3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.

BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP). MATERIALS AND METHODS: Living liver donors who underwent MRCP using the gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the dice similarity coefficient to compare the model's segmentation with the manually labeled ground truth. RESULTS: The study incorporated 250 cases. There was no difference in the baseline characteristics between the train set (n=225) and test set (n=25). The overall mean Dice Similarity Coefficient was 0.80±0.20 between the ground truth and inference result. The qualitative assessment of the model showed relatively high accuracy especially for the common bile duct (88%), common hepatic duct (92%), hilum (96%), right hepatic duct (100%), and left hepatic duct (96%), while the third-order branch of the right hepatic duct (18.2%) showed low accuracy. CONCLUSION: The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation.

CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode.

In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.2 to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which corresponds to a temperature of approximately 200 °C, produced a maximal response of 19.2 to 100 ppm ethanol. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor's accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility, maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer. MATERIALS AND METHODS: This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists. RESULTS: The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88. CONCLUSION: The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

Radiation shielding effects of lead equivalent thickness of a radiation protective apron and distance during C-arm fluoroscopy-guided pain interventions: A randomized trial.

BACKGROUND: The present study aimed to evaluate the degree of radiation shielding effects according to lead equivalent thickness and distance during C-arm fluoroscopy-guided lumbar interventions. METHODS: The exposure time and air kerma were recorded using a fluoroscope. The effective dose (ED) was measured with and without the shielding material of the lead apron using 2 dosimeters at 2 positions. According to the lead equivalent thickness of the shielding material and distance from the side of the table, the groups were divided into 4 groups: group 1 (lead equivalent thickness 0.6 mm, distance 0 cm), group 2 (lead equivalent thickness 0.6 mm, distance 5 cm), group 3 (lead equivalent thickness 0.3 mm, distance 0 cm), and group 4 (lead equivalent thickness 0.3 mm, distance 5 cm). Mean differences such as air kerma, exposure time, ED, and ratio of EDs (ED with protector/ED without protector) were analyzed. RESULTS: A total of 400 cases (100 cases in each group) were collected. The ratio of ED was significantly lower in groups 1 and 2 (9.18 ±â€…2.78% and 9.56 ±â€…3.29%, respectively) when compared to that of groups 3 and 4 (21.93 ±â€…4.19% and 21.53 ±â€…4.30%, respectively). The reductive effect of a 5-cm distance was 33.3% to 36.1% when comparing the ED between groups 1 and 2 and groups 3 and 4. CONCLUSIONS: The 0.3- and 0.6-mm lead equivalent thickness protectors have a radiation attenuation effect of 78.1% to 78.5% and 90.4% to 90.8%, respectively. The 5-cm distance from the side of the table reduces radiation exposure by 33.3% to 36.1%.

Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning.

Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon's standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model's performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety.

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI.

BACKGROUND: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). METHODS: DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. RESULTS: 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. CONCLUSIONS: Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.

Structural and functional insights into Spns2-mediated transport of sphingosine-1-phosphate.

Sphingosine-1-phosphate (S1P) is an important signaling sphingolipid that regulates the immune system, angiogenesis, auditory function, and epithelial and endothelial barrier integrity. Spinster homolog 2 (Spns2) is an S1P transporter that exports S1P to initiate lipid signaling cascades. Modulating Spns2 activity can be beneficial in treatments of cancer, inflammation, and immune diseases. However, the transport mechanism of Spns2 and its inhibition remain unclear. Here, we present six cryo-EM structures of human Spns2 in lipid nanodiscs, including two functionally relevant intermediate conformations that link the inward- and outward-facing states, to reveal the structural basis of the S1P transport cycle. Functional analyses suggest that Spns2 exports S1P via facilitated diffusion, a mechanism distinct from other MFS lipid transporters. Finally, we show that the Spns2 inhibitor 16d attenuates the transport activity by locking Spns2 in the inward-facing state. Our work sheds light on Spns2-mediated S1P transport and aids the development of advanced Spns2 inhibitors.

Prediction of respiratory complications by quantifying lung contusion volume using chest computed tomography in patients with chest trauma.

