Impact of metabolic syndrome on postoperative outcomes of transsphenoidal pituitary surgery: analysis of U.S. nationwide inpatient sample data 2005-2018.

Introduction: Transsphenoidal surgery (TSS) is the preferred surgical method for most pituitary adenomas owing to high efficacy and low mortality. This study aimed to evaluate the influence of metabolic syndrome (MetS) on postoperative outcomes of TSS for pituitary adenoma. Methods: This population-based, retrospective observational study extracted data of adults 20-79 y receiving TSS for pituitary adenoma from the US Nationwide Inpatient Sample (NIS) between 2005-2018. Primary outcomes were pituitary-related complications, poor outcomes (i.e., in-hospital mortality or unfavorable discharge), prolonged length of stay (LOS), and patient safety indicators (PSIs). Univariate and multivariate regressions were performed to determine the associations between study variables and outcomes. Results: 19,076 patients (representing a 93,185 US in-patient population) were included, among which 2,109 (11.1%) patients had MetS. After adjustment, pre-existing MetS was not significantly associated with presence of pituitary-related complications and poor outcomes. In contrast, MetS was significantly associated with an increased risk for prolonged LOS (adjusted OR (aOR) = 1.19; 95% CI: 1.05-1.34), PSIs (aOR = 1.31; 95% CI: 1.07-1.59) and greater hospital costs (adjusted ß = 8.63 thousand USD; 95% CI: 4.98-12.29). Among pituitary-related complications, MetS was independently associated with increased risk of cerebrospinal fluid (CSF) rhinorrhea (aOR = 1.22, 95% CI: 1.01, 1.47) but lowered diabetes insipidus (aOR = 0.83, 95% CI: 0.71, 0.97). Discussion: MetS does not pose excessive risk of in-hospital mortality or unfavorable discharge. However, MetS independently predicted having PSIs, prolonged LOS, greater hospital costs, and CSF rhinorrhea. Study findings may help clinicians achieve better risk stratification before TSS.

The impact of surgeon's academic leave on surgical outcomes for endoscopic transsphenoidal resection of pituitary tumors.

Background: Endoscopic surgery has become the mainstay of pituitary surgery, but requires comprehensive surgical training. We evaluate the impact of a surgeon's academic leave during endoscopic training on surgical outcomes of patients with pituitary tumors. Methods: This retrospective study reviewed the surgical outcomes of endoscopic transsphenoidal surgery for pituitary tumors performed by a single surgeon. The last 56 surgical cases were performed between July 2010 and August 2014 before academic leave (Phase 1 surgery group), while another 56 consecutive cases were performed between November 2017 and March 2020 immediately after the surgeon's academic leave (Phase 2 surgery group). Demographic and clinical characteristics were collected and compared between the two surgery groups. Results: Overall, most surgical outcomes of endoscopic transsphenoidal surgery were not affected adversely by the period of academic leave. The operative time and length of hospital stay was lower in the Phase 2 surgery group compared to the Phase 1 surgery group (P<0.05). Postoperative tumor residual, intraoperative cerebrospinal fluid (CSF) leaks and reoperation also decreased significantly in the Phase 2 group compared to the Phase 1 group (P<0.05). Similar results were observed in patients operated using a one-hand/mono-nostril and two-hand/one-and-half nostril technique. Conclusions: Academic leave had no negative impact on most surgical outcomes for endoscopic transsphenoidal resection of pituitary tumors. Moreover, a trend toward shorter operative times and length of hospital stays was noted for patients receiving surgery immediately after surgeon's return from leave.

Psoriatic arthritis increases the risk of venous thromboembolism following degenerative lumbar spine surgery: An analysis of U.S. Nationwide Inpatient Sample 2005-2018.

