Effect of smoking on the diagnostic results and complication rates of percutaneous transthoracic needle biopsy.

OBJECTIVE: To investigate the association of smoking with the outcomes of percutaneous transthoracic needle biopsy (PTNB). METHODS: In total, 4668 PTNBs for pulmonary lesions were retrospectively identified. The associations of smoking status (never, former, current smokers) and smoking intensity (≤ 20, 21-40, > 40 pack-years) with diagnostic results (malignancy, non-diagnostic pathologies, and false-negative results in non-diagnostic pathologies) and complications (pneumothorax and hemoptysis) were assessed using multivariable logistic regression analysis. RESULTS: Among the 4668 PTNBs (median age of the patients, 66 years [interquartile range, 58-74]; 2715 men), malignancies, non-diagnostic pathologies, and specific benign pathologies were identified in 3054 (65.4%), 1282 (27.5%), and 332 PTNBs (7.1%), respectively. False-negative results for malignancy occurred in 20.5% (236/1153) of non-diagnostic pathologies with decidable reference standards. Current smoking was associated with malignancy (adjusted odds ratio [OR], 1.31; 95% confidence interval [CI]: 1.02-1.69; p = 0.03) and false-negative results (OR, 2.64; 95% CI: 1.32-5.28; p = 0.006), while heavy smoking (> 40 pack-years) was associated with non-diagnostic pathologies (OR, 1.69; 95% CI: 1.19-2.40; p = 0.003) and false-negative results (OR, 2.12; 95% CI: 1.17-3.92; p = 0.02). Pneumothorax and hemoptysis occurred in 21.8% (1018/4668) and 10.6% (495/4668) of PTNBs, respectively. Heavy smoking was associated with pneumothorax (OR, 1.33; 95% CI: 1.01-1.74; p = 0.04), while heavy smoking (OR, 0.64; 95% CI: 0.40-0.99; p = 0.048) and current smoking (OR, 0.64; 95% CI: 0.42-0.96; p = 0.04) were inversely associated with hemoptysis. CONCLUSION: Smoking history was associated with the outcomes of PTNBs. Current and heavy smoking increased false-negative results and changed the complication rates of PTNBs. CLINICAL RELEVANCE STATEMENT: Smoking status and intensity were independently associated with the outcomes of PTNBs. Non-diagnostic pathologies should be interpreted cautiously in current or heavy smokers. A patient's smoking history should be ascertained before PTNB to predict and manage complications. KEY POINTS: • Smoking status and intensity might independently contribute to the diagnostic results and complications of PTNBs. • Current and heavy smoking (> 40 pack-years) were independently associated with the outcomes of PTNBs. • Operators need to recognize the association between smoking history and the outcomes of PTNBs.

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

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

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

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.

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

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.

All-solid-state Z-scheme ZnFe-LDH/rGO/g-C3N5 heterojunction for enhanced sonophotocatalytic degradation of ciprofloxacin: Performance and mechanistic insights.

The fabrication of all-solid-state Z-scheme sonophotocatalysts is vital for improving the transfer rate of photogenerated electrons to remove antibiotics present in wastewater. Herein, a novel indirect Z-scheme ZnFe-layered double hydroxide (LDH)/reduced graphene oxide (rGO)/graphitic carbon nitride (g-C3N5) heterojunction was synthesized using a simple strategy. The ZnFe-LDH/rGO/g-C3N5 (ZF@rGCN) ternary composites were systematically characterized using different techniques. Results revealed that the 15%ZF@rGCN catalyst achieved a ciprofloxacin (CIP) degradation efficiency of 95% via the synergistic effect of sonocatalysis and photocatalysis. The improved sonophotocatalytic performance of the ZF@rGCN heterojunction was attributed to an increase in the number of active sites, a Z-scheme charge-transfer channel in ZF@rGCN, and an extended visible light response range. The introduction of rGO further enhanced the charge-transfer rate and preserved the reductive and oxidative sites of the ZF@rGCN system, thereby affording additional reactive species to participate in CIP removal. In addition, owing to its unique properties, rGO possibly increased the absorption of incident light and served as an electronic bridge in the as-formed ZF@rGCN catalyst. Finally, the possible CIP degradation pathways and the sonophotocatalytic Z-scheme charge-migration route of ZF@rGCN were proposed. This study presents a new approach for fabricating highly efficient Z-scheme sonophotocatalysts for environmental remediation.

