Long-term outcomes after endoscopic versus surgical resection of T1 colorectal carcinoma.

BACKGROUND: The long-term outcomes of patients with T1 colorectal cancer (CRC) who undergo endoscopic and/or surgical treatment are not well understood. Invasive CRC confined to the colonic submucosa (T1 CRC) is challenging in terms of clinical decision-making. We compared the long-term outcomes of T1 CRC by treatment method. METHODS: We examined 370 patients with pathological T1 CRC treated between 2000 and 2015 at Seoul St. Mary's Hospital. In total, 93 patients underwent endoscopic resection (ER) only, 82 underwent additional surgery after ER, and 175 underwent surgical resection only. Patients who did not meet the curative criteria were defined as "high-risk." High-risk patients were classified into three groups according to the treatment modalities: ER only (Group A: 35 patients), additional surgery after ER (Group B: 72 patients), and surgical resection only (Group C: 133 patients). The recurrence-free and overall survival (OS) rates, and factors associated with recurrence and mortality, were analyzed. Factors associated with lymph node metastasis (LNM) were subjected to multivariate analysis. RESULTS: Of the 370 patients, 7 experienced recurrence and 7 died. All recurrences occurred in the high-risk group and two deaths were in the low-risk group. In high-risk groups, there was no significant group difference in recurrence-free survival (P = 0.511) or OS (P =0.657). Poor histology (P =0.042) was associated with recurrence, and vascular invasion (P =0.044) with mortality. LNMs were observed in 30 of 277 patients who underwent surgery either initially or secondarily. Lymphatic invasion was significantly associated with the incidence of LNM (P < 0.001). CONCLUSIONS: ER prior to surgery did not affect the prognosis of high-risk T1 CRC patients, and did not worsen the clinical outcomes of patients who required additional surgery. Lymphatic invasion was the most important predictor of LNM.

Correction to: Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest.

An amendment to this paper has been published and can be accessed via the original article.

Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest.

BACKGROUND: Emergency department overcrowding negatively impacts critically ill patients and could lead to the occurrence of cardiac arrest. However, the association between emergency department crowding and the occurrence of in-hospital cardiac arrest has not been thoroughly investigated. This study aimed to evaluate the correlation between emergency department occupancy rates and the incidence of in-hospital cardiac arrest. METHODS: A single-center, observational, registry-based cohort study was performed including all consecutive adult, non-traumatic in-hospital cardiac arrest patients between January 2014 and June 2017. We used emergency department occupancy rates as a crowding index at the time of presentation of cardiac arrest and at the time of maximum crowding, and the average crowding rate for the duration of emergency department stay for each patient. To calculate incidence rate, we divided the number of arrest cases for each emergency department occupancy period by accumulated time. The primary outcome is the association between the incidence of in-hospital cardiac arrest and emergency department occupancy rates. RESULTS: During the study period, 629 adult, non-traumatic cardiac arrest patients were enrolled in our registry. Among these, 187 patients experienced in-hospital cardiac arrest. Overall survival discharge rate was 24.6%, and 20.3% of patients showed favorable neurologic outcomes at discharge. Emergency department occupancy rates were positively correlated with in-hospital cardiac arrest occurrence. Moreover, maximum emergency department occupancy in the critical zone had the strongest positive correlation with in-hospital cardiac arrest occurrence (Spearman rank correlation ρ = 1.0, P < .01). Meanwhile, occupancy rates were not associated with the ED mortality. CONCLUSION: Maximum emergency department occupancy was strongly associated with in-hospital cardiac arrest occurrence. Adequate monitoring and managing the maximum occupancy rate would be important to reduce unexpected cardiac arrest.

Association between long-term exposure to air pollutants and cardiopulmonary mortality rates in South Korea.