Background: This retrospective study aimed to determine the risk of venous thromboembolism (VTE) in patients with PsA after surgery for lumbar degenerative disease (LDD). Methods: The study data of adults aged ≥20 years admitted to U.S. hospitals with diagnoses of LDD and undergoing spinal decompression or fusion between 2005 and 2018 were extracted from the National Inpatient Sample (NIS) database. Patients were further divided into two groups based on a diagnosis of PsA or not via codes ICD-9: 696.0 and ICD-10: L40.50. Patients with missing information were excluded. Propensity score matching (PSM) was employed to enhance comparability between groups. Logistic regression was used to determine associations between PsA and various outcomes, including complications, unfavorable discharge, and prolonged length of stay (LOS). Results: Data on 471,283 patients with LDD was extracted from the NIS database.from 2005 to 2018. Before propensity score matching, patients with PsA had higher proportions of overall morbidity (8.8 % vs. 6.9 %), VTE (1.4 % vs. 0.7 %), and unfavorable discharge (20.8 % vs. 16.9 %). After matching, patients with PsA still had higher VTE incidence and unfavorable discharge proportions. After adjustments, multivariable regression analysis indicated that patients with PsA had a higher risk of unfavorable discharge (aOR: 1.26, 95 % CI: 1.03-1.55) and VTE (aOR: 1.99, 95 % CI: 1.05-3.75). Conclusions: Among patients undergoing surgery for LDD, pre-existing PsA may be associated with increased risks of unfavorable discharge and VTE occurrence. The findings may benefit preoperative risk stratifications before LDD surgeries.

Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy.

BACKGROUND AND PURPOSE: We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). MATERIALS AND METHODS: A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. RESULTS: The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. CONCLUSION: Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.

Outcomes of hemorrhagic stroke in patients with autoimmune rheumatic diseases: An analysis of the US Nationwide Inpatient Sample.

AIM: To determine whether and how rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and systemic sclerosis (SSc) affect outcomes in patients admitted for hemorrhagic stroke. METHODS: This study screened the Nationwide Inpatient Sample database for adults aged ≥20 years admitted to US hospitals with a principal diagnosis of intracerebral hemorrhage (ICH) between 2005 and 2018. Diagnoses were determined using the International Classification of Diseases, 9th and 10th revisions (ICD-9 and ICD-10) diagnostic codes for ICH (ICD-9: 431, 432; ICD-10: I61, I62). Study outcomes were: (1) in-hospital mortality; (2) unfavorable discharge, defined as transfer to nursing homes or long-term care facilities; and (3) prolonged length of stay (LOS), defined as LOS >75th centile. RESULTS: Associations between comorbid RA, SLE, and SSc and clinical outcomes show a significantly lower risk of in-hospital mortality and prolonged LOS in RA patients. After admissions for ICH, the risk for in-hospital mortality and prolonged LOS was decreased in RA patients, and the risk for unfavorable discharge (long-term care) was reduced in SLE patients. CONCLUSIONS: Among patients admitted to US hospitals for hemorrhagic stroke, patients with RA had decreased risk for in-hospital mortality and prolonged LOS.

Augmented Reality Surgical Navigation System Integrated with Deep Learning.

Most current surgical navigation methods rely on optical navigators with images displayed on an external screen. However, minimizing distractions during surgery is critical and the spatial information displayed in this arrangement is non-intuitive. Previous studies have proposed combining optical navigation systems with augmented reality (AR) to provide surgeons with intuitive imaging during surgery, through the use of planar and three-dimensional imagery. However, these studies have mainly focused on visual aids and have paid relatively little attention to real surgical guidance aids. Moreover, the use of augmented reality reduces system stability and accuracy, and optical navigation systems are costly. Therefore, this paper proposed an augmented reality surgical navigation system based on image positioning that achieves the desired system advantages with low cost, high stability, and high accuracy. This system also provides intuitive guidance for the surgical target point, entry point, and trajectory. Once the surgeon uses the navigation stick to indicate the position of the surgical entry point, the connection between the surgical target and the surgical entry point is immediately displayed on the AR device (tablet or HoloLens glasses), and a dynamic auxiliary line is shown to assist with incision angle and depth. Clinical trials were conducted for EVD (extra-ventricular drainage) surgery, and surgeons confirmed the system's overall benefit. A "virtual object automatic scanning" method is proposed to achieve a high accuracy of 1 ± 0.1 mm for the AR-based system. Furthermore, a deep learning-based U-Net segmentation network is incorporated to enable automatic identification of the hydrocephalus location by the system. The system achieves improved recognition accuracy, sensitivity, and specificity of 99.93%, 93.85%, and 95.73%, respectively, representing a significant improvement from previous studies.

Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT.

