Surgical Outcomes in Non-Small Cell Lung Cancer with Distant Metastasis: The Prognostic Significance of Delayed Metastasis Diagnosis.

BACKGROUND: The diagnosis of distant metastasis on preoperative examinations for non-small cell lung cancer (NSCLC) can be challenging, leading to surgery for some patients with uncertain metastasis. This study evaluated the prognostic impact of delayed diagnosis of metastasis on patients who underwent upfront surgery. METHODS: The study enrolled patients who underwent lobectomy or pneumonectomy for NSCLC between June 2010 and December 2017 and evaluated the presence of distant metastasis before surgery. Overall survival (OS) for patients with stage IV cancer was compared with that for patients without metastasis, and the prognostic factors were analyzed. RESULTS: Of 3046 patients (mean age, 63 years; 1770 men), 100 (3.3 %) had distant metastasis, diagnosed preoperatively in 1.4 % (42/3046) and postoperatively in 1.9 % (58/3046) of the patients. The two most common metastasis sites diagnosed after surgery were contralateral lung (22/58, 37.9 %) and ipsilateral pleura (16/58, 27.6 %). The OS (median, 42.7 months) for the patients with stage IV cancer diagnosed postoperatively was comparable with that for the patients with stage IIIB cancer (P = 0.865), whereas the OS (median OS, 91.7 months) for the patients with stage IV cancer diagnosed preoperatively was better than for the patients with stage IIIB cancer (P = 0.001). Among the patients with distant metastasis, squamous cell type (hazard ratio [HR], 3.15; P = 0.002) and systemic treatment for metastasis (HR, 2.42; P = 0.002) were independent predictors of worse OS. CONCLUSIONS: Among NSCLC patients undergoing upfront surgery, the OS for the patients with stage IV cancer diagnosed postoperatively was comparable with that for the patients with stage IIIB cancer. For patients with stage IV disease, squamous cell type and systemic treatment for metastasis were prognostic factors for poorer OS.

Long-Term Follow-Up of Interstitial Lung Abnormality: Implication in Follow-Up Strategy and Risk Thresholds.

Rationale: The optimal follow-up computed tomography (CT) interval for detecting the progression of interstitial lung abnormality (ILA) is unknown. Objectives: To identify optimal follow-up strategies and extent thresholds on CT relevant to outcomes. Methods: This retrospective study included self-referred screening participants aged 50 years or older, including nonsmokers, who had imaging findings relevant to ILA on chest CT scans. Consecutive CT scans were evaluated to determine the dates of the initial CT showing ILA and the CT showing progression. Deep learning-based ILA quantification was performed. Cox regression was used to identify risk factors for the time to ILA progression and progression to usual interstitial pneumonia (UIP). Measurements and Main Results: Of the 305 participants with a median follow-up duration of 11.3 years (interquartile range, 8.4-14.3 yr), 239 (78.4%) had ILA on at least one CT scan. In participants with serial follow-up CT studies, ILA progression was observed in 80.5% (161 of 200), and progression to UIP was observed in 17.3% (31 of 179), with median times to progression of 3.2 years (95% confidence interval [CI], 3.0-3.4 yr) and 11.8 years (95% CI, 10.8-13.0 yr), respectively. The extent of fibrosis on CT was an independent risk factor for ILA progression (hazard ratio, 1.12 [95% CI, 1.02-1.23]) and progression to UIP (hazard ratio, 1.39 [95% CI, 1.07-1.80]). Risk groups based on honeycombing and extent of fibrosis (1% in the whole lung or 5% per lung zone) showed significant differences in 10-year overall survival (P = 0.02). Conclusions: For individuals with initially detected ILA, follow-up CT at 3-year intervals may be appropriate to monitor radiologic progression; however, those at high risk of adverse outcomes on the basis of the quantified extent of fibrotic ILA and the presence of honeycombing may benefit from shortening the interval for follow-up scans.

CT-guided percutaneous transthoracic needle biopsy for anterior mediastinal lymphoma: the role of PET/CT.

