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Purpose@#The purpose of this study was to determine whether deoxyhemoglobin changes were associated with admission duration in carbon monoxide (CO)-poisoned patients. @*Methods@#This retrospective study included 181 patients who were able to breathe by themselves after CO poisoning. Arterial blood gas analysis was performed to measure their deoxyhemoglobin levels. Their baseline characteristics and clinical outcomes during hospitalization in the emergency department (ED) were collected and compared. To assess changes in deoxyhemoglobin levels, blood samples were taken immediately after patients presented to the ED and then again after 6 hours. For statistical analysis, logistic regression was utilized to determine the effect of deoxyhemoglobin changes on admission duration. @*Results@#The incidence rates of hypocapnia and hypoxemia at presentation after acute CO poisoning were 28.7% and 43.6%, respectively. Moreover, the magnitude of increasing deoxyhemoglobin levels in patients with hypoxemia (2.1 [1.7–3.1], p<0.001) and changes in deoxyhemoglobin levels appeared to have an impact on the length of hospitalization in the ED (odds ratio, 1.722; 95% confidence interval, 0.547–0.952; p<0.001). @*Conclusion@#In patients with acute CO poisoning, deoxyhemoglobin levels appeared to increase in those with hypoxemia, which in turn was associated with prolonged hospitalization.
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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 AICAD 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.
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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.
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Objective@#Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists’ diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. @*Materials and Methods@#This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expertdetermined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. @*Results@#The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expertdetermined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). @*Conclusion@#The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.
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Interest in health insurance coverage for artificial intelligence (AI)–based medical technologies is growing. This article provides a review of the current developments in the sphere and provides future perspectives, focusing on AI application in radiology.Current Concepts: In December 2019, the Health Insurance Review and Assessment Service under the Korean Ministry of Health and Welfare released its first guidelines for determining the National Health Insurance coverage for AI–based medical technologies. Additionally, in 2020, the largest US health insurance provider, the Centers for Medicare and Medicaid Services, approved payment for AI technologies using two different systems. First, in September 2020, it granted New Technology Add-on Payments for AI algorithms that facilitate the diagnosis and treatment of large vessel occlusion strokes. Second, in December 2020, the Centers for Medicare and Medicaid Services finalized the provision of reimbursements for IDx-DR through a Current Procedural Terminology code. The AI system screens for more than mild diabetic retinopathy, which requires further evaluation by an ophthalmologist.Discussion and Conclusion: An in-depth look at the three events suggests the importance of demonstrating the added clinical value of AI technologies through improved patient outcomes in enabling insurance coverage. Therefore, it is critical to create clinically meaningful collaboration between healthcare professionals and AI by understanding and combining their unique strengths, thus actualizing new forms of patient care instead of having AI merely copy the professionals. Furthermore, if National Health Insurance coverage is granted for AI technologies in radiology, add-on payments would be the most appropriate method.
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Objective@#To investigate the diagnostic accuracy and complications of cone-beam CT-guided percutaneous transthoracic needle biopsy (PTNB) of juxtaphrenic lesions and identify the risk factors for diagnostic failure and complications. @*Materials and Methods@#In total, 336 PTNB procedures for lung lesions (mean size ± standard deviation [SD], 4.3 ± 2.3 cm) abutting the diaphragm in 326 patients (189 male and 137 female; mean age ± SD, 65.2 ± 11.4 years) performed between January 2010 and December 2014 were included. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the PTNB procedures for the diagnosis of malignancy were measured based on the intentionto-diagnose principle. The risk factors for diagnostic failures and complications were evaluated using logistic regression analysis. @*Results@#The accuracy, sensitivity, specificity, PPV, and NPV were 92.7% (293/316), 91.3% (219/240), 91.4% (74/81), 96.9% (219/226), and 77.9% (74/95), respectively. There were 23 diagnostic failures (7.3%), and lesion sizes ≤ 2 cm (p = 0.045) were the only significant risk factors for diagnostic failure. Complications occurred in 98 cases (29.2%), including 89 cases of pneumothorax (26.5%) and 7 cases of hemoptysis (2.1%). The multivariable analysis showed that old age (> 65 years) (p = 0.002), lesion size of ≤ 2 cm (p = 0.003), emphysema (p = 0.006), and distance from the pleura to the target lesion (> 2 cm) (p = 0.010) were significant risk factors for complications. @*Conclusion@#The diagnostic accuracy of cone-beam CT-guided PTNB of juxtaphrenic lesions for malignancy was fairly high, and the target lesion size was the only significant predictor of diagnostic failure. Complications of cone-beam CT-guided PTNB of juxtaphrenic lesions occurred at a reasonable rate.