The presence of cranial and facial bone fractures is an important finding on non-enhanced head computed tomography (CT) scans from patients who have sustained head trauma. Some prior studies have proposed automatic cranial fracture detections, but studies on facial fractures are lacking. We propose a deep learning system to automatically detect both cranial and facial bone fractures. Our system incorporated models consisting of YOLOv4 for one-stage fracture detection and improved ResUNet (ResUNet++) for the segmentation of cranial and facial bones. The results from the two models mapped together provided the location of the fracture and the name of the fractured bone as the final output. The training data for the detection model were the soft tissue algorithm images from a total of 1,447 head CT studies (a total of 16,985 images), and the training data for the segmentation model included 1,538 selected head CT images. The trained models were tested on a test dataset consisting of 192 head CT studies (a total of 5,890 images). The overall performance achieved a sensitivity of 88.66%, a precision of 94.51%, and an F1 score of 0.9149. Specifically, the cranial and facial regions were evaluated and resulted in a sensitivity of 84.78% and 80.77%, a precision of 92.86% and 87.50%, and F1 scores of 0.8864 and 0.8400, respectively. The average accuracy for the segmentation labels concerning all predicted fracture bounding boxes was 80.90%. Our deep learning system could accurately detect cranial and facial bone fractures and identify the fractured bone region simultaneously.

Augmented Reality Surgical Navigation System for External Ventricular Drain.

Augmented reality surgery systems are playing an increasing role in the operating room, but applying such systems to neurosurgery presents particular challenges. In addition to using augmented reality technology to display the position of the surgical target position in 3D in real time, the application must also display the scalpel entry point and scalpel orientation, with accurate superposition on the patient. To improve the intuitiveness, efficiency, and accuracy of extra-ventricular drain surgery, this paper proposes an augmented reality surgical navigation system which accurately superimposes the surgical target position, scalpel entry point, and scalpel direction on a patient's head and displays this data on a tablet. The accuracy of the optical measurement system (NDI Polaris Vicra) was first independently tested, and then complemented by the design of functions to help the surgeon quickly identify the surgical target position and determine the preferred entry point. A tablet PC was used to display the superimposed images of the surgical target, entry point, and scalpel on top of the patient, allowing for correct scalpel orientation. Digital imaging and communications in medicine (DICOM) results for the patient's computed tomography were used to create a phantom and its associated AR model. This model was then imported into the application, which was then executed on the tablet. In the preoperative phase, the technician first spent 5-7 min to superimpose the virtual image of the head and the scalpel. The surgeon then took 2 min to identify the intended target position and entry point position on the tablet, which then dynamically displayed the superimposed image of the head, target position, entry point position, and scalpel (including the scalpel tip and scalpel orientation). Multiple experiments were successfully conducted on the phantom, along with six practical trials of clinical neurosurgical EVD. In the 2D-plane-superposition model, the optical measurement system (NDI Polaris Vicra) provided highly accurate visualization (2.01 ± 1.12 mm). In hospital-based clinical trials, the average technician preparation time was 6 min, while the surgeon required an average of 3.5 min to set the target and entry-point positions and accurately overlay the orientation with an NDI surgical stick. In the preparation phase, the average time required for the DICOM-formatted image processing and program import was 120 ± 30 min. The accuracy of the designed augmented reality optical surgical navigation system met clinical requirements, and can provide a visual and intuitive guide for neurosurgeons. The surgeon can use the tablet application to obtain real-time DICOM-formatted images of the patient, change the position of the surgical entry point, and instantly obtain an updated surgical path and surgical angle. The proposed design can be used as the basis for various augmented reality brain surgery navigation systems in the future.

Frequency-Domain Analysis for Accurate and Robust Gait Cycle Time Detection with Clinical Data.

Gait tasks have become a topic of increasing inter-est in biological engineering research in recent years. One way to obtain the gait cycle time (GCT) is to analyze a subject's gait acceleration signal as recorded by an inertial measurement unit (IMU) [1]. An accurate peak detection of the IMU acceleration has thus become a requirement for GCT analysis. This study proposes a detection procedure for accurately detecting the peaks in a noisy IMU acceleration signal based on a frequency-domain analysis of the acceleration.

Surgical Outcome and Evaluation of Strategies in the Management of Growth Hormone-Secreting Pituitary Adenomas After Initial Transsphenoidal Pituitary Adenectomy Failure.