Pulmonary contusion is an important risk factor for respiratory complications in trauma patients. Hence, we aimed to determine the relationship between the ratio of pulmonary contusion volume to the total lung volume and patient outcomes and the predictability of respiratory complications. We retrospectively included 73 patients with a pulmonary contusion on chest computed tomography (CT) from 800 patients with chest trauma admitted to our facility between January 2019 and January 2020. Chest injury severity was expressed as the ratio of pulmonary contusion volume to total lung volume by quantifying pulmonary contusion volume on chest CT. The cut-off value was 80%. Among the 73 patients with pulmonary contusion (77% males, mean age: 45.3 years), 28 patients had pneumonia, and five had acute respiratory distress syndrome. The number of patients in the severe risk group with > 20% of pulmonary contusion volume was 38, among whom 23 had pneumonia. For predicting pneumonia, the area under the receiver operating characteristic curves for the ratio of pulmonary contusion volume was 0.85 (95% confidence interval 0.76-0.95, p = 0.008); the optimal threshold was 70.4%. Quantifying pulmonary contusion volume using initial CT enables identifying patients with chest trauma at high risk of delayed respiratory complications.

Complications and recovery patterns after blunt splenic injury: Recommended duration and follow-up methods.

BACKGROUND: Splenic artery embolization (SAE) is commonly employed as a non-operative management technique for splenic injury. Nonetheless, information on follow-up duration and methods, and the natural course of splenic infarction after SAE is limited. Thus, this study is aimed to analyze the patterns of complications and recovery of splenic infarction after SAE and to determine the appropriate follow-up duration and method. METHODS: Medical records of 314 patients with blunt splenic injury admitted at the Pusan National University Hospital, Level I Trauma Centre were analyzed to identify patients who underwent SAE between January 2014 and November 2018. Computed tomography (CT) scans that were obtained after SAE in patients who were followed up were compared with all their previous CT scans to identify any changes in the spleen and the occurrence of complications such as sustained bleeding, pseudoaneurysm, splenic infarctions, or abscess formation. RESULTS: Of the 314 patients, 132 who underwent SAE were included in the study. In total, 30 complications were noted among the 132 patients; of these, 7 (5.30%) required repeat embolization and 9 (6.82%) required splenectomy. Splenic infarction of <50% occurred in 76 patients and that of ≥50% including total and near-total infarctions occurred in 40 patients. Among patients with splenic infarction of ≥50%, 3 (2.27%) patients had abscesses between 16 and 21 days after SAE, and the range of infarctions increased as the AAAST-OIS grade increased. After SAE, repeat abdominal CT scans for >14 days were obtained in 75 patients; among these, 67 pre-sented with recovery of splenic infarction. The median period of recovery was 43 days after SAE. CONCLUSION: The present findings suggest that patients with ≥50% infarction may need 3 weeks of closed observation, with or without a follow-up CT scan, to rule out infection after SAE, follow-up CT follow-up at 6 weeks after SAE may be necessary to confirm the recovery of the spleen.

Fat Loss in Patients with Metastatic Clear Cell Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors.

The purpose of this study was to determine the prognostic impact of fat loss after immune checkpoint inhibitor (ICI) treatment in patients with metastatic clear cell renal cell carcinoma (ccRCC). Data from 60 patients treated with ICI therapy for metastatic ccRCC were retrospectively analyzed. Changes in cross-sectional areas of subcutaneous fat (SF) between the pre-treatment and post-treatment abdominal computed tomography (CT) images were expressed as percentages and were divided by the interval between the CT scans to calculate ΔSF (%/month). SF loss was defined as ΔSF < -5%/month. Survival analyses for overall survival (OS) and progression-free survival (PFS) were performed. Patients with SF loss had shorter OS (median, 9.5 months vs. not reached; p < 0.001) and PFS (median, 2.6 months vs. 33.5 months; p < 0.001) than patients without SF loss. ΔSF was independently associated with OS (adjusted hazard ratio (HR), 1.49; 95% confidence interval (CI), 1.07-2.07; p = 0.020) and PFS (adjusted HR, 1.57; 95% CI, 1.17-2.12; p = 0.003), with a 5%/month decrease in SF increasing the risk of death and progression by 49% and 57%, respectively. In conclusion, Loss of SF after treatment initiation is a significant and independent poor prognostic factor for OS and PFS in patients with metastatic ccRCC who receive ICI therapy.

Ultrasound stimulation for non-invasive visual prostheses.

