Occupational and environmental risk factors for idiopathic pulmonary fibrosis: A case-control study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease of unknown etiology. The aim of this study was to evaluate the environmental and occupational risk factors of IPF. METHODS: This hospital-based, case-control study included 206 patients with IPF selected from the Seoul National University Bundang Hospital Interstitial Lung Disease registry and 167 controls without lung disease. Data on occupation, lifestyle, transportation, and types of environmental and occupational dust exposure were obtained using a questionnaire. IPF diagnosis was confirmed based on the recent guidelines, and the possibility of hypersensitivity pneumonitis was excluded. Multiple logistic regression was performed to determine the risk factors for IPF. RESULTS: After adjusting for age and sex, ever-smokers (odds ratio [OR], 2.35; 95 % confidence interval [CI]: 1.51-3.68) and individuals who smoked more than 30 pack-years (OR, 2.79; 95%CI: 1.70-4.68) showed an increased risk for IPF. Any occupational dust exposure (adjusted OR, 2.08; 95%CI: 1.19-3.72), especially exposure to chemicals (adjusted OR, 3.52; 99%CI: 1.56-9.05), was associated with IPF after adjusting for age, sex, and smoking. CONCLUSIONS: Smoking and occupational dust exposure are associated with an increased risk for IPF. Both factors have dose and duration-dependent relationships with the risk for IPF.

Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.

Background: Detecting new pulmonary metastases by comparing serial computed tomography (CT) scans is crucial, but a repetitive and time-consuming task that burdens the radiologists' workload. This study aimed to evaluate the usefulness of a nodule-matching algorithm with deep learning-based computer-aided detection (DL-CAD) in diagnosing new pulmonary metastases on cancer surveillance CT scans. Methods: Among patients who underwent pulmonary metastasectomy between 2014 and 2018, 65 new pulmonary metastases missed by interpreting radiologists on cancer surveillance CT (Time 2) were identified after a retrospective comparison with the previous CT (Time 1). First, DL-CAD detected nodules in Time 1 and Time 2 CT images. All nodules detected at Time 2 were initially considered metastasis candidates. Second, the nodule-matching algorithm was used to assess the correlation between the nodules from the two CT scans and to classify the nodules at Time 2 as "new" or "pre-existing". Pre-existing nodules were excluded from metastasis candidates. We evaluated the performance of DL-CAD with the nodule-matching algorithm, based on its sensitivity, false-metastasis candidates per scan, and positive predictive value (PPV). Results: A total of 475 lesions were detected by DL-CAD at Time 2. Following a radiologist review, the lesions were categorized as metastases (n=54), benign nodules (n=392), and non-nodules (n=29). Upon comparison of nodules at Time 1 and 2 using the nodule-matching algorithm, all metastases were classified as new nodules without any matching errors. Out of 421 benign lesions, 202 (48.0%) were identified as pre-existing and subsequently excluded from the pool of metastasis candidates through the nodule-matching algorithm. As a result, false-metastasis candidates per CT scan decreased by 47.9% (from 7.1 to 3.7, P<0.001) and the PPV increased from 11.4% to 19.8% (P<0.001), while maintaining sensitivity. Conclusions: The nodule-matching algorithm improves the diagnostic performance of DL-CAD for new pulmonary metastases, by lowering the number of false-metastasis candidates without compromising sensitivity.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. METHODS: To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. RESULTS: DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). CONCLUSIONS: A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS: • A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

Chest Radiography of Tuberculosis: Determination of Activity Using Deep Learning Algorithm.

BACKGROUND: Inactive or old, healed tuberculosis (TB) on chest radiograph (CR) is often found in high TB incidence countries, and to avoid unnecessary evaluation and medication, differentiation from active TB is important. This study develops a deep learning (DL) model to estimate activity in a single chest radiographic analysis. METHODS: A total of 3,824 active TB CRs from 511 individuals and 2,277 inactive TB CRs from 558 individuals were retrospectively collected. A pretrained convolutional neural network was fine-tuned to classify active and inactive TB. The model was pretrained with 8,964 pneumonia and 8,525 normal cases from the National Institute of Health (NIH) dataset. During the pretraining phase, the DL model learns the following tasks: pneumonia vs. normal, pneumonia vs. active TB, and active TB vs. normal. The performance of the DL model was validated using three external datasets. Receiver operating characteristic analyses were performed to evaluate the diagnostic performance to determine active TB by DL model and radiologists. Sensitivities and specificities for determining active TB were evaluated for both the DL model and radiologists. RESULTS: The performance of the DL model showed area under the curve (AUC) values of 0.980 in internal validation, and 0.815 and 0.887 in external validation. The AUC values for the DL model, thoracic radiologist, and general radiologist, evaluated using one of the external validation datasets, were 0.815, 0.871, and 0.811, respectively. CONCLUSION: This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.