Acromegaly is a systemic disease that requires multidisciplinary treatment to achieve the best clinical outcome. This study aimed to evaluate the outcomes of the endoscopic transsphenoidal approach (TSA) as the primary treatment for somatotroph adenomas and further investigate patients who had suboptimal surgical results. This retrospective study included 83 patients with somatotroph adenomas treated by TSA at our institution from 1999 to 2010. Biochemical remission was defined as hGH <1 and <2.5 ng/ml. Factors associated with failure of TSA and strategy of secondary treatments for refractory and recurrent disease were analyzed. The mean age of patients was 41.1 ± 11.3 years, and the mean follow-up time was 54.2 ± 44.3 months. Approximately 44.5% of patients had residual tumors after TSA. Larger tumor size, higher GH level before the operation, and the existence of residual tumors were associated with TSA failure. Forty-one patients had an inadequate response to TSA or a recurrent lesion, and of these patients, 37 had residual tumor after TSA. Octreotide results in good outcomes in the treatment of DGSA patients, and SRS/EXRT generates good results in treating patients who receive second treatments when remission cannot be reached 6 months after TSA operation.

Deep Learning-Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke.

Brain computed tomography (CT) is commonly used for evaluating the cerebral condition, but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled neuroradiologists. Deep learning networks are commonly employed for medical image analysis because they enable efficient computer-aided diagnosis. This study proposed the use of convolutional neural network (CNN)-based deep learning models for efficient classification of strokes based on unenhanced brain CT image findings into normal, hemorrhage, infarction, and other categories. The included CNN models were CNN-2, VGG-16, and ResNet-50, all of which were pretrained through transfer learning with various data sizes, mini-batch sizes, and optimizers. Their performance in classifying unenhanced brain CT images was tested thereafter. This performance was then compared with the outcomes in other studies on deep learning-based hemorrhagic or ischemic stroke diagnoses. The results revealed that among our CNN-2, VGG-16, and ResNet-50 analyzed by considering several hyperparameters and environments, the CNN-2 and ResNet-50 outperformed the VGG-16, with an accuracy of 0.9872; however, ResNet-50 required a longer time to present the outcome than did the other networks. Moreover, our models performed much better than those reported previously. In conclusion, after appropriate hyperparameter optimization, our deep learning-based models can be applied to clinical scenarios where neurologist or radiologist may need to verify whether their patients have a hemorrhage stroke, an infarction, and any other symptom.

Using a consensus acupoints regimen to explore the relationship between acupuncture sensation and lumbar spinal postoperative analgesia: A retrospective analysis of prospective clinical cooperation.

OBJECTIVE: This study evaluated the effectiveness of acupuncture treatment on postoperative pain in patients with degenerative lumbar spine disease, and explored the relationship between the postoperative analgesic effect of acupuncture and the sensation of acupuncture experienced by the patients. METHODS: This retrospective study analyzed the medical records of 97 patients who had undergone an operation by the same surgeon due to degenerative lumbar disease. These patients were divided into acupuncture group (n = 32), patient-controlled analgesia (PCA) group (n = 27), and oral analgesia group (n = 38) according to the different postoperative analgesic methods. During their hospitalization, patients completed daily evaluations of their pain using a visual analogue scale (VAS), and injection times of supplemental meperidine were recorded. Also, the Chinese version of the Massachusetts General Hospital Acupuncture Sensation Scale (C-MASS) was used in the acupuncture group. RESULTS: Each of the three treatment groups showed significant reductions in postoperative pain, as shown by reduced VAS scores. The acupuncture group, however, had less rebound pain (P < 0.05) than the other two groups. Both the acupuncture and PCA groups experienced acute analgesic effects that were superior to those in the oral analgesia group. In addition, the higher the C-MASS index on the second day after surgery, the lower the VAS score on the fourth day after surgery. There was also a significant difference in the "dull pain" in the acupuncture sensation. CONCLUSION: The results demonstrated that acupuncture was beneficial for postoperative pain and discomfort after simple surgery for degenerative spinal disease. It is worth noting that there was a disproportionate relevance between the patient's acupuncture sensation and the improvement of pain VAS score.

A Robust, Fully Automatic Detection Method and Calculation Technique of Midline Shift in Intracranial Hemorrhage and Its Clinical Application.

