Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.

PURPOSE: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging.

Background: The reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers. Methods: Eighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment. Results: The average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 vs. 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values. Conclusions: The DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.

Clinical implication of the 2020 International Association for the Study of Lung Cancer histologic grading in surgically resected pathologic stage 1 lung adenocarcinomas: Prognostic value and association with computed tomography characteristics.

OBJECTIVES: To investigate the incremental prognostic value of the 2020 International Association for the Study of Lung Cancer (IASLC) histologic grading system over traditional prognosticators in surgically resected pathologic stage 1 lung adenocarcinomas and to identify the clinical and radiologic characteristics of lung adenocarcinomas reclassified by the 2020 histologic grading system. MATERIALS AND METHODS: We retrospectively enrolled 356 patients who underwent surgery for pathologic stage 1 adenocarcinoma between January 2016 and December 2017. The histologic grading was classified according to the predominant histologic subtype (conventional system) and the updated 2020 IASLC grading system. The clinical and computed tomography (CT) characteristics were compared according to the reclassification of the updated system. The performance of prognostic models for recurrence-free survival based on the combination of pathologic tumor size, histologic grade, and CT-based information was compared using the c-index. RESULTS: Postoperative recurrence occurred in 6.7% of patients during the follow-up period (mean, 1589.2 ± 406.7 days). Fifty-nine of 244 (24.2%) tumors with intermediate grades in the conventional system were reclassified as grade 3 with the updated grading system. They showed significantly larger solid proportions and higher percentages of pure solid nodules on CT compared to tumors without reclassification (n = 185) (P < 0.05). Prognostic prediction models based on pathology tumor size and histologic grades had significantly higher c-indices (0.754-0.803) compared to the model based on pathologic tumor size only (c-index:0.723, P < 0.05). CONCLUSION: The 2020 IASLC histologic grading system has significant incremental prognostic value over the pathologic stage in surgically resected pathologic stage 1 lung adenocarcinoma. Reclassified lung adenocarcinomas using the updated grading system have a larger solid proportion and a higher percentage of pure solid nodules on CT.

Influence of computed tomography slice thickness on deep learning-based, automatic coronary artery calcium scoring software performance.

Background: The influence of computed tomography (CT) slice thickness on the accuracy of deep learning (DL)-based, automatic coronary artery calcium (CAC) scoring software has not been explored yet. Methods: This retrospective study included 844 subjects (477 men, mean age of 58.9±10.7 years) who underwent electrocardiogram (ECG)-gated CAC scoring CT scans with 1.5 and 3 mm slice thickness values between September 2013 and October 2020. Automatic CAC scoring was performed using DL-based software (3D patch-based U-Net architectures). Manual CAC scoring was set as the reference standard. The reliability of automatic CAC scoring was evaluated using intraclass correlation coefficients (ICCs) for both the 1.5 and 3 mm datasets. The agreement of CAC severity categories [Agatston score (AS) 0, 1-100, 101-400, >400] between automatic CAC scoring and the reference standard was analyzed using weighted kappa (κ) statistics for both 1.5 and 3 mm datasets. Results: The CAC scoring agreement between the automatic CAC scoring and reference standard was excellent (ICC 0.982 for 1.5 mm, 0.969 for 3 mm, respectively). The categorical agreement of CAC severity between two methods was excellent for both 1.5 and 3 mm scans, with better agreement for 3 mm scans (weighted κ: 0.851 and 0.961, 95% confidence intervals: 0.823-0.879 and 0.945-0.974, respectively). Conclusions: Automatic CAC scoring shows excellent agreement with the reference standard for both 1.5 and 3 mm scans but results in lower agreement in the CAC severity category for 1.5 mm scans.

Patterns of Postoperative Changes in Lung Volume and Perfusion Assessed by Dual-Energy CT: Comparison of Lobectomy and Limited Resection.

BACKGROUND. Pulmonary function tests (PFTs) and perfusion scintigraphy have limited utility for evaluating postoperative changes in regional pulmonary function after lung cancer resection surgery. OBJECTIVE. The purpose of this study is to compare postoperative changes in lung volume and perfusion, as assessed by dual-energy CT (DECT), between patients undergoing surgical resection of lung cancer by lobectomy versus limited resection as well as to assess associations between such changes and the lobar location of the resected tumor. METHODS. This study entailed a retrospective post hoc analysis of a prospective study that enrolled patients awaiting lung cancer resection surgery between March 2019 and February 2020. Eighty-one patients (38 men and 43 women; mean age, 60.5 ± 8.9 [SD] years), 43 of whom underwent lobectomy and 38 of whom underwent limited resection, were included. Patients underwent thoracic DECT and PFT evaluation preoperatively and at 6 months postoperatively. Pulmonary lobes were segmented. Lobar lung volume and lung perfusion ratios (both relative to whole-lung values) were computed. Perfusion measures reflected DECT-derived iodine content. Patients completed 6-month postoperative quality-of-life (QOL) questionnaires. RESULTS. Patients undergoing lobectomy, compared with those undergoing limited resection, had greater increases in the lung volume ratio of the ipsilateral nonresected lobe(s) (mean, 42.3% ± 24.2% [SD] vs 22.9% ± 13.2%, p < .001) and the contralateral lung (mean, 14.6% ± 14.0% vs 6.4% ± 6.9%, p = .002) as well as greater increases in the lung perfusion ratio of the ipsilateral nonresected lobe(s) (mean, 39.9% ± 20.7% [SD] vs 22.8% ± 17.8%, p < .001) and the contralateral lung (mean, 20.9% ± 9.4% vs 4.3% ± 5.6%, p < .001). In patients with right lower lobe tumors, the largest postoperative increases in the lung volume ratio were in the right middle lobe in those undergoing lobectomy (mean, 44.1% ± 21.0%) and limited resection (mean, 24.6% ± 14.5%), whereas the largest postoperative increase in the lung perfusion ratio was in the left lower lobe in those undergoing lobectomy (mean, 53.9% ± 8.6%) and in the right middle lobe in those undergoing limited resection (mean, 32.5% ± 24.1%). Otherwise, the largest increases in lung volume and perfusion ratios occurred in the ipsilateral nonresected lobes (vs the contra-lateral lobes), regardless of the operative approach used and the lobar location. Changes in the lung volume and perfusion ratios in the ipsilateral lobe(s) and the contralateral lung showed weak correlations with certain QOL scores (e.g., for role functioning: ρ = 0.234-0.279 [volume] and -0.233 to -0.284 [perfusion]). CONCLUSION. DECT depicts patterns of lung volume and perfusion changes after lung cancer surgery, depending on the surgical approach (lobectomy vs limited resection) used and the lobar location of the tumor. CLINICAL IMPACT. DECT-derived metrics can help understand variable physiologic impacts of lung cancer resection surgeries.

Importance of Local Ablative Therapies for Lung Metastases in Patients With Colorectal Cancer.

OBJECTIVE: To assess the effect of local ablative therapy (LAT) on overall survival in patients with lung metastases from colorectal cancer (CRC) compared with patients treated with systemic therapy. SUMMARY BACKGROUND DATA: CRC affects approximately 1.4 million individuals worldwide every year. The lungs are commonly affected by CRC, and there is no treatment standard for a secondary lung metastasis from CRC. METHODS: This longitudinal, retrospective cohort study (2010-2018) quantified the pulmonary and extrapulmonary tumor burden of 1143 patients by retrospectively reviewing computed tomography images captured at diagnosis. A comprehensive multidisciplinary approach informed how and when surgery and/or stereotactic body radiotherapy was administered. RESULTS: Among 1143 patients, 473 patients (41%) received LAT, with surgery first (n = 421) or stereotactic ablative radiation therapy first (n = 52) either at the time of diagnosis (n = 288), within 1 year (n = 132), or after 1 year (n = 53). LAT was repeated in 158 patients (33.4%, 384 total sessions) when new lung metastases were detected. The 5- and 10-year survival rates for patients treated with LAT (71.2% and 64.0%, respectively) were significantly higher than those of patients treated with systemic therapy alone (14.2% and 10.0%, respectively; P <0.001). The overall survival of patients who received LAT intervention increased as the total tumor burden decreased. CONCLUSIONS: A high long-term survival rate was achievable in a significant portion of patients with lung metastasis from CRC by the timely administrations of LAT to standard systemic therapy. The tumor burden and LAT feasibility should be included in a discussion during the follow-up period.

Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm.

OBJECTIVE: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. MATERIALS AND METHODS: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. RESULTS: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). CONCLUSION: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

Hepatocyte-Specific Deficiency of DAX-1 Protects Mice from Acetaminophen-Induced Hepatotoxicity by Activating NRF2 Signaling.

Acetaminophen (APAP) is a widely used analgesic and antipyretic drug, but its overdose can cause acute liver failure. The dosage-sensitive sex reversal adrenal hypoplasia congenita critical region on the X chromosome, gene 1 (DAX-1, NR0B1), is an orphan nuclear receptor that acts as a transcriptional co-repressor of various genes. In this study, we identified the role of DAX-1 in APAP-induced liver injury using hepatocyte-specific Dax-1 knockout (Dax-1 LKO) mice. Mouse primary hepatocytes were used as a comparative in vitro study. APAP overdose led to decreased plasma alanine aminotransferase and aspartate aminotransferase levels in Dax-1 LKO mice compared to C57BL/6J (WT) controls, accompanied by reduced liver necrosis. The expression of the genes encoding the enzymes catalyzing glutathione (GSH) synthesis and metabolism and antioxidant enzymes was increased in the livers of APAP-treated Dax-1 LKO mice. The rapid recovery of GSH levels in the mitochondrial fraction of APAP-treated Dax-1 LKO mice led to reduced reactive oxygen species levels, resulting in the inhibition of the prolonged JNK activation. The hepatocyte-specific DAX-1 deficiency increased the protein expression of nuclear factor erythroid 2-related factor 2 (Nrf2) compared with WT controls after APAP administration. These results indicate that DAX-1 deficiency in hepatocytes protects against APAP-induced liver injury by Nrf2-regulated antioxidant defense.

Automated Computer-Aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease.

PURPOSE: This study aimed to explore the possibility and clinical utility of existing artificial intelligence (AI)-based computer-aided detection (CAD) of lung nodules to identify pulmonary oligometastases. PATIENTS AND METHODS: The chest computed tomography (CT) scans of patients with lung metastasis from colorectal cancer between March 2006 and November 2018 were analyzed. The patients were selected from a database of 1395 patients and studied in 2 cohorts. The first cohort included 50 patients, and the CT scans of these patients were independently evaluated for lung-nodule (≥3 mm) detection by a CAD-assisted radiation oncologist (CAD-RO) as well as by an expert radiologist. Interobserver variability by 2 additional radiation oncologists and 2 thoracic surgeons were also measured. In the second cohort of 305 patients, survival outcomes were evaluated based on the number of CAD-RO-detected nodules. RESULTS: In the first cohort, the sensitivity and specificity of the CAD-RO for identifying oligometastatic disease (OMD) from varying criteria by ≤2 nodules, ≤3 nodules, ≤4 nodules, and ≤5 nodules were 71.9% and 88.9%, 82.9% and 93.3%, 97.1% and 73.3%, and 97.5% and 90.0%, respectively. The sensitivity of the CAD-RO in the nodule detection compared with the radiologist was 81.6%. The average (standard deviation) sensitivity in interobserver variability analysis was 80.0% (3.7%). In the second cohort, the 5-year survival rates of patients with 1, 2, 3, 4, or ≥5 metastatic nodules were 75.2%, 52.9%, 45.7%, 29.1%, and 22.7%, respectively. CONCLUSIONS: Proper identification of the pulmonary OMD and the correlation between the number of CAD-RO-detected nodules and survival suggest the potential practicality of AI in OMD recognition. Developing a deep learning-based model specific to the metastatic setting, which enables a quick estimation of disease burden and identification of OMD, is underway.

Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study.

BACKGROUND: Adjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease. METHODS: We prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT. RESULTS: While the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively. CONCLUSION: Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.

Assessment of aortic valve area on cardiac computed tomography in symptomatic bicuspid aortic stenosis: Utility and differences from Doppler echocardiography.

Background: In this study, we investigate the utility of geometric orifice area (GOA) on cardiac computed tomography (CT) and differences from effective orifice area (EOA) on Doppler echocardiography in patients with bicuspid aortic stenosis (AS). Methods: A total of 163 patients (age 64 ± 10 years, 56.4% men) with symptomatic bicuspid AS who were referred for surgery and underwent both cardiac CT and echocardiography within 3 months were studied. To calculate the aortic valve area, GOACT was measured by multiplanar CT planimetry, and EOAEcho was calculated by the continuity equation with Doppler echocardiography. The relationships between GOACT and EOAEcho and patient symptom scale, biomarkers, and left ventricular (LV) functional variables were analyzed. Results: There was a significant but modest correlation between EOAEcho and GOACT (r = 0.604, p < 0.001). Both EOAEcho and GOACT revealed significant correlations with mean pressure gradient and peak transaortic velocity, and the coefficients were higher in EOAEcho than in GOACT. EOAEcho of 1.05 cm2 and GOACT of 1.25 cm2 corresponds to hemodynamic cutoff values for diagnosing severe AS. EOAEcho was well correlated with the patient symptom scale and log NT-pro BNP, but GOACT was not. In addition, EOAEcho had a higher correlation coefficient with estimated LV filling pressure and LV global longitudinal strain than GOACT. Conclusion: GOACT can be used to evaluate the severity of bicuspid AS. The threshold for GOACT for diagnosing severe AS should be higher than that for EOAEcho. However, EOAEcho is still the method of choice because EOAEcho showed better correlations with clinical and functional variables than GOACT.

Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium.

PURPOSE: We aimed to develop a deep learning (DL)-based algorithm for automated quantification of aortic valve calcium (AVC) from non-enhanced electrocardiogram-gated cardiac CT scans and compare performance of DL-measured AVC volume and Agatston score with those of visual gradings by radiologist readers for classification of AVC severity. METHOD: A total of 589 CT examinations performed at a single center between March 2010 and August 2017 were retrospectively included. The DL algorithm was designed to segment AVC and to quantify AVC volume, and Agatston score was calculated using attenuation values. Manually measured AVC volume and Agatston score were used as ground truth. To validate AVC segmentation performance, the Dice coefficient was calculated. For observer performance testing, four radiologists determined AVC grade in two reading rounds. The diagnostic performance of DL-measured AVC volume and Agaston score for classifying severe AVC was compared with that of each reader's assessment. RESULTS: After applying the DL algorithm, the Dice coefficient score was 0.807. In patients with AVC, accuracy of DL-measured AVC volume for AVC grading was 97.0 % with area under the curve (AUC) of 0.964 (95 % confidence interval [CI] 0.923-1) in the test set, which was better than the radiologist readers (accuracy 69.7 %-91.9 %, AUC 0.762-0.923) with manually measured AVC volume as ground truth. When manually measured AVC Agatston score was used as ground truth, accuracy of DL-measured AVC Agatston score for AVC grading was 92.9 % with AUC of 0.933 (95 % CI 0.885-0.981) in the test set, which was also better than the radiologist readers (accuracy 77.8-89.9 %, AUC 0.791-0.903). CONCLUSIONS: DL-based automated AVC quantification may be comparable with manual measurements. The diagnostic performance of the DL-measured AVC volume and Agatston score for classification of severe AVC outperforms radiologist readers.

Differentiation of left atrial appendage thrombus from circulatory stasis using cardiac CT radiomics in patients with valvular heart disease.

OBJECTIVES: To determine whether quantitative radiomic features from cardiac CT could differentiate the left atrial appendage (LAA) thrombus from circulatory stasis in patients with valvular heart disease. METHODS: Ninety-five consecutive patients with valvular heart disease and filling defects in LAA on two-phase cardiac CT from March 2016 to August 2018 were retrospectively enrolled and classified as having thrombus or stasis by transesophageal echocardiography or cardiac surgery. The ratio of Hounsfield units in the filling defects to those in the ascending aorta (AA) was calculated on early- and late-phase CT (LAA/AAE and LAA/AAL, respectively). Radiomic features were extracted from semi-automated three-dimensional segmentation of the filling defect on early-phase CT. The diagnostic ability of radiomic features for differentiating thrombus from stasis was assessed and compared to LAA/AAE and LAA/AAL by comparing the AUC of ROC curves. Diagnostic performances of CT attenuation ratios and radiomic features were validated with an independent validation set. RESULTS: Thrombus was diagnosed in 25 cases and stasis in 70. Sixty-eight radiomic features were extracted. Values of 8 wavelet-transformed features were lower in thrombus than in stasis (p < 0.001). The AUC value of a radiomic feature, wavelet_LHL, for diagnosing thrombus was 0.78, which was higher than that of LAA/AAE (AUC = 0.54, p = 0.025) and similar to that of LAA/AAL (AUC = 0.76, p = 0.773). In the validation set, the AUC of wavelet_LHL was 0.71, which was higher than that of LAA/AAE (AUC = 0.57, p = 0.391) and similar to that of LAA/AAL (AUC = 0.75, p = 0.707). CONCLUSIONS: Quantitative radiomic features from the early phase of cardiac CT may help diagnose LAA thrombus in patients with valvular heart disease. KEY POINTS: • Wavelet-transformed grey-level non-uniformity values from radiomic analysis are significantly lower for LAA thrombus than for circulatory stasis. • Radiomic features may have an additional value for differentiating LAA thrombus from circulatory stasis when interpreting single-phase cardiac CT. • Radiomic features extracted from single-phase images may show similar diagnostic ability as conventional quantitative analysis from two-phase images.

2020 Clinical Practice Guideline for Percutaneous Transthoracic Needle Biopsy of Pulmonary Lesions: A Consensus Statement and Recommendations of the Korean Society of Thoracic Radiology.

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.

Characteristics and Implications of Left Atrial Calcium on Cardiac Computed Tomography in Patients With Earlier Mitral Valve Operation.

Left atrial calcium (LAC) is often observed in patients who have undergone mitral valve (MV) surgery, but little is known about its characteristics and clinical implications. Therefore, we sought to investigate the structural and hemodynamic significance of LAC and its association with clinical outcomes. We investigated 327 patients with repaired or prosthetic MV who underwent cardiac CT from 2010 to 2017. The degree of LAC was analyzed and classified into three groups: group 1 (no LAC), group 2 (mild-to-moderate LAC), and group 3 (severe LAC). Clinical and echocardiographic characteristics and clinical outcomes were compared in three groups. LAC was seen in 79 (24.2%) patients. Groups 2 and 3 showed more prevalent atrial fibrillation, a rheumatic etiology, a higher number of previous surgeries, a larger LA volume index, and higher pulmonary artery systolic pressure than group 1. Paravalvular leakage of the MV increased progressively according to severity of LAC (15.4% in group 1, 39.3% in group 2, and 66.7% in group 3, p <0.001). Event-free survival rate for major adverse cardiovascular adverse events (log rank p = 0.033) and all-cause mortality (log rank p <0.001) were significantly different according to LAC group. In Cox regression analyses, presence of severe LAC was an independent predictor of all-cause mortality (hazard ratio: 4.44, 95% confidence interval: 1.71 to 11.58, p = 0.002). LAC on cardiac CT is not uncommon and reflects more advanced LA remodeling and a stiff LA. The presence and severity of LAC are associated with a worse clinical outcome after MV surgery.