Volumetric analysis of pulmonary nodules: reducing the discrepancy between the diameter-based volume calculation and voxel-counting method.

Background: When assessing the volume of pulmonary nodules on computed tomography (CT) images, there is an inevitable discrepancy between values based on the diameter-based volume calculation and the voxel-counting method, which is derived from the Euclidean distance measurement method on pixel/voxel-based digital image. We aimed to evaluate the ability of a modified diameter measurement method to reduce the discrepancy, and we determined a conversion equation to equate volumes derived from different methods. Methods: Two different anthropomorphic phantoms with subsolid and solid nodules were repeatedly scanned under various settings. Nodules in CT images were detected and segmented using a fully automated algorithm and the volume was calculated using three methods: the voxel-counting method (Vvc ), diameter-based volume calculation (Vd ), and a modified diameter-based volume calculation (Vd+ 1), in which one pixel spacing was added to the diameters in the three axes (x-, y-, and z-axis). For each nodule, Vd and Vd +1 were compared to Vvc by computing the absolute percentage error (APE) as follows: APE =100 × (V - Vvc )/Vvc . Comparisons between APEd and APEd+1 according to CT parameter setting were performed using the Wilcoxon signed-rank test. The Jonckheere-Terpstra test was used to evaluate trends across the four different nodule sizes. Results: The deep learning-based computer-aided diagnosis (DL-CAD) successfully detected and segmented all nodules in a fully automatic manner. The APE was significantly less with Vd+1 than with Vd (Wilcoxon signed-rank test, P<0.05) regardless of CT parameters and nodule size. The APE median increased as the size of the nodule decreased. This trend was statistically significant (Jonckheere-Terpstra test, P<0.001) regardless of volume measurement method (diameter-based and modified diameter-based volume calculations). Conclusions: Our modified diameter-based volume calculation significantly reduces the discrepancy between the diameter-based volume calculation and voxel-counting method.

Application of N Descriptors Proposed by the International Association for the Study of Lung Cancer in Clinical Staging.

Background Patients with N1 or N2 non-small cell lung cancer exhibit prognostic heterogeneity. To refine the current N staging system, new N stages were proposed by the International Association for the Study of Lung Cancer. However, those proposed new N stages have not been validated. Purpose To evaluate the prognostic performance of the proposed N descriptors for clinical staging. Materials and Methods Participants with non-small cell lung cancer without distant metastasis from January 2010 to December 2014 were retrospectively included. Each patient's clinical N (cN) stage was assigned to one of seven categories (cN0, cN1a, cN1b, cN2a1, cN2a2, cN2b, cN3). The 5-year overall survival rates were estimated with the Kaplan-Meier method. The adjusted hazard ratios (HRs) and their 95% CIs were estimated by using a multivariable Cox proportional hazard model. Ad hoc analyses according to lymph node (LN) size were performed. Results A total of 1271 patients (median age, 66 years; interquartile range, 59-73 years; 812 men) were included. The 5-year overall survival rates were 77.3%, 53.7%, 36.0%, 29.2%, 34.4%, 18.0%, and 12.4% for stages cN0, cN1a, cN1b, cN2a1, cN2a2, cN2b, and cN3, respectively. Patients with cN2b disease had a worse prognosis than patients with cN2a disease (HR, 1.53; 95% CI: 1.06, 2.22; P = .02). There was no prognostic difference between cN1b and cN1a (HR, 1.13; 95% CI: 0.61, 2.09; P = .71); however, there was a difference between cN1 subgroups when stratified by LN size (≥2 cm; HR, 2.26; 95% CI: 1.16, 4.44; P = .02). Within cN2a disease, there were no differences between cN2a1 and cN2a2 (HR, 0.98; 95% CI: 0.61, 1.56; P = .93) or between subgroups according to LN size (HR, 0.74; 95% CI: 0.40, 1.37; P = .34). Conclusion A survival difference was observed between single- and multistation involvement among cN2 disease. The number of involved lymph node stations in patients with cN1 disease and the presence of skip metastasis in patients with cN2 disease were not associated with survival differences. © RSNA, 2021 Online supplemental material is available for this article.