Globally, it is estimated there are more than 2.2 billion visually impaired people. Visual diseases such as retinitis pigmentosa, age-related macular degeneration, glaucoma, and optic neuritis can cause irreversible profound vision loss. Many groups have investigated different approaches such as microelectronic prostheses, optogenetics, stem cell therapy, and gene therapy to restore vision. However, these methods have some limitations such as invasive implantation surgery and unknown long-term risk of genetic manipulation. In addition to the safety of ultrasound as a medical imaging modality, ultrasound stimulation can be a viable non-invasive alternative approach for the sight restoration because of its ability to non-invasively control neuronal activities. Indeed, recent studies have demonstrated ultrasound stimulation can successfully modulate retinal/brain neuronal activities without causing any damage to the nerve cells. Superior penetration depth and high spatial resolution of focused ultrasound can open a new avenue in neuromodulation researches. This review summarizes the latest research results about neural responses to ultrasound stimulation. Also, this work provides an overview of technical viewpoints in the future design of a miniaturized ultrasound transducer for a non-invasive acoustic visual prosthesis for non-surgical and painless restoration of vision.

Radiation safety for pain physicians: principles and recommendations.

C-arm fluoroscopy is a useful tool for interventional pain management. However, with the increasing use of C-arm fluoroscopy, the risk of accumulated radiation exposure is a significant concern for pain physicians. Therefore, efforts are needed to reduce radiation exposure. There are three types of radiation exposure sources: (1) the primary X-ray beam, (2) scattered radiation, and (3) leakage from the X-ray tube. The major radiation exposure risk for most medical staff members is scattered radiation, the amount of which is affected by many factors. Pain physicians can reduce their radiation exposure by use of several effective methods, which utilize the following main principles: reducing the exposure time, increasing the distance from the radiation source, and radiation shielding. Some methods reduce not only the pain physician's but also the patient's radiation exposure. Taking images with collimation and minimal use of magnification are ways to reduce the intensity of the primary X-ray beam and the amount of scattered radiation. It is also important to carefully select the C-arm fluoroscopy mode, such as pulsed mode or low-dose mode, for ensuring the physician's and patient's radiation safety. Pain physicians should practice these principles and also be aware of the annual permissible radiation dose as well as checking their radiation exposure. This article aimed to review the literature on radiation safety in relation to C-arm fluoroscopy and provide recommendations to pain physicians during C-arm fluoroscopy-guided interventional pain management.

Preoperative Helical Dynamic Enhanced Multidetector Row Computed Tomography: Can It Be a Prognostic Indicator in Early-Stage Non-small Cell Lung Cancer?

OBJECTIVE: This study aimed to investigate the prognostic significance of dynamic contrast-enhanced computed tomography in patients with stage IA non-small cell lung cancer (NSCLC). METHODS: We retrospectively enrolled 139 patients (77 men, 62 women; mean age, 59 years) with stage IA NSCLC who underwent dynamic contrast-enhanced computed tomography. Data on age, pathologic subtype, peak enhancement, and net enhancement of primary lung cancer were collected and correlated with 5-year survival. RESULTS: Peak enhancement had a significant correlation with overall survival in the univariable analysis (hazard ratio [HR], 1.18, confidence interval [CI], 1.01-1.38; P = 0.04) and in the multivariable analysis (HR, 1.19; CI, 1.01-1.39; P = 0.04). Patients with peak enhancement of 90 Hounsfield unit or higher had a significantly increased risk of death compared with patients with less enhancement after curative surgery (HR, 4.15; CI, 1.23-13.95; P = 0.02). CONCLUSIONS: Our study confirmed the prognostic significance of peak enhancement as an indicator for the overall survival of stage IA NSCLC.

Obesity is associated with improved postoperative overall survival, independent of skeletal muscle mass in lung adenocarcinoma.

BACKGROUND: Although the obesity paradox is a topic of immense interest for oncologists and epidemiologists, the mechanism underlying this unexpected benefit of obesity is poorly understood. We explored the prognostic value of obesity and its association with skeletal muscle mass. METHODS: This retrospective study evaluated the data of patients who underwent surgical excision for lung adenocarcinoma between January 2011 and December 2015. Body mass index was categorized according to the criteria of the Asia-Pacific classification. Cross-sectional areas of the skeletal muscle, subcutaneous fat, and visceral fat were measured. Skeletal muscle mass status was defined based on the cut-offs of skeletal muscle index (cm2 /m2 ), calculated as the area of skeletal muscle divided by height squared. Overall survival was estimated using the Kaplan-Meier method, and differences in survival probabilities were compared using the log-rank test. Cox proportional hazards regression analysis was conducted to determine the association with overall survival. RESULTS: A total of 636 patients with a median age of 61 years (interquartile range, 54.0-68.5 years; 321 men and 315 women) were included. Obese patients (body mass index ≥ 25 kg/m2 ) had longer overall survival than non-obese patients (mean, 110.2 months vs. 98.7 months; log-rank P = 0.015). Under multivariable Cox proportional hazard regression analysis, obesity was associated with longer overall survival after adjusting for covariates (hazard ratio, 0.59; 95% confidence interval, 0.40-0.86; P = 0.007). The prognostic value of obesity remained and predicted favourable overall survival after additional adjusting for skeletal muscle mass status (hazard ratio, 0.57; 95% confidence interval, 0.36-0.89; P = 0.014), skeletal muscle index (hazard ratio, 0.53; 95% confidence interval, 0.33-0.84; P = 0.008), or skeletal muscle area (hazard ratio, 0.61; 95% confidence interval, 0.38-0.98; P = 0.041). No association was observed between skeletal muscle mass status and the impact of body mass index on overall survival (P for interaction = 0.512). CONCLUSIONS: Obesity was associated with favourable overall survival, independent of skeletal muscle mass, after surgical excision of lung adenocarcinoma.