BACKGROUND: The association between long-term exposure to air pollutants, including nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and particulate matter 10 µm or less in diameter (PM10), and mortality by ischemic heart disease (IHD), cerebrovascular disease (CVD), pneumonia (PN), and chronic lower respiratory disease (CLRD) is unclear. We investigated whether living in an administrative district with heavy air pollution is associated with an increased risk of mortality by the diseases through an ecological study using South Korean administrative data over 19 years. METHODS: A total of 249 Si-Gun-Gus, unit of administrative districts in South Korea were studied. In each district, the daily concentrations of CO, SO2, NO2, O3, and PM10 were averaged over 19 years (2001-2018). Age-adjusted mortality rates by IHD, CVD, PN and CLRD for each district were averaged for the same study period. Multivariate beta-regression analysis was performed to estimate the associations between air pollutant concentrations and mortality rates, after adjusting for confounding factors including altitude, population density, higher education rate, smoking rate, obesity rate, and gross regional domestic product per capita. Associations were also estimated for two subgrouping schema: Capital and non-Capital areas (77:172 districts) and urban and rural areas (168:81 districts). RESULTS: For IHD, higher SO2 concentrations were significantly associated with a higher mortality rate, whereas other air pollutants had null associations. For CVD, SO2 and PM10 concentrations were significantly associated with a higher mortality rate. For PN, O3 concentrations had significant positive associations with a higher mortality rate, while SO2, NO2, and PM10 concentrations had significant negative associations. For CLRD, O3 concentrations were associated with an increased mortality rate, while CO, NO2, and PM10 concentrations had negative associations. In the subgroup analysis, positive associations between SO2 concentrations and IHD mortality were consistently observed in all subgroups, while other pollutant-disease pairs showed null, or mixed associations. CONCLUSION: Long-term exposure to high SO2 concentration was significantly and consistently associated with a high mortality rate nationwide and in Capital and non-Capital areas, and in urban and rural areas. Associations between other air pollutants and disease-related mortalities need to be investigated in further studies.

MicroRNA-221 governs tumor suppressor HDAC6 to potentiate malignant progression of liver cancer.

BACKGROUND & AIMS: Most common reason behind changes in histone deacetylase (HDAC) function is its overexpression in cancer. However, among HDACs in liver cancer, HDAC6 is uniquely endowed with a tumor suppressor, but the mechanism underlying HDAC6 inactivation has yet to be uncovered. METHODS: Microarray profiling and target prediction programs were used to identify miRNAs targeting HDAC6. A series of inhibitors, activators and siRNAs was introduced to validate regulatory mechanisms for microRNA-221-3p (miR-221) governing HDAC6 in hepatocarcinogenesis. RESULTS: Comprehensive miRNA profiling analysis identified seven putative endogenous miRNAs that are significantly upregulated in hepatocellular carcinoma (HCC). While miR-221 was identified as a suppressor of HDAC6 by ectopic expression of miRNA mimics in Dicer knockdown cells, targeted-disruption of miR-221 repressed cancer cell growth through derepressing HDAC6 expression. Suppression of HDAC6 via miR-221 was induced by JNK/c-Jun signaling in liver cancer cells but not in normal hepatic cells. Additionally, cytokine-induced NF-κBp65 independently regulated miR-221, thereby suppressing HDAC6 expression in HCC cells. HCC tissues derived from chemical-induced rat and H-ras12V transgenic mice liver cancer models validated that JNK/c-Jun activation and NF-κBp65 nuclear translocation are essential for the transcription of miR-221 leading to repression of HDAC6 in HCC. CONCLUSIONS: Our findings suggest that the functional loss or suppression of the tumor suppressor HDAC6 is caused by induction of miR-221 through coordinated JNK/c-Jun- and NF-κB-signaling pathways during liver tumorigenesis, providing a novel target for the molecular treatment of liver malignancies.

Characteristic molecular and proteomic signatures of drug-induced liver injury in a rat model.