Removal of endocrine disruptors and pharmaceuticals by graphene oxide-based membranes in water: A review.

Membrane-based water treatment has emerged as a promising solution to address global water challenges. Graphene oxide (GO) has been successfully employed in membrane filtration processes owing to its reversible properties, large-scale production potential, layer-to-layer stacking, great oxygen-based functional groups, and unique physicochemical characteristics, including the creation of nano-channels. This review evaluates the separation performance of various GO-based membranes, manufactured by coating or interfacial polymerization with different support layers such as polymer, metal, and ceramic, for endocrine-disrupting compounds (EDCs) and pharmaceutically active compounds (PhACs). In most studies, the addition of GO significantly improved the removal efficiency, flux, porosity, hydrophilicity, stability, mechanical strength, and antifouling performance compared to pristine membranes. The key mechanisms involved in contaminant removal included size exclusion, electrostatic exclusion, and adsorption. These mechanisms could be ascribed to the physicochemical properties of compounds, such as molecular size and shape, hydrophilicity, and charge state. Therefore, understanding the removal mechanisms based on compound characteristics and appropriately adjusting the operational conditions are crucial keys to membrane separation. Future research directions should explore the characteristics of the combination of GO derivatives with various support layers, by tailoring diverse operating conditions and compounds for effective removal of EDCs and PhACs. This is expected to accelerate the development of surface modification strategies for enhanced contaminant removal.

Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest Radiographs.

Background The factors affecting radiologists' diagnostic determinations in artificial intelligence (AI)-assisted image reading remain underexplored. Purpose To assess how AI diagnostic performance and reader characteristics influence detection of malignant lung nodules during AI-assisted reading of chest radiographs. Materials and Methods This retrospective study consisted of two reading sessions from April 2021 to June 2021. Based on the first session without AI assistance, 30 readers were assigned into two groups with equivalent areas under the free-response receiver operating characteristic curve (AUFROCs). In the second session, each group reinterpreted radiographs assisted by either a high or low accuracy AI model (blinded to the fact that two different AI models were used). Reader performance for detecting lung cancer and reader susceptibility (changing the original reading following the AI suggestion) were compared. A generalized linear mixed model was used to identify the factors influencing AI-assisted detection performance, including readers' attitudes and experiences of AI and Grit score. Results Of the 120 chest radiographs assessed, 60 were obtained in patients with lung cancer (mean age, 67 years ± 12 [SD]; 32 male; 63 cancers) and 60 in controls (mean age, 67 years ± 12; 36 male). Readers included 20 thoracic radiologists (5-18 years of experience) and 10 radiology residents (2-3 years of experience). Use of the high accuracy AI model improved readers' detection performance to a greater extent than use of the low accuracy AI model (area under the receiver operating characteristic curve, 0.77 to 0.82 vs 0.75 to 0.75; AUFROC, 0.71 to 0.79 vs 0.7 to 0.72). Readers who used the high accuracy AI showed a higher susceptibility (67%, 224 of 334 cases) to changing their diagnosis based on the AI suggestions than those using the low accuracy AI (59%, 229 of 386 cases). Accurate readings at the first session, correct AI suggestions, high accuracy Al, and diagnostic difficulty were associated with accurate AI-assisted readings, but readers' characteristics were not. Conclusion An AI model with high diagnostic accuracy led to improved performance of radiologists in detecting lung cancer on chest radiographs and increased radiologists' susceptibility to AI suggestions. © RSNA, 2023 Supplemental material is available for this article.

AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial.

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.

A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation.

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.

Fabrication of copper molybdate nanoflower combined polymeric graphitic carbon nitride heterojunction for water depollution: Synergistic photocatalytic performance and mechanism insight.