Impact of Skeletal Muscle Loss and Visceral Obesity Measured Using Serial CT on the Prognosis of Operable Breast Cancers in Asian Patients.

OBJECTIVE: This study aimed to investigate the impact of baseline values and temporal changes in body composition parameters, including skeletal muscle index (SMI) and visceral adipose tissue area (VAT), measured using serial computed tomography (CT) imaging on the prognosis of operable breast cancers in Asian patients. MATERIALS AND METHODS: This study retrospectively included 627 Asian female (mean age ± standard deviation [SD], 53.6 ± 8.3 years) who underwent surgery for stage I-III breast cancer between January 2011 and September 2012. Body composition parameters, including SMI and VAT, were semi-automatically calculated on baseline abdominal CT at the time of diagnosis and follow-up CT for post-treatment surveillance. Serial changes in SMI and VAT were calculated as the delta values. Multivariable Cox regression analysis was used to evaluate the association of baseline and delta SMI and VAT values with disease-free survival. RESULTS: Among 627 patients, 56 patients (9.2%) had breast cancer recurrence after a median of 40.5 months. The mean value ± SD of the baseline SMI and baseline VAT were 43.7 ± 5.8 cm²/m² and 72.0 ± 46.0 cm², respectively. The mean value of the delta SMI was -0.9 cm²/m² and the delta VAT was 0.5 cm². The baseline SMI and VAT were not significantly associated with disease-free survival (adjusted hazard ratio [HR], 0.983; 95% confidence interval [CI], 0.937-1.031; p = 0.475 and adjusted HR, 1.001; 95% CI, 0.995-1.006; p = 0.751, respectively). The delta SMI and VAT were also not significantly associated with disease-free survival (adjusted HR, 0.894; 95% CI, 0.766-1.043; p = 0.155 and adjusted HR, 1.001; 95% CI, 0.989-1.014; p = 0.848, respectively). CONCLUSION: Our study revealed that baseline and early temporal changes in SMI and VAT were not independent prognostic factors regarding disease-free survival in Asian patients undergoing surgery for breast cancer.

3D multi-scale residual fully convolutional neural network for segmentation of extremely large-sized kidney tumor.

BACKGROUND AND OBJECTIVE: We propose a novel deep neural network, the 3D Multi-Scale Residual Fully Convolutional Neural Network (3D-MS-RFCNN) to improve segmentation in extremely large-sized kidney tumors. METHOD: The multi-scale approach with a deep neural network is applied to capture global contextual features. Our method, 3D-MS-RFCNN, consists of two encoders and one decoder as a single complete network. One of the encoders is designed for capturing global contextual information by using the low-resolution, down-sampled data from input images. In the decoder, features from the encoder for global contextual features are concatenated with up-sampled features from the previous layer and features from the other encoder. Ensemble learning strategy is also applied. RESULTS: We evaluated the performance of our proposed method using the KiTS public dataset and the in-house hospital dataset. When compared with the state-of-the-art method, Res3D U-Net, our model, 3D-MS-RFCNN, demonstrated greater accuracy (0.9390 dice score for KiTS dataset and 0.8575 dice score for external dataset) for segmenting extremely large-sized kidney tumors. CONCLUSIONS: Our proposed network shows significantly improved segmentation performance of extremely large-sized targets. This study can be usefully employed in the field of medical image analysis.

Relationship between door-to-embolization time and clinical outcomes after transarterial embolization in trauma patients with complex pelvic fracture.

