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Objectives: To describe our initial experience with percutaneous transthoracic needle biopsy (PCNB) of small (≤1 cm) lung nodules using a cone-beam computed tomography (CBCT) virtual navigation guidance system in 105 consecutive patients.Methods: One hundred and five consecutive patients (55 male, 50 female; mean age, 62 years) with 107 small (≤1 cm) lung nodules (mean size, 0.85 cm ± 0.14) underwent PCNBs under CBCT virtual-navigation guidance system and constituted our study population. Procedural details-including radiation dose, sensitivity, specificity, diagnostic accuracy and complication rates of CBCT virtual navigation guided PCNBs-were described.Results: The mean number of pleural passages with the coaxial needle, biopsies, CT acquisitions, total procedure time, coaxial introducer dwelling time, and estimated radiation exposure during PCNBs were 1.03 ± 0.21, 3.1 ± 0.7, 3.4 ± 1.3, 10.5 min ± 3.2 and 7.2 min ± 2.5, and 5.72 mSv ± 4.19, respectively. Sixty nodules (56.1 %) were diagnosed as malignant, 38 (35.5 %) as benign and nine (8.4 %) as indeterminate. The sensitivity, specificity, and diagnostic accuracy of CBCT virtual-navigation-guided PCNB for small (≤1 cm) nodules were 96.7 % (58/60), 100 % (38/38) and 98.0 % (96/98), respectively. Complications occurred in 13 (12.1 %) cases; pneumothorax in seven (6.5 %) and haemoptysis in six (5.6 %).Conclusion: CBCT virtual-navigation-guided PCNB is a highly accurate and safe diagnostic method for small (≤1 cm) nodules. [ABSTRACT FROM AUTHOR]

Background: Pulmonary sclerosing hemangioma (PSH) has been reported to show increased FDG uptake and be potential false-positives on 18F-FDG PET/CT examination. However, it is still unclear whether the previously-reported high FDG uptake is a universal characteristic of PSH, and furthermore, there have been no investigations on what kind of radiologic or histologic features may have been related with its FDG uptake values. Purpose: To investigate the 18F-FDG PET/CT features of pulmonary sclerosing hemangiomas (PSHs), and to evaluate the relating factors with their FDG uptake values. Material and Methods: We identified 10 PSHs in eight patients who had a pathologic diagnosis and available antecedent 18F-FDG PET/CT images. 18F-FDG PET/CT images were investigated both qualitatively and quantitatively, along with their histopathologic features. Correlation between 18F-FDG PET features and radiologic as well as histopathologic features were also evaluated. Results: Mean diameter of the 10 PSHs in our study was 16.9 mm ± 6.26 (range 5-25 mm). Four tumors showed intense uptake, and four tumors showed moderate uptake on 18F-FDG PET/CT scans. In the remaining two tumors, there were no significant FDG uptakes. The SUVmax of tumors ranged from 0.60-4.7 (median 2.30; 2.51 ± 1.42), and was significantly correlated with the tumor size (r = 0.754, P = 0.012) and three out of four tumors ≥2 cm (75%) showed intense FDG uptake and their SUVmax values were greater than 2.5. Immunohistochemical results for GLUT-1, GLUT-4, and Ki-67 and other pathologic features were not correlated with the tumors' FDG uptake. Conclusion: The majority of PSHs show increased FDG uptakes, and their SUVmax values are significantly correlated with their tumor size. PSH ≥2 cm can frequently be falsely interpreted as malignancy in FDG-PET/CT. Further studies with large study population are warranted to confirm our observations. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the dynamic changes in airways in response to methacholine and salbutamol inhalation and to correlate the xenon ventilation index on xenon-enhanced chest CTs in asthmatics.Methods: Thirty-one non-smokers (6 normal, 25 asthmatics) underwent xenon-enhanced chest CT and pulmonary function tests. Images were obtained at three stages (basal state, after methacholine inhalation and after salbutamol inhalation), and the total xenon ventilation index (TXVI) as well as airway values were measured and calculated. The repeated measures ANOVA and Spearman's correlation coefficient were used for statistical analysis.Results: TXVI in the normal group did not significantly change (P > 0.05) with methacholine and salbutamol. For asthmatics, however, the TXVI significantly decreased after methacholine inhalation and increased after salbutamol inhalation (P < 0.05). Of the airway parameters, the airway inner area (IA) significantly increased after salbutamol inhalation in all airways (P < 0.01) in asthmatics. Airway IA, wall thickness and wall area percentage did not significantly decrease after methacholine inhalation (P > 0.05). IA of the large airways was well correlated with basal TXVI, FEV(1) and FVC (P < 0.05).Conclusions: Airway IA is the most reliable parameter for reflecting the dynamic changes after methacholine and salbutamol inhalation, and correlates well with TXVI in asthmatics on xenon-enhanced CT.Key Points: • In asthmatics, xenon ventilation decreases after methacholine and increases after salbutamol inhalation. • Inner airway area (IA) correlates well with xenon ventilation. • IA is the most reliable parameter reflecting airway changes in drug responses. [ABSTRACT FROM AUTHOR]

Background: Pulmonary aspergillosis in immunocompetent patients has been described as a saprophytic infection with pre-existing lung lesions showing an air-meniscus sign on chest radiograph or CT scans. There have been rare articles dealing with pulmonary aspergillosis in immunocompetent patients without pre-existing lung lesions. Purpose: To evaluate the CT findings of pulmonary aspergillosis in immunocompetent patients without air-meniscus and underlying lung disease and to correlate the CT findings and pathologic features of pulmonary aspergillosis in these patients. Material and Methods: A total of seven surgically proven pulmonary aspergillosis found in immunocompetent patients without an air-meniscus and underlying lung disease (M:F = 1:6; mean age 63.4 years) were included. On CT, the lesion shape, margin, type, location, diameter, presence of satellite nodules, presence of CT halo sign or hypodense sign, and interval growth were evaluated. Histopathologic features of each lesion were classified as one of the following; primary aspergilloma, chronic necrotizing pulmonary aspergillosis, or invasive pulmonary aspergillosis. Correlation between CT findings and pathological features was performed. Results: All lesions presented as a nodule or mass unable to differentiate from malignancy. Most lesions had well-defined margins (n = 4), appeared as solid lesions (n = 7), and were located in the upper lobe (n = 5). Mean diameter of lesions was 2.3 cm. Satellite nodules (n = 2), CT halo sign (n = 1), and hypodense sign (n = 4) were found. Only one lesion increased in size during follow-up. Lesions were pathologically classified as primary aspergilloma (n = 3) and chronic necrotizing pulmonary aspergillosis (n = 4). The hypodense sign on CT was pathologically proved as dense fungal hyphae filled in bronchus and CT halo sign as parenchymal hemorrhage. Conclusion: Pulmonary aspergillosis predominantly presented as a nodule or mass mimicking malignancy in the upper lobes on CT scan in elderly without underlying lung disease and immunosuppressive conditions except for age, and was histopathologically revealed to be either primary aspergilloma or chronic necrotizing pulmonary aspergillosis. [ABSTRACT FROM AUTHOR]