A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications of the fully automatic model. We recruited 300 consecutive non-contrast CT scans consisting of 7269 slices in this study. Six different types of hemorrhage were included. The automatic detection of MLS was based on modified Keypoint R-CNN with keypoint detection followed by training on the ResNet-FPN-50 backbone. The results were further compared with manually drawn outcomes and manually defined keypoint calculations. Clinical parameters, including Glasgow coma scale (GCS), Glasgow outcome scale (GOS), and 30-day mortality, were also analyzed. The mean absolute error for the automatic detection of an MLS was 0.936 mm compared with the ground truth. The interclass correlation was 0.9899 between the automatic method and MLS drawn by different clinicians. There was high sensitivity and specificity in the detection of MLS at 2 mm (91.7%, 80%) and 5 mm (87.5%, 96.7%) and MLSs greater than 10 mm (85.7%, 97.7%). MLS showed a significant association with initial poor GCS and GCS on day 7 and was inversely correlated with poor 30-day GOS (p < 0.001). In conclusion, automatic detection and calculation of MLS can provide an accurate, robust method for MLS measurement that is clinically comparable to the manually drawn method.

Survival after multiple nail gun injuries to the brain, lung, and heart: a case report and a review of the literature.

Most nail gun injuries involve the extremities and result from work-related accidents. Injuries to the brain or thorax are relatively rare, and cases with injuries to both regions are even rarer and often lethal. Initial evaluation, resuscitation, and surgical planning can be challenging for emergency physicians and surgeons. We present the details of a man with multiple nail gun injuries to the brain, lung, and heart following a suicide attempt. The patient presented to the emergency department in shock. After immediate resuscitation, emergent sternotomy, and subsequent craniotomy, he was discharged without significant morbidity. According to the literature, this is the only reported case involving multiple nail gun injuries to the brain, lungs, and heart. The mortality rate of multiple nail gun injuries involving the head and chest is approximately 20%. Rapid evaluation, immediate resuscitation, and appropriate imaging and surgery are crucial for increasing survival and achieving a good prognosis. Emergency sternotomy for cardiac injury is the foremost priority, and the timing of craniotomy depends on the patient's vital sign status and whether brain injury is evident.A preprint of this article is available online: DOI: 10.21203/rs.3.rs-35448/v1.

Endoscopic transsphenoidal surgery for resection of pituitary macroadenoma: A retrospective study.

BACKGROUND: The endoscopic transsphenoidal approach is an efficient minimally invasive procedure for removal of pituitary tumors that can be accomplished through a one-hand or two-hand approach. The one-hand procedure through one nostril is more intuitive for surgeons, but maneuvering the instruments can be restrictive. The two-hand procedure using a one-and-half nostril approach provides more precise manipulation. This study aimed to compare the surgical outcomes of one-hand/mono-nostril and two-hand/one-and-half nostril surgeries for resection of large pituitary tumors by a single neurosurgeon. MATERIALS AND METHODS: The surgical data of 78 consecutive cases with pituitary macroadenoma (diameter >1 cm) were reviewed retrospectively. Altogether, 30 cases received one-hand/mono-nostril surgery, while 48 cases received two-hand/one-and-half nostril surgery. Postoperative outcomes of the two operations were compared. RESULTS: The operative time, hospital stay, residual rate of pituitary macroadenoma, visual field, surgical complications, and re-operative rates were slightly improved in the two-hand/one-and-half nostril surgery group compared with that in the one-hand/mono-nostril surgery group (all p>0.05). However, postoperative hypopituitarism was less frequent (1/48; 2.0%) with the two-hand/one-and-half nostril approach than with the mono-nostril approach (p = 0.004). Similar surgical outcomes were found in all patients with either small or large pituitary tumors, except that the difference in postoperative improvement in visual field change reached statistical significance (p = 0.044). CONCLUSION: A single-surgeon endoscopic endonasal transsphenoidal surgery with two-hand/one-and-half nostril approach is an effective and safe procedure for removal of large pituitary tumors.

Aggressive cytoreduction and multiple subpial cortical transections may obtain good surgical outcomes in refractory epilepsy with multiple epileptic foci.