BACKGROUND: Computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) is not recommended as the diagnostic modality of choice for anterior mediastinal lymphoma, despite its advantages of minimal invasiveness and easy accessibility. PURPOSE: To identify the modifiable risk factors for non-diagnostic results from CT-guided PTNB for anterior mediastinal lymphoma. MATERIAL AND METHODS: This retrospective study identified CT-guided PTNB for anterior mediastinal lesions diagnosed as lymphoma between May 2007 and December 2021. The diagnostic sensitivity and complications were investigated. The appropriateness of PTNB targeting was evaluated using positron emission tomography (PET)/CT and images from intra-procedural CT-guided PTNB. Targeting was considered inappropriate when the supposed trajectory of the cutting needle was within a region of abnormally low metabolism. The risk factors for non-diagnostic results were determined using logistic regression analysis. RESULTS: A total of 67 PTNBs in 60 patients were included. The diagnostic sensitivity for lymphoma was 76.1% (51/67), with an immediate complication rate of 4.5% (3/67). According to the PET/CT images, PTNB targeting was inappropriate in 10/14 (71.4%) of the non-diagnostic PTNBs but appropriate in all diagnostic PTNBs (P <0.001). Inappropriate targeting was the only significant risk factor for non-diagnostic results (odds ratio = 203.69; 95% confidence interval = 8.17-999.99; P = 0.001). The number of specimen acquisitions was not associated with non-diagnostic results (P = 0.40). CONCLUSIONS: Only inappropriate targeting of the non-viable portion according to PET/CT was an independent risk factor for non-diagnostic results. Acquiring PET/CT scans before biopsy and targeting the viable portion on PET/CT may help improve the diagnostic sensitivity of PTNB.

Prognostic Implications of Synchronous Subsolid Nodules in Patients with Resected Subsolid Lung Adenocarcinoma.

Background For multiple subsolid nodules (SSNs) observed at lung CT, current management focuses on removal of the dominant (≥6 mm) nodule and monitoring of remaining SSNs. Whether the presence of these synchronous SSNs is related to postoperative patient outcomes has not been well established. Purpose To evaluate the prognostic value of single versus multiple synchronous SSNs at preoperative CT in patients with resected subsolid lung adenocarcinoma nodules. Materials and Methods This retrospective study included patients who underwent lobectomy or sublobar resection for lung adenocarcinoma manifesting as an SSN and clinical stage IA from January 2010 to December 2017. The radiologic features of the resected SSN (dominant nodule) and synchronous SSNs were assessed on preoperative CT scans. The effects of synchronous SSNs on time to secondary intervention, time to recurrence (TTR), and overall survival (OS) were evaluated using Cox regression analysis. Results Of the 684 included patients (mean age, 60.9 years ± 9.5 [SD]; 389 female), 515 (75.3%) had a single SSN and 169 (24.7%) had multiple SSNs on preoperative CT scans. During follow-up (median, 71.8 months), 38 secondary interventions were performed, primarily due to growth of synchronous SSNs (21 of 38) or metachronous nodules (14 of 38). As the number of synchronous SSNs greater than or equal to 6 mm in size increased, the time to secondary intervention decreased (P < .001). No association was observed between synchronous SSNs and TTR (P = .53) or OS (P = .65), but these measures were associated with features of the resected nodule, specifically solid portion size for TTR (P = .01) and histologic subtype for TTR and OS (P < .001 for both). Conclusion In patients with subsolid lung adenocarcinoma, the presence of synchronous SSNs on preoperative CT scans was not associated with TTR or OS, but the presence of synchronous SSNs greater than or equal to 6 mm in size was associated with an increased likelihood of secondary intervention. © RSNA, 2023 Supplemental material is available for this article.

Recurrence Patterns and Patient Outcomes in Resected Lung Adenocarcinoma Differ according to Ground-Glass Opacity at CT.