Background: Since the first outbreak caused by the pandemic H1N1 2009 influenza in Mexico, the virus has spread widely across the world with meaningful morbidity and mortality. However, there are few data on the comparative investigations to assess the clinical and radiological features between the H1N1 patient and non-H1N1 patients. Purpose: To assess the clinical and radiological features of patients infected by the pandemic H1N1 2009 flu virus at their initial presentation and to compare them with contemporaneous non-H1N1 patients with acute febrile respiratory illness. Material and Methods: This retrospective study was approved by the ethics committee of the Armed Forces Medical Command, South Korea. From August to September 2009, 337 consecutive patients presented with an acute febrile respiratory illness in a tertiary military hospital. Reverse-transcriptase polymerase-chain-reaction tests were performed in 62 of these patients under the impression of H1N1 infection. Clinical and radiological features at their initial presentation were described for the H1N1 group (n = 35) and non-H1N1 group (n = 27) and compared between the two groups. Results: Increased C-reactive protein level (97%) without leukocytosis (9%) or increased erythrocyte sedimentation rate (0%) was common in the H1N1 group at their initial presentation. On chest radiographs, 12 of 35 (34%) H1N1 patients had abnormal findings; nodules in 10 patients (83%) and consolidations in two (17%). Of the 28 H1N1 patients who underwent thin-section CT 16 patients (57%) showed abnormal findings; ground-glass opacities (GGOs) in 15 (94%), and nodules in 13 (81%). However, there were no significant differences between the H1N1 group and non-H1N1 group in terms of symptoms, laboratory results, or radiological findings (P > 0.05). Conclusion: Patients with H1N1 infection show consistent clinical and radiological features at their initial presentation, however, clinical and radiological features of the H1N1 group are not significantly different from those of the non-H1N1 group. [ABSTRACT FROM AUTHOR]

Objective: To describe our initial experience with percutaneous transthoracic biopsy (PCNB) of lung nodules using C-arm cone-beam CT (CBCT).Materials: Seventy-one consecutive patients with lung nodules of 30 mm or smaller underwent CBCT-guided PCNB using a coaxial cutting needle. We evaluated the procedure time, coaxial introducer dwell time, the numbers of pleural passages, coaxial introducer repositionings and CT acquisitions, as well as the technical success rate and radiation doses. Diagnostic accuracy, sensitivity, specificity and incidence of complications were also evaluated.Results: PCNB was performed for 71 nodules: 63 solid, 6 part-solid and 2 ground-glass nodules. The procedure time, coaxial introducer dwell time, numbers of pleural passages, coaxial introducer repositionings and CT acquisitions were 17.9+/-5.9 min, 8.7+/-3.8 min, 1.1+/-0.4, 0.2+/-0.5 and 2.9+/-0.7, respectively. The technical success rate was 100% and the radiation dose was 272+/-116 mGy. Thirty-six nodules (50.7%) were diagnosed as malignant, 25 (35.2%) as benign and 10 (14.1%) as indeterminate. Diagnostic accuracy, sensitivity, specificity and incidence of complications were 98.4%, 97%, 100% and 38%, respectively. Complications included pneumothorax in 18 patients (25.4%), haemoptysis in 10 (14.1%) and chest pain in one (1.4%).Conclusion: Under CBCT guidance, PCNB of lung nodules can be performed accurately, providing both real-time fluoroscopic guidance and CT imaging capabilities. [ABSTRACT FROM AUTHOR]

Background: Although pulmonary alveolar proteinosis (PAP) and exogenous lipoid pneumonia (ELP) require different treatment strategies, both manifest as a crazy-paving pattern on CT and often have similar clinical manifestations and radiologic features. Purpose: To investigate the clinical features and high-resolution computed tomography (HRCT) findings of PAP and ELP showing the crazy-paving pattern. Material and Methods: The clinical features and HRCT findings of eight patients with pathologically proven PAP and six patients with pathologically proven ELP showing the crazy-paving pattern were retrospectively evaluated. Two radiologists analyzed the HRCT findings of PAP and ELP in consensus in terms of the presence, severity, and extent of illdefined centrilobular nodules, consolidations, ground-glass opacities (GGOs), reticulations, and the crazy-paving pattern. Results: With respect to the clinical features of these two diseases, all patients with ELP were retrospectively found to have a history of oil ingestion. In terms of the HRCT findings, ill-defined centrilobular nodules were seen in five of six patients (83%) with ELP, whereas they were not present in any patient with PAP (P=0.003). Consolidation was also more frequently present in patients with ELP (83%) than in those with PAP (11%), which was statistically different (P=0.0265). In terms of the severity and extent, the crazy-paving pattern and reticulations on HRCT were significantly more extensive and severe in patients with PAP than in those with ELP. Conclusion: PAP and ELP with the crazy-paving pattern have several distinctive characteristics with respect to their HRCT findings as well as history of oil ingestion, and can therefore be distinguished from one another. [ABSTRACT FROM AUTHOR]