BACKGROUNDS: Epilepsy surgery is the most efficacious therapeutic modality for patients with medical refractory epilepsy, especially resective surgery. However, the variable etiologies and multiple epileptic foci are usually associated with the outcomes. The aim of this study was to demonstrate that combination of different intervention procedures might be an alternative option for patients of refractory epilepsy. METHODS: We retrospectively analyzed pre-operative and post-surgical outcomes in 30 patients who received epilepsy surgery between January 1, 2010 and December 31, 2014 at Chang Gung Memorial Hospital (CGMH), Linkou, according to Engel's classification. RESULTS: Twenty-six of the 30 patients (86.7%) had good outcomes, sum of class I and class II after epilepsy surgery. The good outcome rate of our complicated group was 80.0% (12/15), compared to 93.3% (14/15) in the simple group, but no significant differences between the two groups (p = 0.569). Four patients whose epileptic foci involved eloquent area and received multiple subpial cortical transection, and good outcome rate was 75% (3/4). At last, six patients had previously failed epilepsy surgery and received a reoperation, with a good outcome rate of 83.3% (5/6). CONCLUSION: After complete pre-surgical evaluation and combined interventional procedures, the patients with refractory epilepsy had satisfactory outcomes and few neurological complications. Moreover, re-operation can improve the outcome in some patients who previously failed epilepsy surgery.

A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI.

The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009-2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017-2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival (p = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5-82.5%, AUC = 0.83-0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning.

Inferior Vena Cava Volume Is an Independent Predictor of Massive Transfusion in Patients With Trauma.

OBJECTIVES: Early adequate resuscitation of patients with trauma is crucial in preventing shock and early mortality. Thus, we aimed to determine the performance of the inferior vena cava (IVC) volume and other risk factors and scores in predicting massive transfusion and mortality. METHODS: We included all patients with trauma who underwent computed tomography (CT) scan of the torso, which included the abdominal area, in our emergency department (ED) from January 2014 to January 2017. We calculated the 3-dimensional IVC volume from the left renal vein to the IVC bifurcation. The primary outcome was the performance of IVC volume in predicting massive transfusion, and the secondary outcome was the performance of IVC volume in predicting 24-hour and 30-day in-hospital mortality. RESULTS: Among the 236 patients with trauma, 7.6% received massive transfusions. The IVC volume and revised trauma score (RTS) were independent predictors of massive transfusion (adjusted odds ratio [OR]: 0.79 vs 1.86, 95% confidence interval [CI], 0.71-0.89 vs 1.4-2.47, respectively). Both parameters showed the good area under the curve (AUC) for the prediction of massive transfusion (adjusted AUC: 0.83 and 0.82, 95% CI, 0.74-0.92 vs 0.72-0.93, respectively). Patients with a large IVC volume (fourth quartile) were less likely to receive massive transfusion than those with a small IVC volume (first quartile, ≥28.29 mL: 0% vs <15.08 mL: 20.3%, OR: 0.13, 95% CI, 0.03-0.66). CONCLUSIONS: The volume of IVC measured on CT scan and RTS are independent predictors of massive transfusion in patients with trauma in the ED.

Three-Dimensional Volumetric Changes and Clinical Outcomes after Decompression with DIAM™ Implantation in Patients with Degenerative Lumbar Spine Diseases.

Background and objectives: The prevalence of degenerative lumbar spine diseases has increased. In addition to standard lumbar decompression and/or fusion techniques, implantation of interspinous process devices (IPDs) can provide clinical benefits in highly selected patients. However, changes in spinal structures after IPD implantation using magnetic resonance imaging (MRI) have rarely been discussed. This volumetric study aimed to evaluate the effect of IPD implantation on the intervertebral disc and foramen using three-dimensional assessment. Materials and Methods: We retrospectively reviewed patients with lumbar degenerative disc diseases treated with IPD implantation and foraminotomy and/or discectomy between January 2016 and December 2019. The mean follow-up period was 13.6 months. The perioperative lumbar MRI data were processed for 3D-volumetric analysis. Clinical outcomes, including the Prolo scale and visual analog scale (VAS) scores, and radiographic outcomes, such as the disc height, foraminal area, and translation, were analyzed. Results: Fifty patients were included in our study. At the one-year follow-up, the VAS and Prolo scale scores significantly improved (both p < 0.001). The disc height and foraminal area on radiographs also increased significantly, but with limited effects up to three months postoperatively. MRI revealed an increased postoperative disc height with a mean difference of 0.5 ± 0.1 mm (p < 0.001). Although the mean disc volume difference did not significantly increase, the mean foraminal volume difference was 0.4 ± 0.16 mm3 (p < 0.05). Conclusions: In select patients with degenerative disc diseases or lumbar spinal stenosis, the intervertebral foramen was enlarged, and disc loading was reduced after IPD implantation with decompression surgery. The 3D findings were compatible with the clinical benefits.

Publisher Correction: A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.