Background Although lung adenocarcinoma with ground-glass opacity (GGO) is known to have distinct characteristics, limited data exist on whether the recurrence pattern and outcomes in patients with resected lung adenocarcinoma differ according to GGO presence at CT. Purpose To examine recurrence patterns and associations with outcomes in patients with resected lung adenocarcinoma according to GGO at CT. Materials and Methods Patients who underwent CT followed by lobectomy or pneumonectomy for lung adenocarcinoma between July 2010 and December 2017 were retrospectively included. Patients were divided into two groups based on the presence of GGO: GGO adenocarcinoma and solid adenocarcinoma. Recurrence patterns at follow-up CT examinations were investigated and compared between the two groups. The effects of patient grouping on time to recurrence, postrecurrence survival (PRS), and overall survival (OS) were evaluated using Cox regression. Results Of 1019 patients (mean age, 62 years ± 9 [SD]; 520 women), 487 had GGO adenocarcinoma and 532 had solid adenocarcinoma. Recurrences occurred more frequently in patients with solid adenocarcinoma (36.1% [192 of 532 patients]) than in those with GGO adenocarcinoma (16.2% [79 of 487 patients]). Distant metastasis was the most common mode of recurrence in the group with solid adenocarcinoma and all clinical stages. In clinical stage I GGO adenocarcinoma, all regional recurrences appeared as ipsilateral lung metastasis (39.2% [20 of 51]) without regional lymph node metastasis. Brain metastasis was more frequent in patients with clinical stage I solid adenocarcinoma (16.5% [16 of 97 patients]). The presence of GGO was associated with time to recurrence and OS (adjusted hazard ratio [HR], 0.6 [P < .001] for both). Recurrence pattern was an independent risk factor for PRS (adjusted HR, 2.1 for distant metastasis [P < .001] and 3.9 for brain metastasis [P < .001], with local-regional recurrence as the reference). Conclusion Recurrence patterns, time to recurrence, and overall survival differed between patients with and without ground-glass opacity at CT, and recurrence patterns were associated with postrecurrence survival. © RSNA, 2023 Supplemental material is available for this article.

Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning-based Texture Analysis.

Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quantitatively assessed. Purpose To determine the prevalence of ILA at CT examinations from the Korean National Lung Cancer Screening Program and define an optimal lung area threshold for ILA detection with CT with use of deep learning-based texture analysis. Materials and Methods This retrospective study included participants who underwent chest CT between April 2017 and December 2020 at two medical centers participating in the Korean National Lung Cancer Screening Program. CT findings were classified by three radiologists into three groups: no ILA, equivocal ILA, and ILA (fibrotic and nonfibrotic). Progression was evaluated between baseline and last follow-up CT scan. The extent of ILA was assessed visually and quantitatively with use of deep learning-based texture analysis. The Youden index was used to determine an optimal cutoff value for detecting ILA with use of texture analysis. Demographics and ILA subcategories were compared between participants with progressive and nonprogressive ILA. Results A total of 3118 participants were included in this study, and ILAs were observed with the CT scans of 120 individuals (4%). The median extent of ILA calculated by the quantitative system was 5.8% for the ILA group, 0.7% for the equivocal ILA group, and 0.1% for the no ILA group (P < .001). A 1.8% area threshold in a lung zone for quantitative detection of ILA showed 100% sensitivity and 99% specificity. Progression was observed in 48% of visually assessed fibrotic ILAs (15 of 31), and quantitative extent of ILA increased by 3.1% in subjects with progression. Conclusion ILAs were detected in 4% of the Korean lung cancer screening population. Deep learning-based texture analysis showed high sensitivity and specificity for detecting ILA with use of a 1.8% lung area cutoff value. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Egashira and Nishino in this issue.

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data. Materials and Methods A deep learning algorithm was retrospectively developed using baseline and follow-up chest radiographs in adults from January 2011 to December 2018 at a tertiary referral hospital. Two thoracic radiologists reviewed randomly selected pairs of "change" and "no change" images to establish the ground truth, including normal or abnormal status. Algorithm performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis in a validation set and temporally separated internal test sets (January 2019 to August 2021) from the emergency department (ED) and ICU. Threshold calibration for the test sets was conducted, and performance with 40% and 60% triage thresholds was assessed. Results This study included 3 304 996 chest radiographs in 329 036 patients (mean age, 59 years ± 14 [SD]; 170 433 male patients). The training set included 550 779 pairs of radiographs. The validation set included 1620 pairs (810 no change, 810 change). The test sets included 533 pairs (ED; 265 no change, 268 change) and 600 pairs (ICU; 310 no change, 290 change). The algorithm had AUCs of 0.77 (validation), 0.80 (ED), and 0.80 (ICU). With a 40% triage threshold, specificity was 88.4% (237 of 268 pairs) and 90.0% (261 of 290 pairs) in the ED and ICU, respectively. With a 60% triage threshold, specificity was 79.9% (214 of 268 pairs) and 79.3% (230 of 290 pairs) in the ED and ICU, respectively. For urgent findings (consolidation, pleural effusion, pneumothorax), specificity was 78.6%-100% (ED) and 85.5%-93.9% (ICU) with a 40% triage threshold. Conclusion The deep learning algorithm could triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Czum in this issue.