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Objective@#To investigate the diagnostic accuracy and complications of cone-beam CT-guided percutaneous transthoracic needle biopsy (PTNB) of juxtaphrenic lesions and identify the risk factors for diagnostic failure and complications. @*Materials and Methods@#In total, 336 PTNB procedures for lung lesions (mean size ± standard deviation [SD], 4.3 ± 2.3 cm) abutting the diaphragm in 326 patients (189 male and 137 female; mean age ± SD, 65.2 ± 11.4 years) performed between January 2010 and December 2014 were included. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the PTNB procedures for the diagnosis of malignancy were measured based on the intentionto-diagnose principle. The risk factors for diagnostic failures and complications were evaluated using logistic regression analysis. @*Results@#The accuracy, sensitivity, specificity, PPV, and NPV were 92.7% (293/316), 91.3% (219/240), 91.4% (74/81), 96.9% (219/226), and 77.9% (74/95), respectively. There were 23 diagnostic failures (7.3%), and lesion sizes ≤ 2 cm (p = 0.045) were the only significant risk factors for diagnostic failure. Complications occurred in 98 cases (29.2%), including 89 cases of pneumothorax (26.5%) and 7 cases of hemoptysis (2.1%). The multivariable analysis showed that old age (> 65 years) (p = 0.002), lesion size of ≤ 2 cm (p = 0.003), emphysema (p = 0.006), and distance from the pleura to the target lesion (> 2 cm) (p = 0.010) were significant risk factors for complications. @*Conclusion@#The diagnostic accuracy of cone-beam CT-guided PTNB of juxtaphrenic lesions for malignancy was fairly high, and the target lesion size was the only significant predictor of diagnostic failure. Complications of cone-beam CT-guided PTNB of juxtaphrenic lesions occurred at a reasonable rate.
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Percutaneous transthoracic needle biopsy (PTNB) is one of the essential diagnostic procedures for pulmonary lesions. Its role is increasing in the era of CT screening for lung cancer and precision medicine. The Korean Society of Thoracic Radiology developed the first evidence-based clinical guideline for PTNB in Korea by adapting pre-existing guidelines. The guideline provides 39 recommendations for the following four main domains of 12 key questions: the indications for PTNB, pre-procedural evaluation, procedural technique of PTNB and its accuracy, and management of post-biopsy complications. We hope that these recommendations can improve the diagnostic accuracy and safety of PTNB in clinical practice and promote standardization of the procedure nationwide.
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Objective@#This study examined the clinical manifestations, treatment, and prognostic factors of hydrogen sulfide intoxication. @*Methods@#Twelve cases of hydrogen sulfide leaking from a wastewater treatment company in Sasang-gu, Busan were reviewed. The demographic characteristics, initial symptoms, treatment, complications, and long-term prognosis were reviewed. The Life Science Ethics Review Committee approved this study. @*Results@#The majority of the 12 cases were male (83%) with an average age of 38 years. Three of the 12 cases, who had been exposed to hydrogen sulfide, died (25%), and four had poor outcomes (33%). The incidence of pulmonary edema was significantly higher in the poor prognosis group, but the incidence of conjunctivitis and pre-hospital cardiac arrest was similar. The lactic acid concentration in the poor prognosis group was higher than the good prognosis group. In the poor prognosis group, the Glasgow coma scale was lower than that in the good prognosis group. @*Conclusion@#A poor outcome occurred in 33% of the 12 people exposed to hydrogen sulfide in Busan 2018. In the poor outcome group, the initial Glasgow Coma Scale was lower, the pulmonary edema rate and the initial serum lactate level were higher than in the good outcome group.
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Objective@#To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. @*Materials and Methods@#In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. @*Results@#Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). @*Conclusion@#Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.
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Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under activeinvestigation with deep learning technology, which has shown promising performance in various tasks, including detection,classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential forclinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facingseveral challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, andincomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deeplearning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.
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In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.
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Background@#The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19. @*Methods@#The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital. @*Results@#The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11–97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit.Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs. @*Conclusion@#The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.
