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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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Purpose@#Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. @*Materials and Methods@#We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%–96%) and specificity of 37% (95% CI: 26%–50%), and applied to the early outbreak in Wuhan, New York, and Italy. @*Results@#From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standarddose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2–6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710–56755) to 44840 (TPR, 38%; 95% CI: 35161–68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. @*Conclusion@#Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.
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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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Interstitial lung abnormalities (ILAs) are radiologic abnormalities found incidentally on chest CT that are potentially related to interstitial lung diseases. Several articles have reported that ILAs are associated with increased mortality, and they can show radiologic progression. With the increased recognition of ILAs on CT, the role of radiologists in reporting them is critical. This review aims to discuss the clinical significance and radiologic characteristics of ILAs to facilitate and enhance their management.
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Objective@#This study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification. @*Materials and Methods@#Among the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed. @*Results@#The median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/ micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma. @*Conclusion@#The tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.
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Objective@#We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. @*Materials and Methods@#Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31–89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. @*Results@#The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model).The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). @*Conclusion@#The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.
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Objective@#We conducted a systematic review and meta-analysis of the tissue adequacy and complication rates of percutaneous transthoracic needle biopsy (PTNB) for molecular analysis in patients with non-small cell lung cancer (NSCLC). @*Materials and Methods@#We performed a literature search of the OVID-MEDLINE and Embase databases to identify original studies on the tissue adequacy and complication rates of PTNB for molecular analysis in patients with NSCLC published between January 2005 and January 2020. Inverse variance and random-effects models were used to evaluate and acquire meta-analytic estimates of the outcomes. To explore heterogeneity across the studies, univariable and multivariable metaregression analyses were performed. @*Results@#A total of 21 studies with 2232 biopsies (initial biopsy, 8 studies; rebiopsy after therapy, 13 studies) were included.The pooled rates of tissue adequacy and complications were 89.3% (95% confidence interval [CI]: 85.6%–92.6%; I2 = 0.81) and 17.3% (95% CI: 12.1%–23.1%; I2 = 0.89), respectively. These rates were 93.5% and 22.2% for the initial biopsies and 86.2% and 16.8% for the rebiopsies, respectively. Severe complications, including pneumothorax requiring chest tube placement and massive hemoptysis, occurred in 0.7% of the cases (95% CI: 0%–2.2%; I2 = 0.67). Multivariable meta-regression analysis showed that the tissue adequacy rate was not significantly lower in studies on rebiopsies (p = 0.058). The complication rate was significantly higher in studies that preferentially included older adults (p = 0.001). @*Conclusion@#PTNB demonstrated an average tissue adequacy rate of 89.3% for molecular analysis in patients with NSCLC, with a complication rate of 17.3%. PTNB is a generally safe and effective diagnostic procedure for obtaining tissue samples for molecular analysis in NSCLC. Rebiopsy may be performed actively with an acceptable risk of complications if clinically required.
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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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Coronavirus disease (COVID-19) has threatened public health as a global pandemic. Chest CT and radiography are crucial in managing COVID-19 in addition to reverse transcription-polymerase chain reaction, which is the gold standard for COVID-19 diagnosis. This is a review of the current status of the use of chest CT and radiography in COVID-19 diagnosis and management and anㄷ introduction of early representative studies on the application of artificial intelligence to chest CT and radiography. The authors also share their experiences to provide insights into the future value of artificial intelligence.
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Coronavirus disease (COVID-19) has threatened public health as a global pandemic. Chest CT and radiography are crucial in managing COVID-19 in addition to reverse transcription-polymerase chain reaction, which is the gold standard for COVID-19 diagnosis. This is a review of the current status of the use of chest CT and radiography in COVID-19 diagnosis and management and anㄷ introduction of early representative studies on the application of artificial intelligence to chest CT and radiography. The authors also share their experiences to provide insights into the future value of artificial intelligence.
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A low-dose chest CT is performed for early detection of lung cancer, but the CT scan frequently shows several incidental abnormalities. Identification of the incidental findings may enable early detection of diseases other than lung cancer, thereby improving the survival of the individual undergoing screening. However, insignificant incidental abnormalities may cause unnecessary additional examination and costs. It is crucial for radiologists to appropriately comprehend and report significant incidental abnormalities other than lung cancer for successful implementation of the national lung cancer screening program in Korea.