Our purpose was to identify thin-section chest computed tomography (CT) findings of malignancy other than the presence of a solid portion within ground-glass nodules (GGNs) and to evaluate whether the radiologists' performance in determining malignancy can be enhanced with this information. The predictive CT findings of malignancy extracted from the CT findings of 80 GGNs (47 malignant, 33 benign) were a size of >8 mm [odds ratio (OR), 10.930; P = 0.045] and a lobulated border (OR, 13.769; P = 0.016) for pure GGNs and a lobulated border (OR, 10.200; P = 0.024) for mixed GGNs. Four chest radiologists and five radiology residents participated in the observer performance study with CT of 130 GGNs (67 malignant, 63 benign). Receiver-operating characteristic (ROC) analysis was used to compare radiologists' performances before and after providing these predictive findings. For pure GGNs, mean areas under the curve (A(z)) of all readers without and with CT predictive information were significantly different (0.621 +/- 0.052 and 0.766 +/- 0.055, P < 0.05). For mixed GGNs, the A(z) values achieved without and with predictive information were not significantly different (0.727 +/- 0.064 and 0.764 +/- 0.056, P > 0.05). Information about lesion size and morphological characteristics can enhance radiologists' performance in determining malignancy of pure GGNs. [ABSTRACT FROM AUTHOR]

Background: The clinical significance of pulmonary nodular ground-glass opacities (NGGOs) in patients with extrapulmonary cancers is not known, although there is an urgent need for study on this topic. The purpose of this study, therefore, was to investigate the clinical significance of pulmonary NGGOs in these patients, and to develop a computerized scheme to distinguish malignant from benign NGGOs.Methods: Fifty-nine pathologically proven pulmonary NGGOs in 34 patients with a history of extrapulmonary cancer were studied. We reviewed the CT scan characteristics of NGGOs and the clinical features of these patients. Artificial neural networks (ANNs) were constructed and tested as a classifier distinguishing malignant from benign NGGOs. The performance of ANNs was evaluated with receiver operating characteristic analysis.Results: Twenty-eight patients (82.4%) were determined to have malignancies. Forty NGGOs (67.8%) were diagnosed as malignancies (adenocarcinomas, 24; bronchioloalveolar carcinomas, 16). Among the rest of the NGGOs, 14 were atypical adenomatous hyperplasias, 4 were focal fibrosis, and 1 was an inflammatory nodule. There were no cases of metastasis appearing as NGGOs. Between malignant and benign NGGOs, there were significant differences in lesion size; the presence of internal solid portion; the size and proportion of the internal solid portion; the lesion margin; and the presence of bubble lucency, air bronchogram, or pleural retraction (p < 0.05). Using these characteristics, ANNs showed excellent accuracy (z value, 0.973) in discriminating malignant from benign NGGOs.Conclusions: Pulmonary NGGOs in patients with extrapulmonary cancers tend to have high malignancy rates and are very often primary lung cancers. ANNs might be a useful tool in distinguishing malignant from benign NGGOs. [ABSTRACT FROM AUTHOR]

Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radiographs from consecutive, asymptomatic Asian individuals aged 50-80 years who underwent health checkups between January 2004 and June 2018. This study performed a dedicated external test of a previously developed CXR-Age model, which predicts an age adjusted based on the risk of all-cause mortality. Adjusted hazard ratios (HRs) of CXR-Age for all-cause, cardiovascular, lung cancer, and respiratory disease mortality were assessed using multivariable Cox or Fine-Gray models, and their added values were evaluated by likelihood ratio tests. Results A total of 36 924 individuals (mean chronological age, 58 years ± 7 [SD]; CXR-Age, 60 years ± 5; 22 352 male) were included. During a median follow-up of 11.0 years, 1250 individuals (3.4%) died, including 153 cardiovascular (0.4%), 166 lung cancer (0.4%), and 98 respiratory (0.3%) deaths. CXR-Age was a significant risk factor for all-cause (adjusted HR at chronological age of 50 years, 1.03; at 60 years, 1.05; at 70 years, 1.07), cardiovascular (adjusted HR, 1.11), lung cancer (adjusted HR for individuals who formerly smoked, 1.12; for those who currently smoke, 1.05), and respiratory disease (adjusted HR, 1.12) mortality ( P < .05 for all). The likelihood ratio test demonstrated added prognostic value of CXR-Age to clinical factors, including chronological age for all outcomes ( P < .001 for all). Conclusion Deep learning-based chest radiographic age was associated with various survival outcomes and had added value to clinical factors in asymptomatic Asian individuals, suggesting its generalizability. Keywords: Conventional Radiography, Thorax, Heart, Lung, Mediastinum, Outcomes Analysis, Quantification, Prognosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Adams and Bressem in this issue.

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 D LCO 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., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their prognostic implications., Methods: This study included ILD patients with CT scans at intervals of over 2 years between January 2015 and June 2021. Deep learning-based texture analysis software was used to segment ILD findings on CT images (fibrosis: reticular opacity + honeycombing cysts; total ILD extent: ground-glass opacity + fibrosis). Patients were grouped according to the absolute decline of predicted FVC (< 5%, 5-10%, and ≥ 10%) and ILD progression assessed by thoracic radiologists, and their quantification results were compared among these groups. The associations between quantification results and survival were evaluated using multivariable Cox regression analysis., Results: In total, 468 patients (239 men; 64 ± 9.5 years) were included. Fibrosis and total ILD extents more increased in patients with larger FVC decline (p < .001 in both). Patients with ILD progression had higher fibrosis and total ILD extent increases than those without ILD progression (p < .001 in both). Increases in fibrosis and total ILD extent were significant prognostic factors when adjusted for absolute FVC declines of ≥ 5% (hazard ratio [HR] 1.844, p = .01 for fibrosis; HR 2.484, p < .001 for total ILD extent) and ≥ 10% (HR 2.918, p < .001 for fibrosis; HR 3.125, p < .001 for total ILD extent)., Conclusion: Changes in ILD CT quantification correlated with changes in FVC and visual assessment of ILD progression, and they were independent prognostic factors in ILD patients., Clinical Relevance Statement: Quantifying the CT features of interstitial lung disease using deep learning techniques could play a key role in defining and predicting the prognosis of progressive fibrosing interstitial lung disease., Key Points: • Radiologic findings on high-resolution CT are important in diagnosing progressive fibrosing interstitial lung disease. • Deep learning-based quantification results for fibrosis and total interstitial lung disease extents correlated with the decline in forced vital capacity and visual assessments of interstitial lung disease progression, and emerged as independent prognostic factors. • Deep learning-based interstitial lung disease CT quantification can play a key role in diagnosing and prognosticating progressive fibrosing interstitial lung disease., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model., Methods: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis., Results: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13)., Conclusion: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas., Clinical Relevance Statement: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management., Key Points: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13)., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer-specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection. Results The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56-70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58-71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity. Conclusion The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria. © RSNA, 2024 Supplemental material is available for this article.

Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March and October 2022. Participants underwent two same-day CT scans at an interval of a few minutes. Deep learning-based texture analysis software was used to segment ILD features. Fibrosis extent was defined as the sum of reticular opacity and honeycombing cysts. Measurement variability between scans was assessed with Bland-Altman analyses for absolute and relative differences with 95% limits of agreement (LOA). The contribution of fibrosis extent to variability was analyzed using a multivariable linear mixed-effects model while adjusting for lung volume. Eight readers assessed ILD fibrosis stability with and without QCT information for 30 randomly selected samples. Results Sixty-five participants were enrolled in this study (mean age, 68.7 years ± 10 [SD]; 47 [72%] men, 18 [28%] women). Between two same-day CT scans, the 95% LOA for the mean absolute and relative differences of quantitative fibrosis extent were -0.9% to 1.0% and -14.8% to 16.1%, respectively. However, these variabilities increased to 95% LOA of -11.3% to 3.9% and -123.1% to 18.4% between CT scans with different reconstruction parameters. Multivariable analysis showed that absolute differences were not associated with the baseline extent of fibrosis ( P = .09), but the relative differences were negatively associated (β = -0.252, P < .001). The QCT results increased readers' specificity in interpreting ILD fibrosis stability (91.7% vs 94.6%, P = .02). Conclusion The absolute QCT measurement variability of fibrosis extent in ILD was 1% in same-day CT scans. Keywords: CT, CT-Quantitative, Thorax, Lung, Lung Diseases, Interstitial, Pulmonary Fibrosis, Diagnosis, Computer Assisted, Diagnostic Imaging Supplemental material is available for this article . © RSNA, 2024.

Objectives: The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis., Methods: In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression. Commercial artificial intelligence software was adopted as the second reader for the presence of ground-glass opacity. The primary end points were freedom from recurrence (FFR) and lung cancer-specific survival (LCSS). In a meta-analysis, we systematically searched Embase and OVID-MEDLINE up to December 30, 2021, for the studies based on the eighth-edition staging system. The pooled hazard ratios (HRs) of solid nodules (i.e., absence of ground-glass opacity) for various end points were calculated with a multi-level random effects model., Results: In a cohort of 612 patients, solid nodules were associated with worse outcomes for FFR (adjusted HR, 1.98; 95% CI: 1.17-3.51; p = 0.01) and LCSS (adjusted HR, 1.937; 95% CI: 1.002-4.065; p = 0.049). The artificial intelligence assessment and multiple sensitivity analyses revealed consistent results. The meta-analysis included 13 studies with 12,080 patients. The pooled HR of solid nodules was 2.13 (95% CI: 1.69-2.67; I 2  = 30.4%) for overall survival, 2.45 (95% CI: 1.52-3.95; I 2  = 0.0%) for FFR, and 2.50 (95% CI: 1.28-4.91; I 2  = 30.6%) for recurrence-free survival., Conclusions: The absence of ground-glass opacity in early-stage lung adenocarcinomas is associated with worse postoperative survival., Clinical Relevance Statement: Early-stage lung adenocarcinomas manifesting as solid nodules at preoperative chest CT, which indicates the absence of ground-glass opacity, were associated with poor postoperative survival. There is room for improvement of the clinical T categorization in the next edition staging system., Key Points: • In a retrospective study of 612 patients with stage I lung adenocarcinoma, solid nodules were associated with shorter freedom from recurrence (adjusted hazard ratio [HR], 1.98; p = 0.01) and lung cancer-specific survival (adjusted HR, 1.937; p = 0.049). • Artificial intelligence-assessed solid nodules also showed worse prognosis (adjusted HR for freedom from recurrence, 1.94 [p = 0.01]; adjusted HR for lung cancer-specific survival, 1.93 [p = 0.04]). • In meta-analyses, the solid nodules were associated with shorter freedom from recurrence (HR, 2.45) and shorter overall survival (HR, 2.13)., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas., Methods: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression., Results: In 681 patients (median age, 65 years [interquartile range, 58-71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3-71.9%; specificity, 71.6-88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43-0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84-1.92 and 0.63-1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86-2.42 and 0.67-1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07-3.07; p = 0.03)., Conclusion: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome., Clinical Relevance Statement: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability., Key Points: • Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. • Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. • Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality., Materials and Methods: This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models., Results: The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status., Conclusions: The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals., Competing Interests: Conflicts of interest and source of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The prognostic role of changes in body fat in patients with idiopathic pulmonary fibrosis (IPF) remains underexplored. We investigated the association between changes in body fat during the first year post-diagnosis and outcomes in patients with IPF., Methods: This single-center, retrospective study included IPF patients with chest CT scan and pulmonary function test (PFT) at diagnosis and a one-year follow-up between January 2010 and December 2020. The fat area (cm 2 , sum of subcutaneous and visceral fat) and muscle area (cm 2 ) at the T12-L1 level were obtained from chest CT images using a fully automatic deep learning-based software. Changes in the body composition were dichotomized using thresholds dividing the lowest quartile and others, respectively (fat area: -52.3 cm 2 , muscle area: -7.4 cm 2 ). Multivariable Cox regression analyses adjusted for PFT result and IPF extent on CT images and the log-rank test were performed to assess the association between the fat area change during the first year post-diagnosis and the composite outcome of death or lung transplantation., Results: In total, 307 IPF patients (69.3 ± 8.1 years; 238 men) were included. During the first year post-diagnosis, fat area, muscle area, and body mass index (BMI) changed by -15.4 cm 2 , -1 cm 2 , and - 0.4 kg/m 2 , respectively. During a median follow-up of 47 months, 146 patients had the composite outcome (47.6%). In Cox regression analyses, a change in the fat area < -52.3 cm 2 was associated with composite outcome incidence in models adjusted with baseline clinical variables (hazard ratio [HR], 1.566, P = .022; HR, 1.503, P = .036 in a model including gender, age, and physiology [GAP] index). This prognostic value was consistent when adjusted with one-year changes in clinical variables (HR, 1.495; P = .030). However, the change in BMI during the first year was not a significant prognostic factor (P = .941). Patients with a change in fat area exceeding this threshold experienced the composite outcome more frequently than their counterparts (58.4% vs. 43.9%; P = .007)., Conclusion: A ≥ 52.3 cm 2 decrease in fat area, automatically measured using deep learning technique, at T12-L1 in one year post-diagnosis was an independent poor prognostic factor in IPF patients., (© 2024. The Author(s).)