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OBJECTIVE: This study presents a preliminary report on the chest radiographic and computed tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) pneumonia in Korea.MATERIALS AND METHODS: As part of a multi-institutional collaboration coordinated by the Korean Society of Thoracic Radiology, we collected nine patients with COVID-19 infections who had undergone chest radiography and CT scans. We analyzed the radiographic and CT findings of COVID-19 pneumonia at baseline. Fisher's exact test was used to compare CT findings depending on the shape of pulmonary lesions.RESULTS: Three of the nine patients (33.3%) had parenchymal abnormalities detected by chest radiography, and most of the abnormalities were peripheral consolidations. Chest CT images showed bilateral involvement in eight of the nine patients, and a unilobar reversed halo sign in the other patient. In total, 77 pulmonary lesions were found, including patchy lesions (39%), large confluent lesions (13%), and small nodular lesions (48%). The peripheral and posterior lung fields were involved in 78% and 67% of the lesions, respectively. The lesions were typically ill-defined and were composed of mixed ground-glass opacities and consolidation or pure ground-glass opacities. Patchy to confluent lesions were primarily distributed in the lower lobes (p = 0.040) and along the pleura (p < 0.001), whereas nodular lesions were primarily distributed along the bronchovascular bundles (p = 0.006).CONCLUSION: COVID-19 pneumonia in Korea primarily manifested as pure to mixed ground-glass opacities with a patchy to confluent or nodular shape in the bilateral peripheral posterior lungs. A considerable proportion of patients with COVID-19 pneumonia had normal chest radiographs.
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OBJECTIVE: This study presents a preliminary report on the chest radiographic and computed tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) pneumonia in Korea.MATERIALS AND METHODS: As part of a multi-institutional collaboration coordinated by the Korean Society of Thoracic Radiology, we collected nine patients with COVID-19 infections who had undergone chest radiography and CT scans. We analyzed the radiographic and CT findings of COVID-19 pneumonia at baseline. Fisher's exact test was used to compare CT findings depending on the shape of pulmonary lesions.RESULTS: Three of the nine patients (33.3%) had parenchymal abnormalities detected by chest radiography, and most of the abnormalities were peripheral consolidations. Chest CT images showed bilateral involvement in eight of the nine patients, and a unilobar reversed halo sign in the other patient. In total, 77 pulmonary lesions were found, including patchy lesions (39%), large confluent lesions (13%), and small nodular lesions (48%). The peripheral and posterior lung fields were involved in 78% and 67% of the lesions, respectively. The lesions were typically ill-defined and were composed of mixed ground-glass opacities and consolidation or pure ground-glass opacities. Patchy to confluent lesions were primarily distributed in the lower lobes (p = 0.040) and along the pleura (p < 0.001), whereas nodular lesions were primarily distributed along the bronchovascular bundles (p = 0.006).CONCLUSION: COVID-19 pneumonia in Korea primarily manifested as pure to mixed ground-glass opacities with a patchy to confluent or nodular shape in the bilateral peripheral posterior lungs. A considerable proportion of patients with COVID-19 pneumonia had normal chest radiographs.
Subject(s)
Humans , Cooperative Behavior , Coronavirus , Korea , Lung , Pleura , Pneumonia , Radiography , Radiography, Thoracic , Thorax , Tomography, X-Ray ComputedABSTRACT
Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However, several challenges must be overcome, to ensure the implementation of lung cancer screening, which include a large number of expected low-dose CT examinations and relative shortage of experienced radiologists for interpreting them. The use of artificial intelligence has garnered attention in this regard. A deep learning technique, which is a subclass of machine learning methods, involving the learning of data representations in an end-to-end manner, has already demonstrated outstanding performance in medical image analysis. Several studies are exploring the possibility of deep learning-based applications in medical domains, including radiology. In lung cancer screening, computer-aided detection, report generation, prediction of malignancy in the detected nodules, and prognosis prediction can be considered for the application of artificial intelligence. This article will cover the current status of deep learning approaches, their limitations, and their potential in lung cancer screening programs.
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OBJECTIVE: To evaluate the learning curve for C-arm cone-beam computed tomography (CBCT) virtual navigation-guided percutaneous transthoracic needle biopsy (PTNB) and to determine the amount of experience needed to develop appropriate skills for this procedure using cumulative summation (CUSUM). MATERIALS AND METHODS: We retrospectively reviewed 2042 CBCT virtual navigation-guided PTNBs performed by 7 novice operators between March 2011 and December 2014. Learning curves for CBCT virtual navigation-guided PTNB with respect to its diagnostic performance and the occurrence of biopsy-related pneumothorax were analyzed using standard and risk-adjusted CUSUM (RA-CUSUM). Acceptable failure rates were determined as 0.06 for diagnostic failure and 0.25 for PTNB-related pneumothorax. RESULTS: Standard CUSUM indicated that 6 of the 7 operators achieved an acceptable diagnostic failure rate after a median of 105 PTNB procedures (95% confidence interval [CI], 14–240), and 6 of the operators achieved acceptable pneumothorax occurrence rate after a median of 79 PTNB procedures (95% CI, 27–155). RA-CUSUM showed that 93 (95% CI, 39–142) and 80 (95% CI, 38–127) PTNB procedures were required to achieve acceptable diagnostic performance and pneumothorax occurrence, respectively. CONCLUSION: The novice operators' skills in performing CBCT virtual navigation-guided PTNBs improved with increasing experience over a wide range of learning periods.