In the scope, developed a novel copper molybdate decorated polymeric graphitic carbon nitride (CuMoO4@g-C3N4 or CMC) heterojunction nanocomposite in an easy solvothermal environment for the first time. The synthesized CMC improved the photocatalytic degradation of an antibiotic drug [ciprofloxacin (CIP)] and organic dye [Rhodamine B (RhB)]. Consequently, the CMC demonstrates a marvelous crystalline nature with ∼26 nm size, as obtained from XRD analysis. Besides, the surface morphology studies confirm the large-scale construction of flower-like CMC with a typical size of 10-15 nm. The CMC showed efficient catalytic activity for both the pollutants, achieving the degradation of 98% for RhB and 97% for CIP in 35 and 60 min, respectively. The reaction parameters including the concentration of pollutants, catalyst dosages, and scavengers are optimized for the best photocatalytic results. Notably, the trapping tests showed that the •OH and O2•- radicals are the primary oxidative species liable for the photocatalytic process. The recyclability test of the photocatalyst infers that the photocatalyst is highly stable up to the fifth recycle. Our work affords an efficient and ideal path to constructing the new g-C3N4-based architected photocatalyst for toxic wastewater treatment in the near future.

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE: We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS: This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS: The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS: The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Erratum: Correction of Affiliations in the Article "Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019".

This corrects the article on p. e413 in vol. 35, PMID: 33258333.

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation.

Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothorax. Purpose To investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice. Materials and Methods A CAD system for post-PTNB pneumothorax detection on chest radiographs was implemented in an institution in February 2020. This retrospective cohort study consecutively included chest radiographs interpreted with CAD assistance (CAD-applied group; February 2020 to November 2020) and those interpreted before implementation (non-CAD group; January 2018 to January 2020). The reference standard was defined by consensus reading by two radiologists. The diagnostic accuracy for pneumothorax was compared between the two groups using generalized estimating equations. Matching was performed according to whether the radiograph reader and PTNB operator were the same using the greedy method. Results A total of 676 radiographs from 655 patients (mean age: 67 years ± 11; 390 men) in the CAD-applied group and 676 radiographs from 664 patients (mean age: 66 years ± 12; 400 men) in the non-CAD group were included. The incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group (P = .05). The CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group (all P < .001). The sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of <10%: 74.5% vs 51.4%, P = .009; pneumothorax of 10%-15%: 92.7% vs 70.2%, P = .008). Among patients with pneumothorax, 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group (P = .009) required subsequent drainage catheter insertion. Conclusion A deep learning-based computer-aided detection system improved the detection performance for pneumothorax on chest radiographs after lung biopsy. © RSNA, 2022 See also the editorial by Schiebler and Hartung in this issue.

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations.

Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to the 2021 U.S. Preventive Services Task Force (USPSTF) recommendations in a health check-up sample. Materials and Methods This single-center retrospective study included consecutive current and former smokers aged 50-80 years who underwent chest radiography during a health check-up between January 2004 and June 2018. Discrimination performance, including receiver operating characteristic curve analysis and area under the receiver operating characteristic curve (AUC) calculation, of the model for incident lung cancers was evaluated. The added value of the model to the 2021 USPSTF recommendations was investigated for lung cancer inclusion rate, proportion of selected CT screening candidates, and positive predictive value (PPV). Results For model validation, a total of 19 488 individuals (mean age, 58 years ± 6 [SD]; 18 467 [95%] men) and the subset of USPSTF-eligible individuals (n = 7835; mean age, 57 years ± 6; 7699 [98%] men) were assessed, and the AUCs for incident lung cancers were 0.68 (95% CI: 0.62, 0.73) and 0.75 (95% CI: 0.68, 0.81), respectively. In individuals with pack-year information (n = 17 390), when excluding low- and indeterminate-risk categories from the USPSTF-eligible sample, the proportion of selected CT screening candidates was reduced to 35.8% (6233 of 17 390) from 45.1% (7835 of 17 390, P < .001), with three missed lung cancers (0.2%). The cancer inclusion rate (0.3% [53 of 17 390] vs 0.3% [56 of 17 390], P = .85) and PPV (0.9% [53 of 6233] vs 0.7% [56 of 7835], P = .42) remained unaffected. Conclusion An externally validated deep learning model showed the added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening in reducing the number of screening candidates while maintaining the inclusion rate and positive predictive value for incident lung cancer. © RSNA, 2022 Online supplemental material is available for this article.