Objectives: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data., Methods: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present., Results: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69)., Conclusions: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities., Advances in Knowledge: This is the first study applying super-resolution in data reduction of chest radiographs., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR)., Methods: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool., Results: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65)., Conclusion: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images., Competing Interests: This work was supported by GE Healthcare (Grant number: 06-2020-0300). There are no patents, products in development, or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

BACKGROUND. Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. OBJECTIVE. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. METHODS. This retrospective single-center study included 4187 patients (mean age, 63.8 ± 11.9 [SD] years; 2513 men, 1674 women) who underwent PTNB between January 2010 and December 2015. Patients were categorized into a UIP group and non-UIP group by review of preprocedural CT. In the UIP group, procedural CT images were reviewed to assess for traversal of UIP findings by needle. Multivariable logistic regression analyses were performed to identify associations between the UIP group and needle traversal with postbiopsy complications, controlling for a range of patient, lesion, and procedural characteristics. RESULTS. The UIP and non-UIP groups included 148 and 4039 patients, respectively; in the UIP group, traversal of UIP findings by needle was observed in 53 patients and not observed in 95 patients. The UIP group, in comparison with the non-UIP group, had a higher frequency of pneumothorax (35.1% vs 17.9%, p < .001) and pneumothorax requiring chest tube placement (6.1% vs 1.5%, p = .001) and lower frequency of hemoptysis (2.0% vs 6.1%, p = .03). In multivariable analyses, the UIP group with traversal of UIP findings by needle, relative to the non-UIP group, showed independent associations with pneumothorax (OR, 5.25; 95% CI, 2.94-9.37; p < .001) and pneumothorax requiring chest tube placement (OR, 9.55; 95% CI, 3.74-24.38; p < .001). The UIP group without traversal of UIP findings by needle, relative to the non-UIP group, was not independently associated with pneumothorax (OR, 1.18; 95% CI, 0.71-1.97; p = .51) or pneumothorax requiring chest tube placement (OR, 1.08; 95% CI, 0.25-4.72; p = .92). The UIP group, with or without traversal of UIP findings by needle, was not independently associated with hemoptysis. No patient experienced air embolism or procedure-related death. CONCLUSION. Needle traversal of UIP findings is a risk factor for pneumothorax and pneumothorax requiring chest tube placement after PTNB. CLINICAL IMPACT. When performing PTNB in patients with UIP, radiologists should plan a needle trajectory that does not traverse UIP findings, when possible.

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article . See also the editorials by Bhalla and Powell in this issue.

Purpose: Smoking cessation intervention is one of the key components of successful lung cancer screening program. We investigated the effectiveness and related factors of smoking cessation services provided to the participants in a population-based lung cancer screening trial., Materials and Methods: The Korean Lung Cancer Screening Project (K-LUCAS) is a nationwide, multi-center lung cancer screening trial that evaluates the feasibility of implementing population-based lung cancer screening. All 5,144 current smokers who participated in the K-LUCAS received a mandatory smoking cessation counseling. Changes in smoking status were followed up using a telephone survey in 6 months after lung cancer screening participation. The lung cancer screening's impact on smoking cessation is analyzed by variations in the smoking cessation interventions provided in screening units., Results: Among 4,136 survey responders, participant's motivation to quit smoking increased by 9.4% on average after lung cancer screening. After 6 months from the initial screening, 24.3% of participants stopped smoking, and 10.6% of participants had not smoked continuously for at least 6 months after screening. Over 80% of quitters stated that participation in lung cancer screening motivated them to quit smoking. Low-cost public smoking cessation program combined with lung cancer screening increased the abstinence rates. The smokers were three times more likely to quit smoking when the smoking cessation counseling was provided simultaneously with low-dose computed tomography screening results than when provided separately., Conclusion: A mandatory smoking cessation intervention integrated with screening result counselling by a physician after participation in lung cancer screening could be effective for increasing smoking cessation attempts.

BACKGROUND. Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. OBJECTIVE. The purpose of our study was to evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. METHODS. This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1 to March 31, 2020, in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1 to January 3, 2020, in 302 patients (157 men, 145 women; mean age, 66 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1 to January 20, 2020, in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C). A commercial DL-based AI system was used to identify ETT presence and measure ETT tip-to-carina distance (TCD). The reference standard for proper ETT position was TCD between greater than 3 cm and less than 7 cm, determined by human readers. Critical ETT position was separately defined as ETT tip below the carina or TCD of 1 cm or less. ROC analysis was performed. RESULTS. AI had sensitivity and specificity for identification of ETT presence of 100.0% and 98.7% (sample B) and 99.2% and 94.5% (sample C). AI had sensitivity and specificity for identification of improper ETT position of 72.5% and 92.0% (sample A), 78.9% and 100.0% (sample B), and 83.7% and 99.1% (sample C). At a threshold y -axis TCD of 2 cm or less, AI had sensitivity and specificity for critical ETT position of 100.0% and 96.7% (sample A), 100.0% and 100.0% (sample B), and 100.0% and 99.2% (sample C). CONCLUSION. AI identified improperly positioned ETTs on chest radiographs obtained after ETT insertion as well as on chest radiographs obtained of patients in the ICU at two institutions. CLINICAL IMPACT. Automated AI identification of improper ETT position on chest radiographs may allow earlier repositioning and thereby reduce complications.