Accuracy and complications of percutaneous transthoracic needle lung biopsy for the diagnosis of malignancy in patients with idiopathic pulmonary fibrosis.

OBJECTIVES: To determine the accuracy of CT-guided percutaneous transthoracic needle lung biopsy (PTNB) for the diagnosis of malignancy and the associated complication rates in patients with idiopathic pulmonary fibrosis (IPF). METHODS: This retrospective study included 91 CT-guided PTNBs performed in 80 patients with IPF from April 2003 through December 2016. Data regarding patients, target lesions, procedures, complications, and pathological reports were collected, and the final diagnosis was made. The diagnostic accuracy, sensitivity, specificity, percentage of nondiagnostic results, and complication rates were determined. Multivariable logistic regression analyses were performed to identify risk factors for nondiagnostic results and major complications. RESULTS: Three biopsies (technical failure [n = 2] and undetermined final diagnosis [n = 1]) were excluded from the diagnostic accuracy calculation. The diagnostic accuracy, sensitivity, and specificity were 89% (78/88), 90% (62/69), and 84% (16/19), respectively. The percentage of nondiagnostic results was 34% (30/88). Lesion size ≤ 3 cm (odds ratio [OR], 8.8; 95% confidence interval [CI], 2.5-31.2; p = 0.001) and needle tip placement outside the target lesion (OR, 13.7; 95% CI, 1.4-132.2; p = 0.02) were risk factors for nondiagnostic results. The overall and major complication rates were 51% (46/91) and 12% (11/91), respectively. The presence of honeycombing along the path of the needle (OR, 11.2; 95% CI, 1.4-89.1; p = 0.02) was an independent risk factor for major complications. CONCLUSIONS: CT-guided PTNB shows a relatively reasonable accuracy in diagnosing malignancy in patients with IPF. The complication rate may be high, especially when the needle passes through honeycomb lesions. KEY POINTS: • In patients with idiopathic pulmonary fibrosis (IPF), CT-guided percutaneous transthoracic needle lung biopsy (PTNB) showed a relatively reasonable accuracy for the diagnosis of malignancy. • Target lesion size ≤ 3 cm and biopsy needle tip placement outside the target lesion were risk factors for nondiagnostic results of CT-guided PTNB. • The complication rate may be high, especially in cases where the biopsy needle passes through honeycomb lesions.

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

OBJECTIVES: To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs. METHODS: 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. RESULTS: 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. CONCLUSION: 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. KEY POINTS: • 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.

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Pulmonary emphysema is a cause of chronic obstructive pulmonary disease. Emphysema can be accurately diagnosed via CT. The severity of emphysema can be assessed using visual interpretation or quantitative analysis. Various studies on emphysema using deep learning have also been conducted. Although the classification of emphysema has proven clinically useful, there is a need to improve the reliability of the measurement.

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers.

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.

Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.

Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer performance for the detection of lung cancers on chest radiographs. Materials and Methods Among patients diagnosed with lung cancers between January 2010 and December 2014, 117 patients (median age, 69 years; interquartile range [IQR], 64-74 years; 57 women) were retrospectively identified in whom lung cancers were visible on previous chest radiographs. For the healthy control group, 234 patients (median age, 58 years; IQR, 48-68 years; 123 women) with normal chest radiographs were randomly selected. Nine observers reviewed each chest radiograph, with and without a DLAD. They detected potential lung cancers and determined whether they would recommend chest CT for follow-up. Observer performance was compared with use of the area under the alternative free-response receiver operating characteristic curve (AUC), sensitivity, and rates of chest CT recommendation. Results In total, 105 of the 117 patients had lung cancers that were overlooked on their original radiographs. The average AUC for all observers significantly rose from 0.67 (95% confidence interval [CI]: 0.62, 0.72) without a DLAD to 0.76 (95% CI: 0.71, 0.81) with a DLAD (P < .001). With a DLAD, observers detected more overlooked lung cancers (average sensitivity, 53% [56 of 105 patients] with a DLAD vs 40% [42 of 105 patients] without a DLAD) (P < .001) and recommended chest CT for more patients (62% [66 of 105 patients] with a DLAD vs 47% [49 of 105 patients] without a DLAD) (P < .001). In the healthy control group, no difference existed in the rate of chest CT recommendation (10% [23 of 234 patients] without a DLAD and 8% [20 of 234 patients] with a DLAD) (P = .13). Conclusion Using a deep learning-based automatic detection algorithm may help observers reduce the number of overlooked lung cancers on chest radiographs, without a proportional increase in the number of follow-up chest CT examinations. © RSNA, 2020 Online supplemental material is available for this article.