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Lung cancer is the leading cause of death from cancer worldwide. The most effective way to reduce lung cancer mortality is early detection and treatment. Two large randomized controlled trials (RCTs), the National Lung Screening Trial and the Dutch-Belgian Randomized Lung Cancer Screening Trial, showed that low-dose CT (LDCT) can reduce the chances of lung cancer death. This paper reviews the two aforementioned RCTs and the current situations of implementing LDCT screening in several counties. Although nationwide programs of lung cancer screening are rare, they would increase in the near future. Using the two aforementioned RCTs and accumulating data from many counties, including the East Asian countries, a more effective way of LDCT screening in Korea can be devised and implemented.
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Lung cancer screening in high-risk subjects using low-dose CT can reduce mortality by 20%. Current evidence suggests that the development of a risk prediction model for lung cancer is one of the major advances in lung cancer screening. Herein, we review the technical requirements for evaluating different risk prediction models. Moreover, we describe the major lung cancer risk prediction models reported, and the results of lung cancer screening using these models.
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PURPOSE: To reduce lung cancer mortality, lung cancer screening was recommended using low-dose computed tomography (LDCT) to high-risk population. A protocol for multicenter lung cancer screening pilot project was developed to evaluate the effectiveness and feasibility of lung cancer screening to implement National Cancer Screening Program in Korea. MATERIALS AND METHODS: Multidisciplinary expert committee was comprised to develop a standardized protocol for Korean Lung Cancer Screening Project (K-LUCAS). K-LUCAS is a population-based single arm trial that targets high-risk population aged 55-74 years with at least 30 pack-year smoking history. LDCT results are reported by Lung-RADS suggested by American Radiology Society. Network-based system using computer-aided detection program is prepared to assist reducing diagnostic errors. Smoking cessation counselling is provided to all currently smoking participants. A small pilot test was conducted to check the feasibility and compliance of the protocols for K-LUCAS. RESULTS: In pilot test, 256 were participated. The average age of participants was 63.2 years and only three participants (1.2%) were female. The participants had a smoking history of 40.5 pack-year on average and 53.9% were current smokers. Among them, 86.3% had willing to participate in lung cancer screening again. The average willingness to quit smoking among current smokers was 12.7% higher than before screening. In Lung-RADS reports, 10 (3.9%) were grade 3 and nine (3.5%) were grade 4. One participant was diagnosed as lung cancer. CONCLUSION: The protocol developed by this study is assessed to be feasible to perform K-LUCAS in multicenter nationwide scale.
Subject(s)
Female , Humans , Arm , Compliance , Diagnostic Errors , Early Detection of Cancer , Korea , Lung Neoplasms , Lung , Mass Screening , Mortality , Pilot Projects , Smoke , Smoking , Smoking CessationABSTRACT
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
OBJECTIVE: To evaluate quantitative magnetic resonance imaging (MRI) parameters for differentiation of cysts from and solid masses in the anterior mediastinum. MATERIALS AND METHODS: The development dataset included 18 patients from two institutions with pathologically-proven cysts (n = 6) and solid masses (n = 12) in the anterior mediastinum. We measured the maximum diameter, normalized T1 and T2 signal intensity (nT1 and nT2), normalized apparent diffusion coefficient (nADC), and relative enhancement ratio (RER) of each lesion. RERs were obtained by non-rigid registration and subtraction of precontrast and postcontrast T1-weighted images. Differentiation criteria between cysts and solid masses were identified based on receiver operating characteristics analysis. For validation, two separate datasets were utilized: 15 patients with 8 cysts and 7 solid masses from another institution (validation dataset 1); and 11 patients with clinically diagnosed cysts stable for more than two years (validation dataset 2). Sensitivity and specificity were calculated from the validation datasets. RESULTS: nT2, nADC, and RER significantly differed between cysts and solid masses (p = 0.032, 0.013, and 0.63; nT2 > 0.39. In validation dataset 1, the sensitivity of the RER, nADC, and nT2 criteria was 87.5%, 100%, and 75.0%, and the specificity was 100%, 40.0%, and 57.4%, respectively. In validation dataset 2, the sensitivity of the RER, nADC, and nT2 criteria was 90.9%, 90.9%, and 72.7%, respectively. CONCLUSION: Quantitative MRI criteria using nT2, nADC, and particularly RER can assist differentiation of cysts from solid masses in the anterior mediastinum.
