with portable ECG equipment and tri-accelerometer (x, y, and z-axes) that can automatically analyze biosignals and test for OSA. We developed an approach to record ECG signals and abdominal movements induced by breathing by affixing ECG-enabled electrodes onto a triaxial accelerometer. With the two signals simultaneously measured, the apnea data obtained would be more accurate, relative to cases where a single signal is measured. This would be helpful in diagnosing OSA.