Subject(s)
Biopsy, Needle , Cone-Beam Computed Tomography , Learning Curve , Learning , Lung , Needles , Pneumothorax , Retrospective StudiesABSTRACT
On page 323, the grant number was incorrectly numbered as HI15C1234. The correct number is HI15C3390.
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OBJECTIVE: To analyze the complications of percutaneous transthoracic needle biopsy using CT-based imaging modalities for needle guidance in comparison with fluoroscopy in a large retrospective cohort. MATERIALS AND METHODS: This study was approved by multiple Institutional Review Boards and the requirement for informed consent was waived. We retrospectively included 10568 biopsies from eight referral hospitals from 2010 through 2014. In univariate and multivariate logistic analyses, 3 CT-based guidance modalities (CT, CT fluoroscopy, and cone-beam CT) were compared with fluoroscopy in terms of the risk of pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis, with adjustment for other risk factors. RESULTS: Pneumothorax occurred in 2298 of the 10568 biopsies (21.7%). Tube insertion was required after 316 biopsies (3.0%), and hemoptysis occurred in 550 cases (5.2%). In the multivariate analysis, pneumothorax was more frequently detected with CT {odds ratio (OR), 2.752 (95% confidence interval [CI], 2.325–3.258), p < 0.001}, CT fluoroscopy (OR, 1.440 [95% CI, 1.176–1.762], p < 0.001), and cone-beam CT (OR, 2.906 [95% CI, 2.235–3.779], p < 0.001), but no significant relationship was found for pneumothorax requiring chest tube insertion (p = 0.497, p = 0.222, and p = 0.216, respectively). The incidence of hemoptysis was significantly lower under CT (OR, 0.348 [95% CI, 0.247–0.491], p < 0.001), CT fluoroscopy (OR, 0.594 [95% CI, 0.419–0.843], p = 0.004), and cone-beam CT (OR, 0.479 [95% CI, 0.317–0.724], p < 0.001) guidance. CONCLUSION: Hemoptysis occurred less frequently with CT-based guidance modalities in comparison with fluoroscopy. Although pneumothorax requiring chest tube insertion showed a similar incidence, pneumothorax was more frequently detected using CT-based guidance modalities.
Subject(s)
Biopsy , Biopsy, Needle , Chest Tubes , Cohort Studies , Cone-Beam Computed Tomography , Ethics Committees, Research , Fluoroscopy , Hemoptysis , Image-Guided Biopsy , Incidence , Informed Consent , Lung Neoplasms , Multivariate Analysis , Needles , Pneumothorax , Referral and Consultation , Retrospective Studies , Risk FactorsABSTRACT
OBJECTIVE: To measure the diagnostic accuracy of percutaneous transthoracic needle lung biopsies (PTNBs) on the basis of the intention-to-diagnose principle and identify risk factors for diagnostic failure of PTNBs in a multi-institutional setting. MATERIALS AND METHODS: A total of 9384 initial PTNBs performed in 9239 patients (mean patient age, 65 years [range, 20–99 years]) from January 2010 to December 2014 were included. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PTNBs for diagnosis of malignancy were measured. The proportion of diagnostic failures was measured, and their risk factors were identified. RESULTS: The overall accuracy, sensitivity, specificity, PPV, and NPV were 91.1% (95% confidence interval [CI], 90.6–91.7%), 92.5% (95% CI, 91.9–93.1%), 86.5% (95% CI, 85.0–87.9%), 99.2% (95% CI, 99.0–99.4%), and 84.3% (95% CI, 82.7–85.8%), respectively. The proportion of diagnostic failures was 8.9% (831 of 9384; 95% CI, 8.3–9.4%). The independent risk factors for diagnostic failures were lesions ≤ 1 cm in size (adjusted odds ratio [AOR], 1.86; 95% CI, 1.23–2.81), lesion size 1.1–2 cm (1.75; 1.45–2.11), subsolid lesions (1.81; 1.32–2.49), use of fine needle aspiration only (2.43; 1.80–3.28), final diagnosis of benign lesions (2.18; 1.84–2.58), and final diagnosis of lymphomas (10.66; 6.21–18.30). Use of cone-beam CT (AOR, 0.31; 95% CI, 0.13–0.75) and conventional CT-guidance (0.55; 0.32–0.94) reduced diagnostic failures. CONCLUSION: The accuracy of PTNB for diagnosis of malignancy was fairly high in our large-scale multi-institutional cohort. The identified risk factors for diagnostic failure may help reduce diagnostic failure and interpret the biopsy results.