Drug-induced liver injury (DILI) is a major safety concern during drug development and remains one of the main reasons for withdrawal of drugs from the market. Although it is crucial to develop methods that will detect potential hepatotoxicity of drug candidates as early and as quickly as possible, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data. Hence, in this study, we assessed characteristic molecular signatures in rat liver treated with drugs (pyrazinamide, ranitidine, enalapril, carbamazepine and chlorpromazine) that are known to cause DILI in humans. Unsupervised hierarchical clustering analysis of transcriptome changes induced by DILI-causing drugs resulted in three different subclusters on dendrogram, i.e., hepatocellular, cholestatic and mixed type of DILI at early time points (2 days), and multiclassification analysis suggested 31 genes as discernible markers for each DILI pattern. Further analysis for characteristic molecular signature of each DILI pattern provided a molecular basis for different modes of DILI action. A proteomics study of the same rat livers was used to confirm the results, and the two sets of data showed 60 matching classifiers. In conclusion, the data of different DILI-causing drug treatments from genomic analysis in a rat model suggest that DILI-specific molecular signatures can discriminate different patterns of DILI at an early exposure time point, and that they provide useful information for mechanistic studies that may lead to a better understanding of the molecular basis of DILI.

Sirtuin7 oncogenic potential in human hepatocellular carcinoma and its regulation by the tumor suppressors MiR-125a-5p and MiR-125b.

UNLABELLED: Sirtuins are nicotinamide adenine dinucleotide oxidized form (NAD(+) )-dependent deacetylases and function in cellular metabolism, stress resistance, and aging. For sirtuin7 (SIRT7), a role in ribosomal gene transcription is proposed, but its function in cancer has been unclear. In this study we show that SIRT7 expression was up-regulated in a large cohort of human hepatocellular carcinoma (HCC) patients. SIRT7 knockdown influenced the cell cycle and caused a significant increase of liver cancer cells to remain in the G1 /S phase and to suppress growth. This treatment restored p21(WAF1/Cip1) , induced Beclin-1, and repressed cyclin D1. In addition, sustained suppression of SIRT7 reduced the in vivo tumor growth rate in a mouse xenograft model. To explore mechanisms in SIRT7 regulation, microRNA (miRNA) profiling was carried out. This identified five significantly down-regulated miRNAs in HCC. Bioinformatics analysis of target sites and ectopic expression in HCC cells showed that miR-125a-5p and miR-125b suppressed SIRT7 and cyclin D1 expression and induced p21(WAF1/Cip1) -dependent G1 cell cycle arrest. Furthermore, treatment of HCC cells with 5-aza-2'-deoxycytidine or ectopic expression of wildtype but not mutated p53 restored miR-125a-5p and miR-125b expression and inhibited tumor cell growth, suggesting their regulation by promoter methylation and p53 activity. To show the clinical significance of these findings, mutations in the DNA binding domain of p53 and promoter methylation of miR-125b were investigated. Four out of nine patients with induced SIRT7 carried mutations in the p53 gene and one patient showed hypermethylation of the miR-125b promoter region. CONCLUSION: Our findings suggest the oncogenic potential of SIRT7 in hepatocarcinogenesis. A regulatory loop is proposed whereby SIRT7 inhibits transcriptional activation of p21(WAF1/Cip1) by way of repression of miR-125a-5p and miR-125b. This makes SIRT7 a promising target in cancer therapy. (HEPATOLOGY 2013).

Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs.

This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program's performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20-85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5-2.4 mm), followed by lumbosacral landmarks (median error 2.1-3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4-4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements.

Histone deacetylase 6 functions as a tumor suppressor by activating c-Jun NH2-terminal kinase-mediated beclin 1-dependent autophagic cell death in liver cancer.

