6 research outputs found
Alternate Cervical Venous Access Sites for Implantable Port Catheters: Experience at a Single Quaternary Care Institution
IntroductionClinical outcomes of implantable port catheters (IPCs) placed via alternative veins such as the external jugular and cervical collaterals have not been well established. This investigation evaluates the short- and long-term outcomes of IPCs inserted via alternate cervical veins (ACV) compared to traditionally inserted IPCs via the internal jugular vein (IJV).Materials and methodsA total of 24 patients who received an IPC between 2010 and 2020 via an ACV-defined as the external jugular vein, superficial cervical vein, or unnamed collateral veins-were identified. Based on power analysis, a matched control group of 72 patients who received IPCs via the IJV was identified. Non-inferiority analysis for port complications was performed between the two groups based on the selected non-inferiority margin of 20%. Secondary end points included complication-free survival and comparison of complications by the time at which they occurred.ResultsACV access was non-inferior to traditional access for overall complications. Alternate access resulted in fewer complications than traditional access with an estimated reduction of - 7.0% [95% CI - 23.6%, 39.7%]. There was no significant difference in peri-procedural and post-procedural complications between the two groups. Complication-free survival was also equivalent between the two groups.ConclusionIPC placement via ACVs was non-inferior to IPCs placed via traditional access through the IJV. When abnormal pathology obviates the use of IJV access, other cervical veins may be considered prior to seeking alternate locations such as femoral, translumbar, inferior vena cava, and hepatic veins
Patterns of RAPD markers and heavy metal concentrations in Perna viridis (L.), collected from metal-contaminated and uncontaminated coastal waters: Are they correlated with each other?
Prolonged Blood Storage Does Not Effect Survival in an Animal Model of Hemorrhagic Shock
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AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.
Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations