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.

Coronary CTA for Acute Chest Pain in the Emergency Department: Comparison of 64-Detector-Row Single-Source and Third-Generation Dual-Source Scanners.

BACKGROUND. When coronary CTA is performed in the emergency department (ED), the use of a contemporary scanner with improved temporal resolution may eliminate the need to administer ß-blockers for heart rate (HR) control, thereby expediting workup. OBJECTIVE. The purpose of this study was to compare ED length of stay (LOS), image quality, frequency of nondiagnostic examinations, and other clinical outcomes between patients undergoing coronary CTA in the ED on a single-source CT (SSCT) scanner with HR control versus on a dual-source CT (DSCT) scanner without HR control. METHODS. This retrospective study included 509 patients (283 men, 226 women; mean age, 52.1 ± 15.1 [SD] years) at low to intermediate risk for acute coronary syndrome who underwent coronary CTA for acute chest pain during off-hours in a single ED from March 1, 2020, to April 25, 2022. A total of 205 patients initially underwent CTA using a 64-detector-row SSCT scanner with HR control (oral ß-blocker administration if HR was > 65 beats/min); after scanner replacement on April 26, 2021, 304 patients underwent CTA using a third-generation DSCT without HR control. Groups were compared in terms of ED LOS and CT completion time (defined as time from ordering CTA to completion of acquisition) using propensity score matching and additional endpoints including image quality and nondiagnostic examinations based on radiology reports. RESULTS. The DSCT group, compared with the SSCT group, showed no significant difference in median ED LOS (505 vs 457 minutes, respectively; p = .37) but showed shorter median CT completion time (95 vs 117 minutes, p < .001); on the basis of a mediation analysis, 89% of the reduction in CT completion time for DSCT was attributed to the absence of HR control. The DSCT group, compared with the SSCT group, showed higher frequency of examinations with good or excellent image quality (87.8% vs 60.0%, p < .001) and lower frequency of nondiagnostic examinations (1.6% vs 6.3%, p = .01) but showed no significant difference in frequencies of emergent cardiology consultation, invasive angiography, ED disposition, or coronary revascularization (all p > .05). No patient in either group experienced 30-day all-cause mortality or a major adverse cardiovascular event. CONCLUSION. The use of a DSCT scanner for coronary CTA can eliminate the need for ß-blocker administration for HR control while decreasing nondiagnostic examinations. CLINICAL IMPACT. A DSCT scanner can expedite clinical processes in the ED.

A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study.

OBJECTIVE: To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). MATERIALS AND METHODS: This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. RESULTS: For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). CONCLUSION: The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.

Stability selection for LASSO with weights based on AUC.

Stability selection is a variable selection algorithm based on resampling a dataset. Based on stability selection, we propose weighted stability selection to select variables by weighing them using the area under the receiver operating characteristic curve (AUC) from additional modelling. Through an extensive simulation study, we evaluated the performance of the proposed method in terms of the true positive rate (TPR), positive predictive value (PPV), and stability of variable selection. We also assessed the predictive ability of the method using a validation set. The proposed method performed similarly to stability selection in terms of the TPR, PPV, and stability. The AUC of the model fitted on the validation set with the selected variables of the proposed method was consistently higher in specific scenarios. Moreover, when applied to radiomics and speech signal datasets, the proposed method had a higher AUC with fewer variables selected. A major advantage of the proposed method is that it enables researchers to select variables intuitively using relatively simple parameter settings.

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.

Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness.

OBJECTIVES: To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). METHODS: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. RESULTS: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). CONCLUSIONS: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. KEY POINTS: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mm than with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mm than with LDCT2.5-mm.

Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT.

OBJECTIVES: To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. METHODS: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. RESULTS: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. CONCLUSIONS: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. KEY POINTS: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.

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.

Hepatocyte DAX1 Deletion Exacerbates Inflammatory Liver Injury by Inducing the Recruitment of CD4+ and CD8+ T Cells through NF-κB p65 Signaling Pathway in Mice.

Fulminant hepatitis is characterized by rapid and massive immune-mediated liver injury. Dosage-sensitive sex reversal-adrenal hypoplasia congenita critical region on the X chromosome, gene 1 (DAX1; NR0B1) represses the transcription of various genes. Here, we determine whether DAX1 serves as a regulator of inflammatory liver injury induced by concanavalin A (ConA). C57BL/6J (WT), myeloid cell-specific Dax1 knockout (MKO), and hepatocyte-specific Dax1 knockout (LKO) mice received single intravenous administration of ConA. Histopathological changes in liver and plasma alanine aminotransferase and aspartate aminotransferase levels in Dax1 MKO mice were comparable with those in WT mice following ConA administration. Unlike Dax1 MKO mice, Dax1 LKO mice were greatly susceptible to ConA-induced liver injury, which was accompanied by enhanced infiltration of immune cells, particularly CD4+ and CD8+ T cells, in the liver. Factors related to T-cell recruitment, including chemokines and adhesion molecules, significantly increased following enhanced and prolonged phosphorylation of NF-κB p65 in the liver of ConA-administered Dax1 LKO mice. This is the first study to demonstrate that hepatocyte-specific DAX1 deficiency exacerbates inflammatory liver injury via NF-κB p65 activation, thereby causing T-cell infiltration by modulating inflammatory chemokines and adhesion molecules. Our results suggest DAX1 as a therapeutic target for fulminant hepatitis treatment.