Role of Cardiac Computed Tomography for Etiology Evaluation of Newly Diagnosed Heart Failure with Reduced Ejection Fraction.

Delayed-enhanced dual-energy computed tomography (DECT) can evaluate the extent and degree of myocardial fibrosis while coronary CT angiography (CCTA) is a widely accepted coronary artery evaluation method. We sought to describe the role of combined cardiac CT for the evaluation of underlying etiology in patients with newly diagnosed heart failure with reduced ejection fraction (HFrEF). Sixty-three consecutive patients (31 men, 63 ± 16 years) with newly diagnosed HFrEF were enrolled in this prospective study. Coronary artery disease and myocardial fibrosis were evaluated on CCTA and DECT, respectively, and the tentative underlying etiologies of heart failure (HF) were determined with combinations of findings from both CTs. Concordance between tentative etiologies from cardiac CT and final etiologies from clinical decisions within a 2-year follow-up was assessed. Eighteen patients were diagnosed with ischemic HF on initial cardiac CT, and the final diagnosis was not changed. Another 45 patients with nonischemic HF included tentative etiologies of dilated cardiomyopathy (n = 32, 71.1%), sarcoidosis or myocarditis (n = 8, 17.8%), amyloidosis (n = 2, 4.4%), noncompaction (n = 2, 4.4%) and arrhythmogenic right ventricular cardiomyopathy (n = 1, 2.2%). Five nonischemic HF patients showed different etiologies between initial cardiac CT and clinical decisions. The concordance between cardiac CT and clinical decisions was 92.1%. A high degree of concordance was achieved between tentative etiologies from cardiac CT and final diagnoses from clinical decisions. Combined cardiac CT is a feasible, safe and effective imaging tool for the initial evaluation of newly diagnosed HFrEF patients.

Coronary artery calcium severity grading on non-ECG-gated low-dose chest computed tomography: a multiple-observer study in a nationwide lung cancer screening registry.

OBJECTIVES: To identify the optimal method for evaluation of coronary artery calcium (CAC) severity on non-electrocardiogram-gated low-dose chest computed tomography (LDCT) in a nationwide lung cancer screening registry. METHODS: A total of 256 subjects were retrospectively enrolled from participants of the Korean Lung Cancer Screening (K-LUCAS) project (an LDCT lung cancer screening registry for high-risk individuals). Four board-certified cardiothoracic radiologists independently assessed CAC severity using four different scoring methods (visual assessment, artery-based grading, segment-involvement grading, and segment-based grading) and classified severity for each case using all four methods as none, mild, moderate, or severe. Agreements between the four observers for CAC category classification and between the four different scoring methods for the same observer were assessed by Fleiss kappa statistics. Evaluation time for CAC grading was compared between observers and between grading methods. RESULTS: Interobserver agreement was moderate for visual assessment (Fleiss kappa 0.451) and substantial for the other three methods (Fleiss kappa 0.673-0.704). Agreement between the four grading methods for the same observer was substantial for three observers (Fleiss kappa 0.610-0.705) and moderate for one (Fleiss kappa 0.578). Mean evaluation time differed significantly between methods (visual assessment, 14.3 ± 11.8 s; artery-based grading, 17.6 ± 22.3 s, segment-involvement grading, 19.2 ± 6.8 s; segment-based grading, 34.2 ± 37.4 s; p < 0.01). CONCLUSION: Artery-based grading could be appropriate with substantial interobserver agreement and an acceptable mean evaluation time. KEY POINTS: • CAC severity grading methods on LDCT show moderate to substantial agreements between grading methods and observers. • Artery-based grading could be appropriate with substantial interobserver agreement and a mean evaluation time of 17.6 s. • Visual assessment is disadvantaged by high interobserver variability despite having the shortest evaluation time.

Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas.

BACKGROUND: We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma. METHODS: This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability. RESULTS: Among 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299). CONCLUSIONS: Quantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression. KEY POINTS: Significant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.

A Novel Algorithm to Differentiate Between Multiple Primary Lung Cancers and Intrapulmonary Metastasis in Multiple Lung Cancers With Multiple Pulmonary Sites of Involvement.

INTRODUCTION: Differentiating between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) is critical for developing a therapeutic strategy to treat multiple lung cancers with multiple pulmonary sites of involvement. METHODS: We retrospectively included 252 lesions (126 pairs) from 126 patients with surgically resected multiple lung adenocarcinomas. Each pair was classified as MPLC or IPM based on histopathologic findings as the reference standard. A novel algorithm was established with four sequential decision steps based on the combination of computed tomography (CT) lesion types (step 1), CT lesion morphology (step 2), difference of maximal standardized uptake values on positron-emission tomography/CT (step 3), and presence of N2/3 lymph node metastasis or distant metastasis (step 4). The diagnostic accuracy of the algorithm was analyzed. Performances of 11 observers were assessed without and with knowledge of algorithm. RESULTS: Among 126 pairs, 90 (71.4%) were classified as MPLCs and 36 (28.6%) as IPMs. On applying the diagnostic algorithm, the overall accuracy for diagnosis of IPM among conclusive cases up to step 4 was 88.9%, and 65 and 44 pairs were correctly diagnosed based on step 1 and step 2, respectively. Specificity and positive predictive value for diagnosis of IPM increased significantly in all observers compared with reading rounds without the algorithm. CONCLUSIONS: Application of the algorithm based on comprehensive information on clinical and imaging variables can allow differentiation between MPLCs and IPMs. When both of two suspected malignant lesions appear as solid predominant lesions without spiculation or air-bronchogram on CT, IPM should be considered.

Utility of FDG PET/CT for Preoperative Staging of Non-Small Cell Lung Cancers Manifesting as Subsolid Nodules With a Solid Portion of 3 cm or Smaller.

OBJECTIVE. The objective of our study was to investigate the utility of FDG PET/CT for the preoperative staging of subsolid non-small cell lung cancers (NSCLCs) with a solid portion size of 3 cm or smaller. MATERIALS AND METHODS. We retrospectively enrolled 855 patients with pathologically proven NSCLCs manifesting as subsolid nodules with a solid portion of 3 cm or smaller on CT. We then compared the diagnostic performances of FDG PET/CT and chest CT for detecting lymph node (LN), intrathoracic, or distant metastases in patients who underwent preoperative chest CT and FDG PET/CT. After propensity score matching, we compared the diagnostic performance of FDG PET/CT in the group who underwent both chest CT and FDG PET/CT with that of chest CT in patients who did not undergo FDG PET/CT. RESULTS. There were LN metastases in 25 of 765 patients (3.3%) who underwent surgical LN dissection or biopsy and intrathoracic or distant metastasis in two of 855 patients (0.2%). For LN staging, FDG PET/CT showed a sensitivity of 44.0%, specificity of 81.5%, positive predictive value of 9.6%, negative predictive value of 97.0%, and accuracy of 79.9%, which were lower than those of chest CT for accuracy (p < 0.0001). FDG PET/CT could not accurately detect any intrathoracic or distant metastasis. After propensity score matching, the diagnostic accuracy for LN staging of FDG PET/CT in the group who underwent both CT and FDG PET/CT was lower than that of chest CT in the group who did not undergo FDG PET/CT (p = 0.002), and the diagnostic accuracy for intrathoracic and distant metastases was not different (p > 0.999). CONCLUSION. FDG PET/CT has limited utility in preoperatively detecting LN or distant metastasis in patients with subsolid NSCLCs with a solid portion size of 3 cm or smaller.