Deep Learning-based Approach to Predict Pulmonary Function at Chest CT.

Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV1) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV1 percent predicted (hereafter, FVC% and FEV1%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV1/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV1. For the prediction of the respiratory high-risk group, FVC%, FEV1%, and FEV1/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.

Development and Verification of a Diagnostic Technology for Waste Battery Deterioration Factors.

We defined four major deterioration factors (electrolyte loss (EL), lithium loss (LL), lithium precipitation (LP), and compound deterioration (CD)). Then, we derived eleven key performance indicators (KPIs) for comparative analysis. After that, we fabricated three deteriorated cells for each of three deterioration factors (EL, LL, and LP) and one cell with CD (for verification) with four individual (dis)charging experiment manuals. The two major contributions of this study are the performance of 1) trend analysis to determine a suitable diagnostic metric by inspecting the eleven KPIs and 2) comparison analysis of V o c v , t ' ' ${{V}_{ocv,t}^{{ {^\prime} {^\prime}}}}$ and V o c v , t , s i m ' ' ${{V}_{ocv,t,sim}^{{ {^\prime} {^\prime}}}}$ to verify the effectiveness of utilizing V o c v , t ' ' ${{V}_{ocv,t}^{{ {^\prime} {^\prime}}}}$ as a real-time deterioration diagnostic factor using a concept of model-in-the-loop simulation. The results show that 1) V o c v , t ' ' ${{V}_{ocv,t}^{{ {^\prime} {^\prime}}}}$ has the most conspicuous trendline tendency among the eleven comparison targets for all four major deterioration factors, and 2) the angle difference between the two trends of V o c v , t ' ' ${{V}_{ocv,t}^{{ {^\prime} {^\prime}}}}$ and V o c v , t , s i m ' ' ${{V}_{ocv,t,sim}^{{ {^\prime} {^\prime}}}}$ lies within a minimum of 9° and a maximum of 43° (with a 10 4 ${{10}^{4}}$ sscale on the x-axis and a 10 - 7 ${{10}^{-7}}$ scale on the y-axis for a clear trend line analysis). From this, we can conclude that the trendline-based real-time deterioration analysis employing V o c v , t ' ' ${{V}_{ocv,t}^{{ {^\prime} {^\prime}}}}$ may be practically applicable to a limited extent.

Automated CT quantification of interstitial lung abnormality and interstitial lung disease according to the Fleischner Society in patients with resectable lung cancer: prognostic significance.

OBJECTIVE: To assess the prognostic significance of automatically quantified interstitial lung abnormality (ILA) according to the definition by the Fleischner Society in patients with resectable non-small-cell lung cancer (NSCLC). METHODS: Patients who underwent lobectomy or pneumonectomy for NSCLC between January 2015 and December 2019 were retrospectively included. Preoperative CT scans were analyzed using the commercially available deep-learning-based automated quantification software for ILA. According to quantified results and the definition by the Fleischner Society and multidisciplinary discussion, patients were divided into normal, ILA, and interstitial lung disease (ILD) groups. RESULTS: Of the 1524 patients, 87 (5.7%) and 20 (1.3%) patients had ILA and ILD, respectively. Both ILA (HR, 1.81; 95% CI: 1.25-2.61; p = .002) and ILD (HR, 5.26; 95% CI: 2.99-9.24; p < .001) groups had poor recurrence-free survival (RFS). Overall survival (OS) decreased (HR 2.13 [95% CI: 1.27-3.58; p = .004] for the ILA group and 7.20 [95% CI: 3.80-13.62, p < .001] for the ILD group) as the disease severity increased. Both quantified fibrotic and non-fibrotic ILA components were associated with poor RFS (HR, 1.57; 95% CI: 1.12-2.21; p = .009; and HR, 1.11; 95% CI: 1.01-1.23; p = .03) and OS (HR, 1.59; 95% CI: 1.06-2.37; p = .02; and HR, 1.17; 95% CI: 1.03-1.33; and p = .01) in normal and ILA groups. CONCLUSIONS: The automated CT quantification of ILA based on the definition by the Fleischner Society predicts outcomes of patients with resectable lung cancer based on the disease category and quantified fibrotic and non-fibrotic ILA components. CLINICAL RELEVANCE STATEMENT: Quantitative CT assessment of ILA provides prognostic information for lung cancer patients after surgery, which can help in considering active surveillance for recurrence, especially in those with a larger extent of quantified ILA. KEY POINTS: • Of the 1524 patients with resectable lung cancer, 1417 (93.0%) patients were categorized as normal, 87 (5.7%) as interstitial lung abnormality (ILA), and 20 (1.3%) as interstitial lung disease (ILD). • Both ILA and ILD groups were associated with poor recurrence-free survival (hazard ratio [HR], 1.81, p = .002; HR, 5.26, p < .001, respectively) and overall survival (HR, 2.13; p = .004; HR, 7.20; p < .001). • Both quantified fibrotic and non-fibrotic ILA components were associated with recurrence-free survival and overall survival in normal and ILA groups.

CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning.

Training deep learning models on medical images heavily depends on experts' expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bias and imbalances. In this paper, chest X-ray pre-trained model via self-supervised contrastive learning (CheSS) was proposed to learn models with various representations in chest radiographs (CXRs). Our contribution is a publicly accessible pretrained model trained with a 4.8-M CXR dataset using self-supervised learning with a contrastive learning and its validation with various kinds of downstream tasks including classification on the 6-class diseases in internal dataset, diseases classification in CheXpert, bone suppression, and nodule generation. When compared to a scratch model, on the 6-class classification test dataset, we achieved 28.5% increase in accuracy. On the CheXpert dataset, we achieved 1.3% increase in mean area under the receiver operating characteristic curve on the full dataset and 11.4% increase only using 1% data in stress test manner. On bone suppression with perceptual loss, we achieved improvement in peak signal to noise ratio from 34.99 to 37.77, structural similarity index measure from 0.976 to 0.977, and root-square-mean error from 4.410 to 3.301 when compared to ImageNet pretrained model. Finally, on nodule generation, we achieved improvement in Fréchet inception distance from 24.06 to 17.07. Our study showed the decent transferability of CheSS weights. CheSS weights can help researchers overcome data imbalance, data shortage, and inaccessibility of medical image datasets. CheSS weight is available at https://github.com/mi2rl/CheSS .

Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT.

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Differences in the prognostic implication of ground-glass opacity on CT according to pathological nodal status in lung cancers treated with lobectomy or pneumonectomy.

OBJECTIVES: To clarify the prognostic significance of a ground-glass opacity (GGO) component according to T category and pathological nodal status in patients with resectable non-small cell lung cancer (NSCLC). METHODS: Patients who underwent lobectomy or pneumonectomy for NSCLC between July 2010 and December 2017 were retrospectively included. Patients were divided into GGO and solid groups based on the presence of a GGO component on CT. The effects on survival of interactions between GGO and (a) pathological nodal status (pN) and (b) cT category were evaluated using Cox regression. RESULTS: Out of 1545 patients, 548 were classified into the GGO group (pN0: 457, pN1/2: 91) and 997 into the solid group (pN0: 660, pN1/2: 337). There were interactions between the presence of GGO and pathological nodal status on 5-year disease-free survival (DFS; p = .006) and 5-year overall survival (OS; p = .02). In multivariate analysis, better survival of patients in the GGO group than in the solid group was observed only in pN0 category (adjusted hazard ratio [HR], 0.63 for 5-year DFS; p = .002 and 0.47 for 5-year OS; p = .002), but not in pN1/2 category. Moreover, in those with pN0 category, the favorable prognostic value of GGO was limited to those with cT1 category for 5-year DFS (adjusted HR, 0.48; p < .001) and those with cT1/2 category for 5-year OS (adjusted HR, 0.37; p = .002). CONCLUSIONS: GGO was a favorable predictor of survival only in patients with pN0 category, showing an advantage in DFS for those with cT1 category and OS for those with cT1/2 category. KEY POINTS: • The presence of ground-glass opacity was associated with a favorable prognosis, only in pathological node-negative patients (5-year disease-free survival, p = .002; 5-year overall survival, p = .002). • Within pathological node-negative patients, the effect of ground-glass opacity on 5-year disease-free survival was valid in patients with cT1 category (adjusted hazard ratio, 0.48; 95% confidence interval, 0.32-0.72; p < .001), but not in patients with cT2 or above category. • Within pathological node-negative patients, the effect of ground-glass opacity on 5-year overall survival was valid in patients with cT1/2 category (adjusted hazard ratio, 0.37; 95% confidence interval, 0.20-0.68; p = .002), but not in patients with cT3/4 category.

Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement.

OBJECTIVES: To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization. METHODS: Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics. RESULTS: The five readers reported 139-151 negative screening results without CAD and 126-142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (p < 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004). CONCLUSIONS: Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases. KEY POINTS: • Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65. • The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%). • In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).

Identification of predictors for brain metastasis in newly diagnosed non-small cell lung cancer: a single-center cohort study.

OBJECTIVES: To identify clinical and staging chest CT characteristics predictive of brain metastasis in patients with newly diagnosed NSCLC dichotomized according to resectability. METHODS: Patients newly diagnosed with NSCLC of clinical stages II-IV between November 2017 and October 2018 were enrolled and classified into resectable (stage II+IIIA) and unresectable stages (stage IIIB/C+IV) according to chest CT. Associations of clinicopathological characteristics and CT findings with brain metastasis were analyzed using logistic regression. Predictive models were evaluated using receiver operating characteristics curve analysis. A subgroup analysis for unresectable-stage patients with known epidermal growth factor receptor gene (EGFR) mutation status was performed. RESULTS: This study included 911 NSCLC patients (mean age, 65 ± 11 years; 620 men), 194 of whom were diagnosed with brain metastasis. For resectable stages, independent predictors for brain metastasis were N2-stage (13 of 25 patients), absence of air-bronchogram/bubble lucency (23 of 25 patients), and presence of spiculation (15 of 25 patients), with a model combining the two imaging features showing an AUC of 0.723. In unresectable stages, independent predictors of brain metastasis were younger age, female sex, extrathoracic metastasis, and adenocarcinoma, with models combining these showing AUCs of 0.675-0.766. In the subgroup with known EGFR-mutation status, extrathoracic metastasis and positive EGFR mutation were independent predictors of brain metastasis, with the model showing AUCs of 0.641-0.732. CONCLUSION: CT-derived imaging features, clinical stages, lung cancer subtype, and EGFR mutation were associated with brain metastasis in patients with newly diagnosed NSCLC. The predictors were completely different between resectable and unresectable stages. KEY POINTS: • In resectable stages of NSCLC, two imaging features (absence of air-bronchogram/bubble lucency and presence of spiculation) and N2 stage were independent predictors of brain metastasis. • In unresectable stages of NSCLC, younger age, female sex, extrathoracic metastasis, and adenocarcinoma were associated with brain metastasis. • In the subgroup of NSCLC with known EGFR-mutation status, extrathoracic metastasis and positive EGFR mutation were independent predictors of brain metastasis.

Air embolism in CT-guided transthoracic needle biopsy: emphasis on pulmonary vein injury.

OBJECTIVE: To assess whether pulmonary vein injury is detectable on CT and associated with air embolism after percutaneous transthoracic needle biopsy (PTNB) in a tertiary referral hospital. METHODS: Between January 2012 and November 2021, 11,691 consecutive CT-guided PTNBs in 10,685 patients were retrospectively evaluated. Air embolism was identified by reviewing radiologic reports. Pulmonary vein injury was defined as the presence of the pulmonary vein in the needle pathway or shooting range of the cutting needle with the presence of parenchymal hemorrhage. The association between pulmonary vein injury and air embolism was assessed using logistic regression analysis in matched patients with and without air embolism with a ratio of 1:4. RESULTS: A total of 27 cases of air embolism (median age, 67 years; range, 48-80 years; 24 men) were found with an incidence of 0.23% (27/11,691). Pulmonary vein injury during the procedures was identifiable on CT in 24 of 27 patients (88.9%), whereas it was 1.9% (2/108) for matched patients without air embolism The veins beyond the target lesion (70.8% [17/24]) were injured more frequently than the veins in the needle pathway before the target lesion (29.2% [7/24]). In univariable and multivariable analyses, pulmonary vein injury was associated with air embolism (odds ratio, 485.19; 95% confidence interval, 68.67-3428.19, p <.001). CONCLUSION: Pulmonary vein injury was detected on CT and was associated with air embolism. Avoiding pulmonary vein injury with careful planning of the needle pathway on CT may reduce air embolism risk. KEY POINTS: • Pulmonary vein injury during CT-guided biopsy was identifiable on CT in most of the patients (88.9% [24/27]). • The veins beyond the target lesion (70.8% [17/24]) were injured more frequently than the veins in the needle pathway before the target lesion (29.2% [7/24]). • Avoiding the distinguishable pulmonary vein along the pathway or shooting range of the needle on CT may reduce the air embolism risk.