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.

Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.

We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 consecutive patients with 285 cardiac CT scans that were reconstructed with DLR, IR, and FBP, were retrospectively enrolled. Radiomic features were extracted from the left ventricular (LV) myocardium, and from the periprosthetic mass if patients had cardiac valve replacement. Radiomic features of LV myocardium from each reconstruction were compared using a fitting linear mixed model. Radiomics models were developed to diagnose periprosthetic abnormality, and the performance was evaluated using the area under the receiver characteristics curve (AUC). Most radiomic features of LV myocardium (73 of 88) were significantly different in pairwise comparisons between all three reconstruction methods (P < 0.05). The radiomics model on IR exhibited the best diagnostic performance (AUC 0.948, 95% CI 0.880-1), relative to DLR (AUC 0.873, 95% CI 0.735-1) and FBP (AUC 0.875, 95% CI 0.731-1), but these differences did not reach significance (P > 0.05). In conclusion, applying DLR to cardiac CT scans yields radiomic features distinct from those obtained with IR and FBP, implying that feature robustness is not guaranteed when applying DLR.

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.

Phantom-based correction for standardization of myocardial native T1 and extracellular volume fraction in healthy subjects at 3-Tesla cardiac magnetic resonance imaging.

OBJECTIVES: To investigate the effect of the phantom-based correction method for standardizing myocardial native T1 and extracellular volume fraction (ECV) in healthy subjects. METHODS: Seventy-one healthy asymptomatic adult (≥ 20 years) volunteers of five different age groups (34 men and 37 women, 45.5 ± 15.5 years) were prospectively enrolled in three academic hospitals. Cardiac MRI including Modified Look - Locker Inversion recovery T1 mapping sequence was performed using a 3-Tesla system with a different type of scanner for each hospital. Native T1 and ECV were measured in the short-axis T1 map and analyzed for mean values of the 16 entire segments. The myocardial T1 value of each subject was corrected based on the site-specific equation derived from the T1 Mapping and ECV Standardization phantom. The global native T1 and ECV were compared between institutions before and after phantom-based correction, and the variation in native T1 and ECV among institutions was assessed using a coefficient of variation (CoV). RESULTS: The global native T1 value significantly differed between the institutions (1198.7 ± 32.1 ms, institution A; 1217.7 ± 39.9 ms, institution B; 1232.7 ± 31.1 ms, institution C; p = 0.002), but the mean ECV did not (26.6-27.5%, p = 0.355). After phantom-based correction, the global native T1 and ECV were 1289.7 ± 32.4 ms and 25.0 ± 2.7%, respectively, and CoV for native T1 between the three institutions decreased from 3.0 to 2.5%. The corrected native T1 value did not significantly differ between institutions (1284.5 ± 31.5 ms, institution A; 1296.5 ± 39.1 ms, institution B; 1291.3 ± 29.3 ms, institution C; p = 0.440), and neither did the ECV (24.4-25.9%, p = 0.078). CONCLUSIONS: The phantom-based correction method can provide standardized reference T1 values in healthy subjects. KEY POINTS: • After phantom-based correction, the global native T1 of 16 entire myocardial segments on 3-T cardiac MRI is 1289.4 ± 32.4 ms, and the extracellular volume fraction was 25.0 ± 2.7% for healthy subjects. • After phantom - based correction was applied, the differences in the global native T1 among institutions became insignificant, and the CoV also decreased from 3.0 to 2.5%.

Progostic value of coronary artery calcium scores from 1.5 mm slice reconstructions of electrocardiogram-gated computed tomography scans in asymptomatic individuals.

It is unknown whether the thinner slice reconstruction has added value relative to 3 mm reconstructions in predicting major adverse cardiac events (MACEs). This retrospective study included 550 asymptomatic individuals who underwent cardiac CT. Coronary artery calcium (CAC) scores and severity categories were assessed from 1.5 and 3 mm scans. CAC scores obtained from 1.5 and 3 mm scans were compared using Wilcoxon signed-rank tests. Cox proportional hazard models were developed to predict MACEs based on the degree of coronary artery stenosis on coronary CT angiography and the presence of CAC on both scans. Model performances were compared using the time-dependent ROC curve and integrated area under the curve (iAUC) methods. The CAC scores obtained from 1.5 mm scans were significantly higher than those from 3 mm scans (median, interquartile range 4.5[0-71] vs. 0[0-48.4]; p < 0.001). Models showed no difference in predictive accuracy of the presence of CAC between 1.5 and 3 mm scans (iAUC, 0.625 vs. 0.672). In conclusion, CAC scores obtained from 1.5 mm scans are significantly higher than those from 3 mm scans, but do not provide added prognostic value relative to 3 mm scans.

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.