UNLABELLED: Ubiquitin-binding histone deacetylase 6 (HDAC6) is uniquely endowed with tubulin deacetylase activity and plays an important role in the clearance of misfolded protein by autophagy. In cancer, HDAC6 has become a target for drug development due to its major contribution to oncogenic cell transformation. In the present study we show that HDAC6 expression was down-regulated in a large cohort of human hepatocellular carcinoma (HCC) patients, and that low expression of HDAC6 was significantly associated with poor prognosis of HCC patients in 5-year overall, disease-free, and recurrence-free survival. Notably, we observed that ectopic overexpression of HDAC6 suppressed tumor cell growth and proliferation in various liver cancer cells, and elicited increased LC3B-II conversion and autophagic vacuole formation without causing apoptotic cell death or cell cycle inhibition. In addition, the sustained overexpression of HDAC6 reduced the in vivo tumor growth rate in a mouse xenograft model. It was also found that HDAC6 mediated autophagic cell death by way of Beclin 1 and activation of the LC3-II pathway in liver cancer cells, and that HDAC6 overexpression activated c-Jun NH2-terminal kinase (JNK) and increased the phosphorylation of c-Jun. In contrast, the induction of Beclin 1 expression was blocked by SP600125 (a specific inhibitor of JNK) or by small interfering RNA directed against HDAC6. CONCLUSION: Our findings suggest that loss of HDAC6 expression in human HCCs and tumor suppression by HDAC6 occur by way of activation of caspase-independent autophagic cell death through the JNK/Beclin 1 pathway in liver cancer and, thus, that a novel tumor suppressor function mechanism involving HDAC6 may be amenable to nonepigenetic regulation.

Characteristic molecular signatures of early exposure to volatile organic compounds in rat liver.

OBJECTIVE: Investigation on whether the characteristic molecular signatures can discriminate individual volatile organic compounds (VOCs) and provide predictive markers for the detection of VOC exposure. METHODS: Transcriptomic analysis of liver tissues was performed 48 h after the single oral administration of three VOCs doses at LD25 or LD5 values, to Sprague-Dawley. RESULTS: Combination analysis of different multi-classifications suggested that 145 genes predicted VOC exposure. Additionally, Gene Set Enrichment Analysis of genes deregulated by VOCs revealed that T cell prolymphatic leukemia signaling was inactivated in all VOCs. CONCLUSIONS: These molecular markers could be widely implemented to assess and predict environmental exposure to VOCs.

Enhanced Cholesterol-Lowering and Antioxidant Activities of Soymilk by Fermentation with Lactiplantibacillus plantarum KML06.

This study aimed to evaluate the cholesterol-lowering and antioxidant activities of soymilk fermented with probiotic Lactobacillaceae strains and to investigate the production of related bioactive compounds. Lactiplantibacillus plantarum KML06 (KML06) was selected for the fermentation of soymilk because it has the highest antioxidant, cholesterol-lowering, and ß-glucosidase activities among the 10 Lactobacillaceae strains isolated from kimchi. The genomic information of strain KML06 was analyzed. Moreover, soymilk fermented with KML06 was evaluated for growth kinetics, metabolism, and functional characteristics during the fermentation period. The number of viable cells, which was similar to the results of radical scavenging activities and cholesterol assimilation, as well as the amount of soy isoflavone aglycones, daidzein, and genistein, was the highest at 12 h of fermentation. These results indicate that soymilk fermented with KML06 can prevent oxidative stress and cholesterol-related problems through the production of soy isoflavone aglycones.

Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network.

The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training.

HDAC2 overexpression confers oncogenic potential to human lung cancer cells by deregulating expression of apoptosis and cell cycle proteins.