Utility of a Deep Learning Algorithm for Detection of Reticular Opacity on Chest Radiography in Patients With Interstitial Lung Disease.

BACKGROUND. Deep learning has been heavily explored for pulmonary nodule detection on chest radiographs. Detection of reticular opacity in interstitial lung disease (ILD) is challenging and may also benefit from a deep learning algorithm (DLA). OBJECTIVE. The purpose of this study was to evaluate the utility of a DLA for detection of reticular opacity on chest radiographs of patients with surgically confirmed ILD. METHODS. This retrospective study included 197 patients (130 men, 67 women; mean age, 62.6 ± 7.6 [SD] years) with surgically proven ILD between January 2017 and December 2018 who underwent preoperative chest radiography and chest CT within a 30-day interval. A total of 197 age- and sex-matched control patients with normal chest radiographs were randomly selected. A commercially available DLA was used to detect lower lobe or subpleural abnormalities; those matching the reticular opacity location on CT were deemed true-positive. Six readers (three thoracic radiologists, three residents) independently reviewed radiographs with and without the DLA for the presence of reticular opacity. Interobserver agreement was assessed. Diagnostic performance was compared among interpretations. Subanalysis was performed according to CT-based classification of the severity of reticular opacity. Performance of the DLA was also assessed on 102 chest radiographs from a second institution (51 patients with ILD, 51 matched patients in the control group). RESULTS. Interobserver agreement was moderate (κ = 0.517) for readers alone and almost perfect (κ = 0.870) for readers using the DLA. Sensitivity, specificity, and accuracy of the DLA for reticular opacity were 98.0%, 99.0%, and 98.5%; of pooled readers alone were 77.3%, 92.3%, and 84.8%; and of readers using the DLA were 93.8%, 97.3%, and 95.6%. All metrics were significantly better (all p ≤ .002) for the DLA and for readers using the DLA than for readers alone. Sensitivity for readers without and with the DLA were 66.7% and 86.8% for mild disease, 84.2% and 98.8% for moderate disease, and 87.3% and 100.0% for severe disease. The DLA had 100.0% accuracy at the second institution. CONCLUSION. The DLA outperformed readers in detection of reticular opacity, and use of the DLA improved reader performance and interobserver agreement. The benefit of the DLA was more notable in sensitivity than in specificity and was maintained in mild disease. CLINICAL IMPACT. Use of the DLA may facilitate detection of reticular opacity on chest radiographs in the early stages of ILD.

Learning Curve for CT-Guided Percutaneous Transthoracic Needle Biopsy: Retrospective Evaluation Among 17 Thoracic Imaging Fellows at a Tertiary Referral Hospital.