Usefulness of CT-Guided Percutaneous Transthoracic Needle Lung Biopsies in Patients with Suspected Pulmonary Infection.

OBJECTIVE: This study aimed to evaluate the clinical benefits and risks of CT-guided percutaneous transthoracic needle lung biopsies (PTNBs) in patients with a suspected pulmonary infection. MATERIALS AND METHODS: This study included 351 CT-guided PTNBs performed in 342 patients (mean age, 58.9 years [range, 17-91 years]) with suspected pulmonary infection from January 2010 to December 2016. The proportion of biopsies that revealed the causative organism for pulmonary infection and that influenced patient's treatment were measured. Multivariate analyses were performed to identify factors associated with PTNB that revealed the causative organism or affected the treatment. Finally, the complication rate was measured. RESULTS: CT-guided PTNB revealed the causative organism in 32.5% of biopsies (114/351). The presence of necrotic components in the lesion (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.1-2.7; p = 0.028), suspected pulmonary tuberculosis (OR, 2.0; 95% CI, 1.2-3.5; p = 0.010), and fine needle aspiration (OR, 2.5; 95% CI, 1.1-5.8; p = 0.037) were factors associated with biopsies that revealed the causative organism. PTNB influenced patient's treatment in 40.7% (143/351) of biopsies. The absence of leukocytosis (OR, 1.9; 95% CI, 1.0-3.7; p = 0.049), presence of a necrotic component in the lesion (OR, 2.4; 95% CI, 1.5-3.8; p < 0.001), and suspected tuberculosis (OR, 1.7; 95% CI, 1.0-2.8; p = 0.040) were factors associated with biopsies that influenced the treatment. The overall complication rate of PTNB was 19% (65/351). CONCLUSION: In patients with suspected pulmonary infection, approximately 30-40% of CT-guided PTNBs revealed the causative organism or affected the treatment. The complication rate of PTNB for suspected pulmonary infection was relatively low.

Improved repeatability of subsolid nodule measurement in low-dose lung screening with monoenergetic images: a phantom study.

BACKGROUND: To investigate whether monoenergetic images captured with dual-layer spectral computed tomography (CT) can improve the repeatability of subsolid nodule measurement, and whether this approach can further reduce the radiation dose of CT while maintaining its measurement repeatability. METHODS: An anthropomorphic phantom with simulated subsolid nodules at three different levels was repeatedly scanned with both conventional single-energy CT and dual-layer spectral CT. A proxy for the measurement repeatability in the National Lung Screening Trial (proxy for NLST) was calculated with the typical CT protocol used in NLST. Using the dual-layer spectral CT, monoenergetic images of 40 to 110 keV, with an interval of 10 keV, were generated. The average diameter and volume of a total of 15,120 nodules in 840 CT images were measured by using a commercially-available computer-aided detection (CAD) system. The repeatability coefficient (RC), %RC, and 95% confidence intervals (CIs) of each image set were calculated and compared. RESULTS: At the same tube voltage and tube current-time product, monoenergetic images resulted in significantly lower RC than the proxy for NLST, indicating that measurement repeatability was enhanced. When the radiation dose was lowered by 30% or 55%, monoenergetic images showed significantly lower RC at high-energy keV than the proxy for NLST. The estimated measurement repeatability from monoenergetic images with 30% or 55% lower radiation dose was comparable to the repeatability from conventional single-energy CT images with standard radiation dose and iterative reconstruction. CONCLUSIONS: Monoenergetic images captured by using dual-layer spectral CT can improve the repeatability of subsolid nodule measurement. The use of monoenergetic images would allow lung cancer screening with a lower radiation dose, while maintaining comparable measurement repeatability.

Quantitative Thoracic Magnetic Resonance Criteria for the Differentiation of Cysts from Solid Masses in the Anterior Mediastinum.