Histone deacetylase 2 (HDAC2) is crucial for embryonic development, affects cytokine signaling relevant for immune responses, and is often significantly overexpressed in solid tumors, but little is known of its role in human lung cancer. In this study, we demonstrated the aberrant expression of HDAC2 in lung cancer tissues and investigated oncogenic properties of HDAC2 in human lung cancer cell lines. HDAC2 inactivation resulted in regression of tumor cell growth and activation of cellular apoptosis via p53 and Bax activation and Bcl2 suppression. In cell cycle regulation, HDAC2 inactivation caused induction of p21WAF1/CIP1 expression, and simultaneously suppressed the expressions of cyclin E2, cyclin D1, and CDK2, respectively. Consequently, this led to the hypophosphorylation of pRb protein in G1/S transition and thereby inactivated E2F/DP1 target gene transcriptions of A549 cells. In addition, we demonstrated that HDAC2 directly regulated p21WAF1/CIP1 expression in a p53-independent manner. However, HDAC1 was not related to p21WAF1/CIP1 expression and tumorigenesis of lung cancer. Lastly, we observed that sustained-suppression of HDAC2 in A549 lung cancer cells attenuated in vitro tumorigenic properties and in vivo tumor growth of the mouse xenograft model. Taken together, we suggest that the aberrant regulation of HDAC2 and its epigenetic regulation of gene transcription in apoptosis and cell cycle components play an important role in the development of lung cancer.

Characteristic molecular signature for early detection and prediction of persistent organic pollutants in rat liver.

Persistent organic pollutants (POPs) are degradation-resistant anthropogenic chemicals that accumulate in the food chain and in adipose tissue, and are among the most hazardous compounds ever synthesized. However, their toxic mechanisms are still undefined. To investigate whether characteristic molecular signatures can discriminate individual POP and provide prediction markers for the early detection of POPs exposure in an animal model, we performed transcriptomic analysis of rat liver tissues after exposure to POPs. The six different POPs (toxaphene, hexachlorobenzene, chlordane, mirex, dieldrin, and heptachlor) were administered to 11-week-old male Sprague-Dawley rats, and after 48 h of exposure, RNAs were extracted from liver tissues and subjected to rat whole genome expression microarrays. Early during exposure, conventional toxicological analysis including changes in the body and organ weight, histopathological examination, and blood biochemical analysis did not reflect any toxicant stresses. However, unsupervised gene expression analysis of rat liver tissues revealed in a characteristic molecular signature for each toxicant, and supervised analysis identified 2708 outlier genes that discerned the POPs exposure group from the vehicle-treated control. Combination analysis of two different multiclassifications suggested 384 genes as early detection markers for predicting each POP exposure with 100% accuracy. The data from large-scale gene expression analysis of a different POP exposure in rat model suggest that characteristic expression profiles exist in liver hepatic cells and multiclassification of POP-specific molecular signatures can discriminate each toxicant at an early exposure time. The use of these molecular markers may be more widely implemented in combination with more traditional techniques for assessment and prediction of toxicity exposure to POPs from an environmental aspect.

Identification of characteristic molecular signature for volatile organic compounds in peripheral blood of rat.

In a previous report we demonstrated that the transcriptomic response of liver tissue was specific to toxicants, and a characteristic molecular signature could be used as an early prognostic biomarker in rats. It is necessary to determine the transcriptomic response to toxicants in peripheral blood for application to the human system. Volatile organic compounds (VOCs) comprise a major group of pollutants which significantly affect the chemistry of the atmosphere and human health. In this study we identified and validated the specific molecular signatures of toxicants in rat whole blood as early predictors of environmental toxicants. VOCs (dichloromethane, ethylbenzene, and trichloroethylene) were administered to 11-week-old SD male rats after 48h of exposure, peripheral whole blood was subjected to expression profiling analysis. Unsupervised gene expression analysis resulted in a characteristic molecular signature for each toxicant, and supervised analysis identified 1,217 outlier genes as distinct molecular signatures discerning VOC exposure from healthy controls. Further analysis of multi-classification suggested 337 genes as early detective molecular markers for three VOCs with 100% accuracy. A large-scale gene expression analysis of a different VOC exposure animal model suggested that characteristic expression profiles exist in blood cells and multi-classification of this VOC-specific molecular signature can discriminate each toxicant at an early exposure time. This blood expression signature can thus be used as discernable surrogate marker for detection of biological responses to VOC exposure in an environment.

Molecular signature for early detection and prediction of polycyclic aromatic hydrocarbons in peripheral blood.