Rationale and Objectives: The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves., Results: In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012)., Conclusion: Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia., Competing Interests: Declaration of Competing Interest Eui Jin Hwang reports financial support was provided by Korea Ministry of Science and ICT. Eui Jin Hwang reports a relationship with Lunit Inc that includes: funding grants. Chang Min Park reports a relationship with Lunit Inc that includes: funding grants. Chang Min Park holds stock of Promedius and stock options of LunitInc. and Coreline Soft., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: To investigate computer-aided quantitative scores from high-resolution CT (HRCT) images and determine their longitudinal changes and clinical significance in patients with idiopathic inflammatory myopathies (IIMs)-related interstitial lung disease (IIMs-ILD)., Methods: The clinical data and HRCT images of 80 patients with IIMs who underwent serial HRCT scans at least twice were retrospectively analysed. Quantitative ILD (QILD) scores (%) were calculated as the sum of the extent of lung fibrosis, ground-glass opacity, and honeycombing. The individual time-estimated ΔQILD between two consecutive scans was derived using a linear approximation of yearly changes., Results: The baseline median QILD (interquartile range) scores in the whole lung were 28.1% (19.1-43.8). The QILD was significantly correlated with forced vital capacity (r = -0.349, P = 0.002) and diffusing capacity for carbon monoxide (r = -0.381, P = 0.001). For ΔQILD between the first two scans, according to the visual ILD subtype, QILD aggravation was more frequent in patients with usual interstitial pneumonia (UIP) than non-UIP (80.0% vs 44.4%, P = 0.013). Multivariable logistic regression analyses identified UIP was significantly related to radiographic ILD progression (ΔQILD >2%, P = 0.015). Patients with higher baseline QILD scores (>28.1%) had a higher risk of lung transplantation or death (P = 0.015). In the analysis of three serial HRCT scans (n = 41), dynamic ΔQILD with four distinct patterns (improving, worsening, convex and concave) was observed., Conclusion: QILD changes in IIMs-ILD were dynamic, and baseline UIP patterns seemed to be related to a longitudinal progression in QILD. These may be potential imaging biomarkers for lung function, changes in ILD severity and prognosis in IIMs-ILD., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

BACKGROUND. Chest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. OBJECTIVE. The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. METHODS. In this retrospective study, a DL model was developed from data on 7105 patients from one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis by use of patients' initial chest radiographs. The DL model was evaluated in a cohort of patients diagnosed with CAP during emergency department visits at the same institution from January 2020 to March 2020 (temporal test cohort [ n = 947]) and in two additional cohorts from different institutions (external test cohort A [ n = 467], January 2020 to December 2020; external test cohort B [ n = 381], March 2019 to October 2021). AUCs were compared between the DL model and an established risk prediction tool based on the presence of confusion, blood urea nitrogen level, respiratory rate, blood pressure, and age 65 years or older (CURB-65 score). The combination of CURB-65 score and DL model was evaluated with a logistic regression model. RESULTS. The AUC for predicting 30-day mortality was significantly larger ( p < .001) for the DL model than for CURB-65 score in the temporal test set (0.77 vs 0.67). The larger AUC for the DL model than for CURB-65 score was not significant ( p > .05) in external test cohort A (0.80 vs 0.73) or external test cohort B (0.80 vs 0.72). In the three cohorts, the DL model, in comparison with CURB-65 score, had higher ( p < .001) specificity (range, 61-69% vs 44-58%) at the sensitivity of CURB-65 score. The combination of DL model and CURB-65 score, in comparison with CURB-65 score, yielded a significant increase in AUC in the temporal test cohort (0.77, p < .001) and external test cohort B (0.80, p = .04) and a nonsignificant increase in AUC in external test cohort A (0.80, p = .16). CONCLUSION. A DL-based model consisting of initial chest radiographs was predictive of 30-day mortality among patients with CAP with improved performance over CURB-65 score. CLINICAL IMPACT. The DL-based model may guide clinical decision-making in the care of patients with CAP.

Objective: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR)., Materials and Methods: We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC)., Results: The median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06)., Conclusion: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objective: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT)., Materials and Methods: AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit., Results: A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33; P = 0.008) and referrals with subsequent negative chest CT results (OR, 1.46; P = 0.005). Meanwhile, referrals with positive chest CT results were not significantly associated with AI-CAD use (OR, 1.08; P = 0.647)., Conclusion: The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results., Competing Interests: Eui Jin Hwang reports research grants from Lunit, Coreline Soft, and Monitor corporation, outside the present study; Chang Min Park reports a research grant from Lunit, outside the present study, and stock of Promedius and stock options of Lunit and Coreline Soft; Jin Mo Goo reports research grants from Infinitt Healthcare, Dongkook Lifescience, and LG Electronics, outside the present study. Jin Mo Goo, a contributing editor of the Korean Journal of Radiology, was not involved in the editorial evaluation or decision to publish this article. Wonju Hong has declared no conflicts of interest., (Copyright © 2023 The Korean Society of Radiology.)

Background: Whether antimicrobial treatment improves long-term survival in patients with Mycobacterium avium complex pulmonary disease (MAC-PD) is unclear., Methods: We analyzed survival in patients aged ≥18 years who were treated for MAC-PD at a tertiary referral center in South Korea between 1 January 2009 and 31 December 2020. Treatment exposure was divided into 4 time intervals: <6, ≥6 to <12, ≥12 to <18, and ≥18 months. Time-varying multivariable Cox proportional hazards models were used to calculate the all-cause mortality risk in each time interval. The model was adjusted for major clinical factors related to mortality including age, sex, body mass index, presence of cavities, erythrocyte sedimentation rate, positive acid-fast bacilli (AFB) smear, clarithromycin resistance, and comorbid conditions., Results: A total of 486 patients treated for MAC-PD were included in the analysis. A significant inverse correlation was observed between mortality and duration of treatment (P for trend = .007). Long-term treatment (≥18 months) was significantly associated with reduced mortality (adjusted hazard ratio, 0.32 [95% confidence interval, .15-.71]). In subgroup analyses, patients with cavitary lesions (adjusted hazard ratio, 0.17 [95% confidence interval, .05-.57]) or positive AFB smears (0.13 [.02-.84]) at baseline maintained this significant inverse relationship between treatment duration and mortality., Conclusions: Long-term antimicrobial treatment should be actively considered in patients with progressive MAC-PD, especially in the presence of cavities or positive AFB smears indicative of high mycobacterial burden., Competing Interests: Potential conflicts of interest. J. M. G. received research grants from Infinitt Healthcare, Dongkook Lifescience, and LG Electronics. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically., Materials and Methods: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent., Results: GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively., Conclusion: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Soon Ho Yoon work as chief medical officer in MEDICALIP. Joseph Nathanael Witanto is a machine learning researcher in the same company. Hyungjin Kim has a stock of MEDILCAIP. Those authors did not involve data/statistical analysis]. Other authors have no competing interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Rationale: Modeling imaging surrogates for well-validated histopathological risk factors would enable prognostication in early-stage lung adenocarcinomas. Objectives: We aimed to develop and validate computed tomography (CT)-based deep learning (DL) models for the prognostication of early-stage lung adenocarcinomas through learning histopathological features and to investigate the models' reproducibility using retrospective, multicenter datasets. Methods: Two DL models were trained to predict visceral pleural invasion and lymphovascular invasion, respectively, using preoperative chest CT scans from 1,426 patients with stage I-IV lung adenocarcinomas. The averaged model output was defined as the composite score and evaluated for the prognostic discrimination and its added value to clinicopathological factors in temporal ( n  = 610) and external test sets ( n  = 681) of stage I lung adenocarcinomas. The study outcomes were freedom from recurrence (FFR) and overall survival (OS). Interscan and interreader reproducibility were analyzed in 31 patients with lung cancer who underwent same-day repeated CT scans. Results: For the temporal test set, the time-dependent area under the receiver operating characteristic curve was 0.76 (95% confidence interval [CI], 0.71-0.81) for 5-year FFR and 0.67 (95% CI, 0.59-0.75) for 5-year OS. For the external test set, the area under the curve was 0.69 (95% CI, 0.63-0.75) for 5-year OS. The discrimination performance remained stable in 10-year follow-up for both outcomes. The prognostic value of the composite score was independent of and complementary to the clinical factors (adjusted per-percent hazard ratio for FFR [temporal test], 1.04 [95% CI, 1.03-1.05; P  < 0.001]; OS [temporal test], 1.03 [95% CI, 1.02-1.04; P  < 0.001]; OS [external test], 1.03 [95% CI, 1.02-1.04; P  < 0.001]). The likelihood ratio tests indicated added value of the composite score (all P  < 0.05). The interscan and interreader reproducibility were excellent (Pearson's correlation coefficient, 0.98 for both). Conclusions: The CT-based composite score obtained from DL of histopathological features predicted survival in early-stage lung adenocarcinomas with high reproducibility.