BACKGROUND. CT-guided percutaneous transthoracic needle biopsy (PTNB) is widely used for evaluation of indeterminate pulmonary lesions, although guidelines are lacking regarding the experience needed to gain sufficient skill. OBJECTIVE. The purpose of our study was to investigate the learning curve among a large number of operators in a tertiary referral hospital and to determine the number of procedures required to obtain acceptable performance. METHODS. This retrospective study included CT-guided PTNBs with coaxial technique performed by 17 thoracic imaging fellows from March 2, 2011, to August 8, 2017, who were novices in the procedure. A maximum number of 200 consecutive procedures per operator were included. The cumulative summation method was used to assess learning curves for diagnostic accuracy, false-negative rate, pneumothorax rate, and hemoptysis rate. Operators were assessed individually and in a pooled analysis. Pneumothorax risk was also assessed in a model adjusting for risk factors. Acceptable failure rates were defined as 0.1 for diagnostic accuracy and false-negative rate, 0.45 for pneumothorax rate, and 0.05 for hemoptysis rate. RESULTS. The study included 3261 procedures in 3134 patients (1876 men, 1258 women; mean age, 67.7 ± 12.1 [SD] years). Overall diagnostic accuracy was 94.2% (2960/3141). All 17 operators achieved acceptable diagnostic accuracy (37 procedures required in the pooled analysis; median, 33 procedures required [range, 19-67 procedures required]). Overall false-negative rate was 7.6% (179/2370). All 17 operators achieved acceptable false-negative rate (52 procedures required in the pooled analysis; median, 33 procedures required [range, 19-95 procedures required]). Pneumothorax occurred in 32.6% of the procedures (1063/3261 procedures), and hemoptysis occurred in 2.7% of the procedures (89/3261 procedures). All 17 operators achieved acceptable pneumothorax rate (20 procedures required in the pooled analysis; median, 19 procedures required [range, 7-63 procedures required]). In the risk-adjusted model, 15 operators achieved acceptable pneumothorax rate (54 procedures required in the pooled analysis; median, 36 procedures required [range, 10-192 procedures required]). Sixteen operators achieved acceptable hemoptysis rate (67 procedures required in the pooled analysis; median, 55 procedures required [range, 41-152 procedures required]). CONCLUSION. For CT-guided PTNB, at least 37 and 52 procedures are required to achieve acceptable diagnostic accuracy and false-negative rate, respectively. Not all operators achieved acceptable complication rates. CLINICAL IMPACT. The findings may help set standards for training, supervision, and ongoing assessment of operator proficiency for this procedure.

Volume Doubling Times of Pulmonary Metastases in Patients With Bone and Soft-Tissue Sarcomas: Associations With Subsequent New Metastases and Survival After Metastasectomy.

BACKGROUND. Pulmonary metastases of bone and soft-tissue sarcoma are common and have a high recurrence rate after metastasectomy. Factors associated with postmetastasectomy recurrence are not well studied. OBJECTIVE. The purpose of this study was to investigate the association of the volume doubling time (VDT) of pulmonary metastases with the subsequent development of new pulmonary nodules and survival after metastasectomy in patients with bone or soft-tissue sarcoma. METHODS. This retrospective study included patients with bone or soft-tissue sarcoma who, between January 2010 and December 2020, underwent first complete metastasectomy of pulmonary nodules visualized on two sequential preoperative CT scans. Semiautomatic volumetric segmentation of the pulmonary metastases was performed on the two CT scans, and VDTs were calculated. VDT was compared between patients with and without subsequent new metastases after metastasectomy. Cox proportional hazards regression analyses were performed to determine risk factors for recurrence-free survival (RFS) after metastasectomy and for postmetastasectomy overall survival (OS). RESULTS. Forty patients (21 women, 19 men; mean age, 51.1 ± 14.3 [SD] years) were included. Of these patients, 23 (57.5%) developed new metastatic nodules after metastasectomy, and 10 (25.0%) died during follow-up. Median VDT was shorter in patients with, versus those without, new metastases after metastasectomy (56 vs 140 days, p = .002). Only four of 23 patients with new metastases had VDT of 140 days or more. In multivariable analysis, older age (hazard ratio [HR], 1.06; p = .004), female sex (HR, 2.80; p = .03), and VDT less than 140 days (HR, 4.22; p = .01) were independent predictors of worse RFS. Also in multivariable analysis, only older age (HR, 1.17; p = .005) and VDT less than 50 days (HR, 8.60; p = .02) were independent predictors of worse OS. OS was significantly worse in patients with VDT less than 140 days (10 deaths among 27 patients) than in patients with VDT of 140 days or more (no deaths in 13 patients) (p = .01). CONCLUSION. In patients with bone and soft-tissue sarcoma, shorter VDT of pulmonary metastases is independently associated with subsequent new metastases after metastasectomy and worse OS. CLINICAL IMPACT. VDT of pulmonary nodules may be considered in patient selection for pulmonary metastasectomy and in postoperative patient management.

Artificial Intelligence in Lung Imaging.

Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.