Whole blood is one of the most easily accessible biofluids, and circulating leukocytes would include informative transcripts as a first line of immune defense for many disease processes. To demonstrate that transcriptomic responses of circulating blood cells reflect the exposure to environmental toxicants and the characteristic molecular signatures can discriminate and predict the type of toxicant at an early exposure time, we identified and validated characteristic gene expression profiles of rat whole blood after exposure to polycyclic aromatic hydrocarbons (PAHs). At an early exposure time point, conventional toxicological analysis including changes in the body and organ weight, histopathological examination, and blood biochemical analysis did not reflect any toxicant stresses. However, unsupervised gene expression analysis of blood cells resulted in a characteristic molecular signature for each toxicant. Further analysis of multiclassification suggested 220 genes as early detective and surrogate markers for predicting each PAH with 100% accuracy. These findings suggest that the blood expression signature could be used as a predictable and discernible surrogate marker for detection and prediction of PAHs, and the use of these molecular markers may be more widely implemented in combination with more traditional techniques for assessment and prediction of toxicity exposure to PAHs from an environmental aspect.

Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations.

Realistic image generation is valuable in dental medicine, but still challenging for generative adversarial networks (GANs), which require large amounts of data to overcome the training instability. Thus, we generated lateral cephalogram X-ray images using a deep-learning-based progressive growing GAN (PGGAN). The quality of generated images was evaluated by three methods. First, signal-to-noise ratios of real/synthesized images, evaluated at the posterior arch region of the first cervical vertebra, showed no statistically significant difference (t-test, p = 0.211). Second, the results of an image Turing test, conducted by non-orthodontists and orthodontists for 100 randomly chosen images, indicated that they had difficulty in distinguishing whether the image was real or synthesized. Third, cephalometric tracing with 42 landmark points detection, performed on real and synthesized images by two expert orthodontists, showed consistency with mean difference of 2.08 ± 1.02 mm. Furthermore, convolutional neural network-based classification tasks were used to classify skeletal patterns using a real dataset with class imbalance and a dataset balanced with synthesized images. The classification accuracy for the latter case was increased by 1.5%/3.3% at internal/external test sets, respectively. Thus, the cephalometric images generated by PGGAN are sufficiently realistic and have potential to application in various fields of dental medicine.

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias.

OBJECTIVE: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). MATERIALS AND METHODS: The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1-5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). RESULTS: The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1-5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. CONCLUSION: The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test.

BACKGROUND: Generative adversarial network (GAN)-based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. OBJECTIVE: The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. METHODS: We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with <5 years of experience [Group I], 4 radiologists with 5-10 years of experience [Group II], and 2 radiologists with >10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image. RESULTS: The mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4% vs 1500/3000, 50.0%, respectively; P<.001). However, in terms of identifying synthetic images as fake, there was no significant difference in the specificity between the visual Turing test and random guessing (779/1500, 51.9% vs 750/1500, 50.0%, respectively; P=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0%; Group II, 726/1200, 60.5%; and Group III, 359/600, 59.8%; P=.36). Interreader agreements were poor (κ=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details. CONCLUSIONS: The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details.

Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach.

Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulation (ROSC). We developed and validated a machine learning-based system to predict good outcome in OHCA patients before ROSC. This prospective, multicenter, registry-based study analyzed non-traumatic OHCA data collected between October 2015 and June 2017. We used information available before ROSC as predictor variables, and the primary outcome was neurologically intact survival at discharge, defined as cerebral performance category 1 or 2. The developed models' robustness were evaluated and compared with various score metrics to confirm their performance. The model using a voting classifier had the best performance in predicting good neurological outcome (area under the curve = 0.926). We confirmed that the six top-weighted variables predicting neurological outcomes, such as several duration variables after the instant of OHCA and several electrocardiogram variables in the voting classifier model, showed significant differences between the two neurological outcome groups. These findings demonstrate the potential utility of a machine learning model to predict good neurological outcome of OHCA patients before ROSC.