Background: The prognostic role of increased visceral fat attenuation (VFA) remains underexplored. We investigated the long-term prognostic implications of computed tomography (CT)-derived VFA in a health check-up population., Methods: This study included consecutive individuals who had positron-emission tomography/CT scans for health check-ups between January 2004 and December 2010. The primary outcome was overall survival (OS), and the secondary outcomes were cancer-specific survival (CSS) and non-cancer-specific survival (NCS). Commercially available body composition analysis software was used to obtain abdominal waist VFA, visceral fat volume index (VFI) and skeletal muscle index (SMI) at the L3 level. Sarcopenia was determined using sex-specific SMI references. VFA and VFI were dichotomized using the thresholds for the highest quartiles. The relationship between CT-derived body composition parameters and body mass index (BMI) was evaluated with Pearson correlation coefficients. The prognostic implications of VFA and sarcopenic obesity (SO) defined by VFA were assessed by multivariable Cox regression analysis and Kaplan-Meier plots with log-rank tests., Results: A total of 2720 individuals (1530 men [56.3%] and 1190 women [43.7%]; median age: 53 years, inter-quartile range: 47-60 years) were included. During the median follow-up of 138 months, 128 individuals (5%) died (cancer mortality: 2%; non-cancer mortality: 3%), with 0.2% (5 of 2720) and 1.1% (30 of 2720) of 1- and 5-year mortality rates. VFA was negatively correlated with BMI (r = -0.62; P < 0.001) and VFI (r = -0.69; P < 0.001). After adjusting for clinical variables, sarcopenia and VFI, high VFA was a negative prognostic factor for OS (hazard ratio [HR]: 1.05 per Hounsfield unit; 95% confidence interval [CI]: 1.02, 1.08; P = 0.001), CSS (HR: 1.07 per Hounsfield unit; 95% CI: 1.02, 1.12; P = 0.006) and NCS (HR: 1.03 per Hounsfield unit; 95% CI: 1.01, 1.06; P = 0.009). Individuals with high VFA had higher high-sensitivity C-reactive protein levels than those with low VFA (0.11 vs. 0.03 mg/dL; P < 0.001). Individuals with SO defined by VFA had worse OS (9% vs. 4%; P < 0.001), CSS (3% vs. 2%; P = 0.02) and NCS (6% vs. 3%; P < 0.001) than those without SO, even in the same BMI (underweight-to-normal BMI, OS: 8% vs. 4%; overweight-to-obese BMI, OS: 38% vs. 4%; P < 0.001 in both) or VFI category (high VFI, OS: 43% vs. 6%; low VFI, OS: 8% vs. 3%; P < 0.001 in both)., Conclusions: High VFA was associated with long-term mortality and low-grade inflammation. VFA can further stratify the current SO by BMI or VFI, and SO defined by VFA can identify individuals who are most vulnerable to long-term mortality., (© 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Objective: To investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF)., Methods: Patients diagnosed with IPF who underwent nonenhanced chest CT and spirometry between 2005 and 2009 were retrospectively collected. Proportions of normal (CT-Norm%) and fibrotic lung volume (CT-Fib%) were calculated on CT using the DL software. The correlations of CT-Norm% and CT-Fib% with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (D LCO ) were evaluated. The multivariable-adjusted hazard ratios (HRs) of CT-Norm% and CT-Fib% for overall survival were calculated with clinical and physiologic variables as covariates using Cox regression. The feasibility of substituting CT-Norm% for D LCO in the GAP index was investigated using time-dependent areas under the receiver operating characteristic curve (TD-AUCs) at 3 years., Results: In total, 161 patients (median age [IQR], 68 [62-73] years; 104 men) were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC (Pearson's r, 0.40 for CT-Norm% and - 0.37 for CT-Fib%; both p < 0.001) and D LCO (0.52 for CT-Norm% and - 0.46 for CT-Fib%; both p < 0.001). On multivariable Cox regression, both CT-Norm% and CT-Fib% were independent prognostic factors when adjusted to age, sex, smoking status, comorbid chronic diseases, FVC, and D LCO (HRs, 0.98 [95% CI 0.97-0.99; p < 0.001] for CT-Norm% at 3 years and 1.03 [1.01-1.05; p = 0.01] for CT-Fib%). Substituting CT-Norm% for D LCO showed comparable discrimination to the original GAP index (TD-AUC, 0.82 [0.78-0.85] vs. 0.82 [0.79-0.86]; p = 0.75)., Conclusion: CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software were independent prognostic factors for overall survival in IPF., Key Points: • Normal and fibrotic lung volume proportions were automatically calculated using commercial deep learning software from chest CT taken from 161 patients diagnosed with idiopathic pulmonary fibrosis. • CT-quantified volumetric parameters from commercial deep learning software were correlated with forced vital capacity (Pearson's r, 0.40 for normal and - 0.37 for fibrotic lung volume proportions) and diffusion capacity of carbon monoxide (Pearson's r, 0.52 and - 0.46, respectively). • Normal and fibrotic lung volume proportions (hazard ratios, 0.98 and 1.04; both p < 0.001) independently predicted overall survival when adjusted for clinical and physiologic variables., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To evaluate the accuracy of a deep learning-based computer-aided detection (CAD) system in identifying active pulmonary tuberculosis on chest radiographs (CRs) of patients with positive interferon-gamma release assay (IGRA) results in different scenarios of clinical implementation., Materials and Methods: We collected the CRs of consecutive patients with positive IGRA results. Findings of active pulmonary tuberculosis on CRs were independently evaluated by the CAD and a thoracic radiologist, followed by interpretation using the CAD. Sensitivity and specificity were evaluated in different scenarios: (a) radiologists' interpretation, (b) radiologists' CAD-assisted interpretation, and (c) CAD-based prescreening (radiologists' interpretation for positive CAD results only). We conducted a reader test to compare the accuracy of the CAD with those of 5 radiologists., Results: Among 1780 patients (men, 53.8%; median age, 56 y), 44 (2.5%) were diagnosed with active pulmonary tuberculosis. The CAD-assisted interpretation exhibited a higher sensitivity (81.8% vs. 72.7%; P =0.046) but lower specificity than the radiologists' interpretation (84.1% vs. 85.7%; P <0.001). The CAD-based prescreening exhibited a higher specificity than the radiologists' interpretation (88.8% vs. 85.7%; P <0.001) at the same sensitivity, with a workload reduction of 85.2% (1780 to 263). In the reader test, the CAD exhibited a higher sensitivity than radiologists (72.7% vs. 59.5%; P =0.005) at the same specificity (88.0%), and CAD-assisted interpretation significantly improved the sensitivity of radiologists' interpretation (72.3%; P <0.001)., Conclusions: For identifying active pulmonary tuberculosis among patients with positive IGRA results, deep learning-based CAD can enhance the sensitivity of interpretation. CAD-based prescreening may reduce the radiologists' workload at an improved specificity., Competing Interests: E.J.H. received a research grant from Lunit Inc. outside the present study. J.G.N. received a research grant from Vuno, outside the present study. C.M.P. received a research grant from Lunit Inc. outside the present study, and holds stock of Promedius and stock options of Lunit Inc. and Coreline Soft. The remaining authors declare no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background The impact of artificial intelligence (AI)-based computer-aided detection (CAD) software has not been prospectively explored in real-world populations. Purpose To investigate whether commercial AI-based CAD software could improve the detection rate of actionable lung nodules on chest radiographs in participants undergoing health checkups. Materials and Methods In this single-center, pragmatic, open-label randomized controlled trial, participants who underwent chest radiography between July 2020 and December 2021 in a health screening center were enrolled and randomized into intervention (AI group) and control (non-AI group) arms. One of three designated radiologists with 13-36 years of experience interpreted each radiograph, referring to the AI-based CAD results for the AI group. The primary outcome was the detection rate, that is, the number of true-positive radiographs divided by the total number of radiographs, of actionable lung nodules confirmed on CT scans obtained within 3 months. Actionable nodules were defined as solid nodules larger than 8 mm or subsolid nodules with a solid portion larger than 6 mm (Lung Imaging Reporting and Data System, or Lung-RADS, category 4). Secondary outcomes included the positive-report rate, sensitivity, false-referral rate, and malignant lung nodule detection rate. Clinical outcomes were compared between the two groups using univariable logistic regression analyses. Results A total of 10 476 participants (median age, 59 years [IQR, 50-66 years]; 5121 men) were randomized to an AI group ( n = 5238) or non-AI group ( n = 5238). The trial met the predefined primary outcome, demonstrating an improved detection rate of actionable nodules in the AI group compared with the non-AI group (0.59% [31 of 5238 participants] vs 0.25% [13 of 5238 participants], respectively; odds ratio, 2.4; 95% CI: 1.3, 4.7; P = .008). The detection rate for malignant lung nodules was higher in the AI group compared with the non-AI group (0.15% [eight of 5238 participants] vs 0.0% [0 of 5238 participants], respectively; P = .008). The AI and non-AI groups showed similar false-referral rates (45.9% [56 of 122 participants] vs 56.0% [56 of 100 participants], respectively; P = .14) and positive-report rates (2.3% [122 of 5238 participants] vs 1.9% [100 of 5238 participants]; P = .14). Conclusion In health checkup participants, artificial intelligence-based software improved the detection of actionable lung nodules on chest radiographs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Auffermann in this isssue.