OBJECTIVE: To evaluate quantitative magnetic resonance imaging (MRI) parameters for differentiation of cysts from and solid masses in the anterior mediastinum. MATERIALS AND METHODS: The development dataset included 18 patients from two institutions with pathologically-proven cysts (n = 6) and solid masses (n = 12) in the anterior mediastinum. We measured the maximum diameter, normalized T1 and T2 signal intensity (nT1 and nT2), normalized apparent diffusion coefficient (nADC), and relative enhancement ratio (RER) of each lesion. RERs were obtained by non-rigid registration and subtraction of precontrast and postcontrast T1-weighted images. Differentiation criteria between cysts and solid masses were identified based on receiver operating characteristics analysis. For validation, two separate datasets were utilized: 15 patients with 8 cysts and 7 solid masses from another institution (validation dataset 1); and 11 patients with clinically diagnosed cysts stable for more than two years (validation dataset 2). Sensitivity and specificity were calculated from the validation datasets. RESULTS: nT2, nADC, and RER significantly differed between cysts and solid masses (p = 0.032, 0.013, and < 0.001, respectively). The following criteria differentiated cysts from solid masses: RER < 26.1%; nADC > 0.63; nT2 > 0.39. In validation dataset 1, the sensitivity of the RER, nADC, and nT2 criteria was 87.5%, 100%, and 75.0%, and the specificity was 100%, 40.0%, and 57.4%, respectively. In validation dataset 2, the sensitivity of the RER, nADC, and nT2 criteria was 90.9%, 90.9%, and 72.7%, respectively. CONCLUSION: Quantitative MRI criteria using nT2, nADC, and particularly RER can assist differentiation of cysts from solid masses in the anterior mediastinum.

Growth of thymic epithelial tumors and thymic cysts: Differential radiological points.

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.

Diagnostic Yield and False-Referral Rate of Staging Chest CT in Patients with Colon Cancer.

Purpose To measure the diagnostic yield and false-referral rate (FRR) of staging contrast material-enhanced chest CT based on the clinical stage from contrast-enhanced abdominal CT in patients with colon cancer. Materials and Methods This retrospective study included 1743 patients (mean age, 63.4 years; range, 18-96 years) with a diagnosis of colon cancer. The primary outcomes were diagnostic yield and FRR of contrast-enhanced chest CT in the detection of thoracic metastasis. The proportions of patients with occult thoracic metastasis and those undergoing pulmonary metastasectomy for true-positive metastases were key secondary outcomes. The outcomes were stratified according to clinical stage at contrast-enhanced abdominal CT. Results The diagnostic yields in clinical stage 0/I, cII, cIII, and cIV were 0% (95% confidence interval [CI]: 0%, 0.8%), 1.3% (95% CI: 0.4%, 3.3%), 4.4% (95% CI: 3.0%, 6.1%), and 43.3% (95% CI: 36.8%, 49.9%), respectively. The corresponding FRRs were 5.7% (95% CI: 3.8%, 8.2%), 2.9% (95% CI: 1.3%, 5.5%), 6.7% (95% CI: 5.0%, 8.8%), and 6.1% (95% CI: 3.4%, 10.0%), respectively. The proportions of patients with occult metastasis were 0% (95% CI: 0%, 0.8%), 3.3% (95% CI: 1.6%, 5.9%), 1.5% (95% CI: 0.8%, 2.7%), and 6.1% (95% CI: 3.4%, 10.0%), respectively. The proportion of patients who underwent pulmonary metastasectomy was 0% (none of 474; 95% CI: 0%, 0.8%) for clinical stage 0/I tumors. Conclusion In clinical stages 0 and I, the diagnostic yield of staging contrast-enhanced chest CT in detecting thoracic metastasis was zero. For clinical stages II, III, and IV, contrast-enhanced chest CT as a baseline examination was helpful for the detection of thoracic metastasis and allowed for the possibility of a curative metastasectomy. There was no significant association between clinical stage and false-referral rate. © RSNA, 2018 Online supplemental material is available for this article.

Nodule Classification on Low-Dose Unenhanced CT and Standard-Dose Enhanced CT: Inter-Protocol Agreement and Analysis of Interchangeability.

Objective: To measure inter-protocol agreement and analyze interchangeability on nodule classification between low-dose unenhanced CT and standard-dose enhanced CT. Materials and Methods: From nodule libraries containing both low-dose unenhanced and standard-dose enhanced CT, 80 solid and 80 subsolid (40 part-solid, 40 non-solid) nodules of 135 patients were selected. Five thoracic radiologists categorized each nodule into solid, part-solid or non-solid. Inter-protocol agreement between low-dose unenhanced and standard-dose enhanced images was measured by pooling κ values for classification into two (solid, subsolid) and three (solid, part-solid, non-solid) categories. Interchangeability between low-dose unenhanced and standard-dose enhanced CT for the classification into two categories was assessed using a pre-defined equivalence limit of 8 percent. Results: Inter-protocol agreement for the classification into two categories {κ, 0.96 (95% confidence interval [CI], 0.94-0.98)} and that into three categories (κ, 0.88 [95% CI, 0.85-0.92]) was considerably high. The probability of agreement between readers with standard-dose enhanced CT was 95.6% (95% CI, 94.5-96.6%), and that between low-dose unenhanced and standard-dose enhanced CT was 95.4% (95% CI, 94.7-96.0%). The difference between the two proportions was 0.25% (95% CI, -0.85-1.5%), wherein the upper bound CI was markedly below 8 percent. Conclusion: Inter-protocol agreement for nodule classification was considerably high. Low-dose unenhanced CT can be used interchangeably with standard-dose enhanced CT for nodule classification.