No Prognostic Impact of Staging Brain MRI in Patients with Stage IA Non-Small Cell Lung Cancer.

Background Although various guidelines discourage performing brain MRI for staging purposes in asymptomatic patients with clinical stage IA non-small cell lung cancer (NSCLC), evidence regarding their postoperative survival is lacking. Purpose To investigate the survival benefit of performing brain MRI in asymptomatic patients with early-stage NSCLC. Materials and Methods Patients who underwent curative resection between February 2009 and March 2016 for clinical TNM stage T1N0M0 NSCLC were retrospectively included. Patient survival and development of brain metastasis during postoperative surveillance were documented. The cumulative survival rate and incidence of brain metastasis were compared between patients who underwent surgery with or without staging brain MRI by using Cox regression and a Fine-Gray subdistribution hazard model, respectively, for multivariable adjustment. Propensity score matching and inverse probability of treatment weighting were applied for confounder adjustment. Results A total of 628 patients (mean age, 64 years ± 10 [SD]; 319 men) were included, of whom 53% (331 of 628) underwent staging brain MRI. In the multivariable analyses, brain MRI did not show prognostic benefits for brain metastasis-free survival (hazard ratio [HR], 1.06; 95% CI: 0.69, 1.63; P = .79), time to brain metastasis (HR, 1.60; 95% CI: 0.70, 3.94; P = .29), and overall survival (HR, 0.86; 95% CI, 0.54, 1.37; P = .54). Consistent results were obtained after propensity score matching (brain metastasis-free survival [HR, 0.97; 95% CI: 0.60, 1.57; P = .91], time to brain metastasis [HR, 1.29; 95% CI: 0.50, 3.33; P = .60], and overall survival [HR, 0.89; 95% CI: 0.53, 1.51; P = .67]) and inverse probability of treatment weighting. Conclusion No difference was observed between asymptomatic patients with clinical stage IA non-small cell lung cancer who underwent staging brain MRI and those who did not in terms of brain metastasis-free survival, time to brain metastasis, and overall survival. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Bizzi and Pascuzzo in this issue.

Deep Learning Prediction of Survival in Patients with Chronic Obstructive Pulmonary Disease Using Chest Radiographs.

Background Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose To develop and to validate a deep learning-based survival prediction model in patients with COPD (DLSP) using chest radiographs, in addition to other clinical factors. Materials and Methods In this retrospective study, data from patients with COPD who underwent postbronchodilator spirometry and chest radiography from 2011-2015 were collected and split into training (n = 3475), validation (n = 435), and internal test (n = 315) data sets. The algorithm for predicting survival from chest radiographs was trained (hereafter, DLSPCXR), and then age, body mass index, and forced expiratory volume in 1 second (FEV1) were integrated within the model (hereafter, DLSPinteg). For external test, three independent cohorts were collected (n = 394, 416, and 337). The discrimination performance of DLSPCXR was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) at 5-year survival. Goodness of fit was assessed by using the Hosmer-Lemeshow test. Using one external test data set, DLSPinteg was compared with four COPD-specific clinical indexes: BODE, ADO, COPD Assessment Test (CAT), and St George's Respiratory Questionnaire (SGRQ). Results DLSPCXR had a higher performance at predicting 5-year survival than FEV1 in two of the three external test cohorts (TD AUC: 0.73 vs 0.63 [P = .004]; 0.67 vs 0.60 [P = .01]; 0.76 vs 0.77 [P = .91]). DLSPCXR demonstrated good calibration in all cohorts. The DLSPinteg model showed no differences in TD AUC compared with BODE (0.87 vs 0.80; P = .34), ADO (0.86 vs 0.89; P = .51), and SGRQ (0.86 vs 0.70; P = .09), and showed higher TD AUC than CAT (0.93 vs 0.55; P < .001). Conclusion A deep learning model using chest radiographs was capable of predicting survival in patients with chronic obstructive pulmonary disease. © RSNA, 2022 Online supplemental material is available for this article.