It is unclear whether the visualization methods for artificial-intelligence-based computer-aided detection (AI-CAD) of chest radiographs influence the accuracy of readers' interpretation. We aimed to evaluate the accuracy of radiologists' interpretations of chest radiographs using different visualization methods for the same AI-CAD. Initial chest radiographs of patients with acute respiratory symptoms were retrospectively collected. A commercialized AI-CAD using three different methods of visualizing was applied: (a) closed-line method, (b) heat map method, and (c) combined method. A reader test was conducted with five trainee radiologists over three interpretation sessions. In each session, the chest radiographs were interpreted using AI-CAD with one of the three visualization methods in random order. Examination-level sensitivity and accuracy, and lesion-level detection rates for clinically significant abnormalities were evaluated for the three visualization methods. The sensitivity ( p = 0.007) and accuracy ( p = 0.037) of the combined method are significantly higher than that of the closed-line method. Detection rates using the heat map method ( p = 0.043) and the combined method ( p = 0.004) are significantly higher than those using the closed-line method. The methods for visualizing AI-CAD results for chest radiographs influenced the performance of radiologists' interpretations. Combining the closed-line and heat map methods for visualizing AI-CAD results led to the highest sensitivity and accuracy of radiologists.

Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial., Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit., Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD., Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED., Competing Interests: Jin Mo Goo who is on the editorial board of the Korean Journal of Radiology was not involved in the editorial evaluation or decision to publish this article., (Copyright © 2023 The Korean Society of Radiology.)