Diameter of the Solid Component in Subsolid Nodules on Low-Dose Unenhanced Chest Computed Tomography: Measurement Accuracy for the Prediction of Invasive Component in Lung Adenocarcinoma.

Objective: To determine if measurement of the diameter of the solid component in subsolid nodules (SSNs) on low-dose unenhanced chest computed tomography (CT) is as accurate as on standard-dose enhanced CT in prediction of pathological size of invasive component of lung adenocarcinoma. Materials and Methods: From February 2012 to October 2015, 114 SSNs were identified in 105 patients that underwent low-dose unenhanced and standard-dose enhanced CT pre-operatively. Three radiologists independently measured the largest diameter of the solid component. Intraclass correlation coefficients (ICCs) were used to assess inter-reader agreement. We estimated measurement differences between the size of solid component and that of invasive component. We measured diagnostic accuracy of the prediction of invasive adenocarcinoma using a size criterion of a solid component ≥ 6 mm, and compared them using a generalized linear mixed model. Results: Inter-reader agreement was excellent (ICC, 0.84.0.89). The mean ± standard deviation of absolute measurement differences between the solid component and invasive component was 4 ± 4 mm in low-dose unenhanced CT and 5 ± 4 mm in standard-dose enhanced CT. Diagnostic accuracy was 81.3% (95% confidence interval, 76.7.85.3%) in low-dose unenhanced CT and 76.6% (71.8.81.0%) in standard-dose enhanced CT, with no statistically significant difference (p = 0.130). Conclusion: Measurement of the diameter of the solid component of SSNs on low-dose unenhanced chest CT was as accurate as on standard-dose enhanced CT for predicting the invasive component. Thus, low-dose unenhanced CT may be used safely in the evaluation of patients with SSNs.

Effect of computed tomography window settings and reconstruction plane on 8th edition T-stage classification in patients with lung adenocarcinoma manifesting as a subsolid nodule.

PURPOSE: To assess the effect of window settings and reconstruction plane on clinical T-stage determined by solid portion size within subsolid nodules (SSNs), based on 8th-edition TNM standards. MATERIALS AND METHODS: This retrospective study included 247 SSNs from 221 patients who underwent surgery for lung adenocarcinomas between Feb 2012 and Oct 2015. Two radiologists independently measured the diameter of the solid portion on axial, coronal, and sagittal planes using lung- and mediastinal-window. The largest diameter among the measurements on the three planes was referred to as multiplanar measurement. Inter-reader agreement as well as the correlation between the CT and pathologic measurements were calculated using intra-class correlation coefficients (ICCs). The proportions of disagreement in clinical T-stage on different measurement methods were measured. The κ values for agreement between clinical- and pathological T-stage were measured. RESULTS: Inter-reader agreement was moderate-to-excellent (ICC confidence interval [CI] range, 0.51-0.92) in lung-window, while it was good-to-excellent (0.77-0.95) in mediastinal-window. The correlation between the CT and pathologic measurements was good-to-excellent (ICC CI range, 0.63-0.82) in lung-window and fair-to-good (0.25-0.78) in mediastinal-window. The proportions of disagreement between clinical T-stages using mediastinal- and lung-window were 32.0%-41.7% and 33.6%-49.0% with axial and multiplanar measurement, respectively. Multiplanar measurement resulted in upstaging in 12.6%-15.8% and 19.0%-24.3% of cases with mediastinal- and lung-window, respectively, when compared with axial measurement alone. The κ values for agreement between clinical T-stage and pathological T-stage ranged from 0.53 to 0.69. CONCLUSIONS: Mediastinal-window was a more stable method in the aspect of the inter-reader agreement, but the correlation between the CT and pathologic measurement was better in lung-window. The clinical T-stage varied in up to one-half of the cases according to the window setting, and multiplanar measurement resulted in upstaging in up to one-fourth of the cases.