Subject(s)
Humans , Dataset , Diffusion , Magnetic Resonance Imaging , Mediastinal Cyst , Mediastinum , ROC Curve , Sensitivity and Specificity , ThymomaABSTRACT
OBJECTIVE: To investigate whether computed tomography (CT) and fluorine-18-labeled fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) may be applied to distinguish thymic epithelial tumors (TETs) from benign cysts in the anterior mediastinum. MATERIALS AND METHODS: We included 262 consecutive patients with pathologically proven TETs and benign cysts 5 cm or smaller who underwent preoperative CT scans. In addition to conventional morphological and ancillary CT findings, the relationship between the lesion and the adjacent mediastinal pleura was evaluated qualitatively and quantitatively. Mean lesion attenuation was measured on CT images. The maximum standardized uptake value (SUVmax) was obtained with FDG-PET scans in 40 patients. CT predictors for TETs were identified with multivariate logistic regression analysis. For validation, we assessed the diagnostic accuracy and inter-observer agreement between four radiologists in a size-matched set of 24 cysts and 24 TETs using a receiver operating characteristic curve before and after being informed of the study findings. RESULTS: The multivariate analysis showed that post-contrast attenuation of 60 Hounsfield unit or higher (odds ratio [OR], 12.734; 95% confidence interval [CI], 2.506–64.705; p = 0.002) and the presence of protrusion from the mediastinal pleura (OR, 9.855; 95% CI, 1.749–55.535; p = 0.009) were the strongest CT predictors for TETs. SUVmax was significantly higher in TETs than in cysts (5.3 ± 2.4 vs. 1.1 ± 0.3; p < 0.001). After being informed of the study findings, the readers' area under the curve improved from 0.872–0.955 to 0.949–0.999 (p = 0.066–0.149). Inter-observer kappa values for protrusion were 0.630–0.941. CONCLUSION: Post-contrast CT attenuation, protrusion from the mediastinal pleura, and SUVmax were useful imaging features for distinguishing TETs from cysts in the anterior mediastinum.
Subject(s)
Humans , Logistic Models , Mediastinum , Multivariate Analysis , Pleura , Positron-Emission Tomography , ROC Curve , Thymus Neoplasms , Tomography, X-Ray ComputedABSTRACT
OBJECTIVE: The purposes of this study were to evaluate size-specific dose estimate (SSDE) of low-dose CT (LDCT) in the Korean Lung Cancer Screening (K-LUCAS) project and to determine whether CT protocols from Western countries are appropriate for lung cancer screening in Korea. MATERIALS AND METHODS: For participants (n = 256, four institutions) of K-LUCAS pilot study, volume CT dose index (CTDI(vol)) using a 32-cm diameter reference phantom was compared with SSDE, which was recalculated from CTDI(vol) using size-dependent conversion factor (f-size) based on the body size, as described in the American Association of Physicists in Medicine Report 204. This comparison was subsequently assessed by body mass index (BMI) levels (underweight/normal vs. overweight/obese), and automatic exposure control (AEC) adaptation (yes/no). RESULTS: Size-specific dose estimate was higher than CTDI(vol) (2.22 ± 0.75 mGy vs. 1.67 ± 0.60 mGy, p < 0.001), since the f-size was larger than 1.0 for all participants. The ratio of SSDE to CTDI(vol) was higher in lower BMI groups; 1.26, 1.37, 1.43, and 1.53 in the obese (n = 103), overweight (n = 70), normal (n = 75), and underweight (n = 4), respectively. The ratio of SSDE to CTDI(vol) was greater in standard-sized participants than in large-sized participants independent of AEC adaptation; with AEC, SSDE/CTDI(vol) in large- vs. standard-sized participants: 1.30 ± 0.08 vs. 1.44 ± 0.08 (p < 0.001) and without AEC, 1.32 ± 0.08 vs. 1.42 ± 0.06 (p < 0.001). CONCLUSION: Volume CT dose index based on a reference phantom underestimates radiation exposure of LDCT in standard-sized Korean participants. The optimal radiation dose limit needs to be verified for standard-sized Korean participants.