Percutaneous transthoracic catheter drainage for lung abscess: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the outcomes of patients receiving image-guided percutaneous catheter drainage (PCD) for lung abscesses in terms of treatment success, major complications, and mortality as well as the predictors of those outcomes. METHODS: Embase and OVID-MEDLINE databases were searched to identify studies on lung abscesses treated with PCD that had extractable outcomes. The outcomes were pooled using a random-intercept logistic regression model. Multivariate Firth's bias-reduced penalised-likelihood logistic regression analyses were performed to identify predictors of treatment success and complications. Methodological quality was assessed by summing scores of binary responses to items regarding selection, ascertainment of exposure and outcome, causality of follow-up duration, and reporting. RESULTS: From 26 studies with acceptable methodological quality (median score, 4; range, 3-5), 194 patients were included. The pooled rates of treatment success and major complications were 86.5% (95% confidence interval [CI], 78.5-91.8%; I2 = 23%) and 8.1% (95% CI, 4.1-15.3%; I2 = 26%), respectively. Four patients eventually died from uncontrolled lung abscesses (pooled rate, 1.5%; 95% CI, 0.2-11.1%; I2 = 36%). Malignancy-related abscess (odds ratio [OR], 0.129; 95% CI, 0.024-0.724; p = .022) and the occurrence of a major complication (OR, 0.065; 95% CI, 0.02-0.193; p < .001) were significant predictors of treatment failure. Traversing normal lung parenchyma was the only significant risk factor for major complications (OR, 27.69; 95% CI, 7.196-123.603; p < .001). CONCLUSION: PCD under imaging guidance was effective for lung abscess treatment, with a low complication rate. Traversal of normal lung parenchyma was the sole risk factor for complications, and malignancy-related abscesses and the occurrence of major complications were predictors of treatment failure. KEY POINTS: • The pooled treatment success rate of PCD for lung abscess was reasonably high (86.5%); malignancy-related abscesses and the occurrence of a major complication were predictors of treatment failure. • The pooled rate of percutaneous transthoracic catheter drainage-related major complications was 8.1% and traversing normal lung parenchyma by the catheter was the only risk factor. • The pooled mortality rate from uncontrolled lung abscesses with percutaneous transthoracic catheter drainage was low.

Preoperative percutaneous needle lung biopsy techniques and ipsilateral pleural recurrence in stage I lung cancer.

OBJECTIVES: A recent meta-analysis of individual patient data revealed that preoperative percutaneous transthoracic needle lung biopsy (PTNB) was associated with an increased risk of ipsilateral pleural recurrence in stage I lung cancer. This study aimed to examine whether particular PTNB techniques reduced the risk of pleural recurrence. METHODS: We retrospectively included 415 consecutive patients with stage I lung cancer who underwent preoperative PTNB and curative resection from 2009 through 2016. Detailed information was collected, including clinical, PTNB technique, radiologic, and pathologic characteristics of lung cancer. Cox regression analyses were performed to identify risk factors for pleural recurrence before and after propensity score matching. RESULTS: The overall follow-up period after PTNB was 62.1 ± 23.0 months, and ipsilateral pleural recurrence occurred in 40 patients. Before propensity score matching, age (p = 0.063), microscopic pleural invasion (p = 0.065), and pathologic tumor size (p = 0.016) tended to be associated with pleural recurrence in univariate analyses and subsequently were matched using a propensity score. After propensity score matching, multivariate analysis revealed that ipsilateral pleural recurrence was associated with a larger target size on computed tomography (hazard ratio [HR] = 1.498; 95% CI, 1.506-2.125; p = 0.023) and microscopic lymphatic invasion (HR = 3.526; 95% CI, 1.491-8.341; p = 0.004). However, no PTNB techniques such as needle gauge, biopsy, or pleural passage numbers were associated with a reduced risk of recurrence. CONCLUSIONS: No particular PTNB techniques were associated with reduced pleural seeding after PTNB in stage I lung cancer. Regardless of the technique, PTNB needs to be cautiously applied when early lung cancer is suspected, followed by curative treatment. KEY POINTS: • Age, microscopic pleural invasion, and pathologic tumor size tended to be associated with pleural recurrence in stage I lung cancer before propensity matching. • After propensity matching, pre-biopsy CT target size and microscopic lymphatic invasion were associated with pleural recurrence. • No particular PTNB techniques were associated with reduced pleural seeding in stage I lung cancer before and after propensity matching.