BACKGROUND: While transarterial embolization (TAE) is an effective way to control arterial bleeding associated with pelvic fracture, the clinical outcomes according to door-to-embolization (DTE) time are unclear. This study investigated how DTE time affects outcomes in patients with severe pelvic fracture. METHODS: Using a trauma database between November 1, 2015 and December 31, 2019, trauma patients undergoing TAE were retrospectively reviewed. The final study population included 192 patients treated with TAE. The relationships between DTE time and patients' outcomes were evaluated. Multiple binomial logistic regression analyses, multiple linear regression analyses, and Cox hazard proportional regression analyses were performed to estimate the impacts of DTE time on clinical outcomes. RESULTS: The median DTE time was 150 min (interquartile range, 121-184). The mortality rates in the first 24 h and overall were 3.7% and 14.6%, respectively. DTE time served as an independent risk factor for mortality in the first 24 h (adjusted odds ratio = 2.00, 95% confidence interval [CI] = 1.20-3.34, p = 0.008). In Cox proportional hazards regression analyses, the adjusted hazard ratio of DTE time for mortality at 28 days was 1.24 (95% CI = 1.04-1.47, p = 0.014). In addition, there was a positive relationship between DTE time and requirement for packed red blood cell transfusion during the initial 24 h and a negative relationship between DTE time and ICU-free days to day 28. CONCLUSION: Shorter DTE time was associated with better survival in the first 24 h, as well as other clinical outcomes, in patients with complex pelvic fracture who underwent TAE. Efforts to minimize DTE time are recommended to improve the clinical outcomes in patients with pelvic fracture treated with TAE.

Does transarterial pelvic embolization have a negative effect on perioperative outcomes in pelvic ring injury and acetabular fracture?

OBJECTIVE: The purpose of this study was to determine whether TAE negatively impacted perioperative outcomes in patients who underwent open surgery for pelvic trauma. METHODS: We retrospectively reviewed the medical records and radiographs of patients who had open surgery for an acute pelvic trauma between February 2014 and May 2017. The patients were classified into two groups: those who underwent TAE and those who did not. We evaluated preoperative demographics and perioperative outcomes between the two groups. Injury type-specific comparisons were also performed. RESULTS: A total of 136 patients (50 TAE and 86 non-TAE) were included in this study. There were significant differences in preoperative demographics including the type of injury, injury severity score, revised trauma score, surgical approach, initial blood pressure, and the amount of transfusion within 24 h between the two groups. However, no differences in perioperative outcomes were identified except for the amount of total transfusion. There were significant differences between the two groups in the injury type-specific comparisons, specifically in the amount of transfusion within 24 h in patients with pelvic ring injury and in the injury severity score in patients with acetabular fracture. However, there was no difference in perioperative outcomes between the groups for either injury type. CONCLUSION: In the present study, we were unable to identify negative effects of TAE on perioperative outcomes. Therefore, when considering open surgery subsequent to TAE, there is no evidence of increased risk for negative perioperative outcomes, especially with respect to bone healing and deep infection.

Prediction of epithelial-to-mesenchymal transition molecular subtype using CT in gastric cancer.

OBJECTIVES: To develop a prediction model with computed tomography (CT) images and to build a nomogram incorporating known clinicopathologic variables for individualized estimation of epithelial-to-mesenchymal transition (EMT) subtype gastric cancer. METHODS: Patients who underwent primary resection of gastric cancer (GC) and molecular subgroup analysis (n = 451) were reviewed. Multivariable analysis using a stepwise variable selection method was performed to build a predictive model for EMT subtype GC. A nomogram using the results of the multivariable analysis was constructed. An optimal cutoff value of total prognostic points of the nomogram for the prediction of EMT subtype was determined. The predictive model for the EMT subtype was internally validated by bootstrap resampling method. RESULTS: There were 88 patients with EMT subtype and 363 patients with non-EMT subtype based on transcriptome analysis. The patient's age, Lauren classification, and mural stratification on CT were variables selected for the predictive model. The area under the curve (AUC) of the model was 0.865, and the validated AUC of the bootstrap sample was 0.860. The optimal cutoff value of total prognostic points for the prediction of EMT subtype was 94.622, with 90.9% sensitivity, 67.2% specificity, and 71.8% accuracy. CONCLUSION: A predictive model using patient's age, Lauren classification, and mural stratification on CT for EMT molecular subtype GC was made. A nomogram was built which would serve as a useful screening tool for an individualized estimate of EMT subtype. KEY POINTS: • A predictive model for epithelial-to-mesenchymal transition (EMT) subtype incorporating patient's age, Lauren classification, and mural stratification on CT was built. • The predictive model had high diagnostic accuracy (area under the curve (AUC) = 0.865) and was validated (bootstrap AUC = 0.860). • Adding CT findings to clinicopathologic variables increases the accuracy of the predictive model than using only.