PSA density as a parameter in prostate biopsy decision of patients with prostate sized 80 mL or larger

Background: Patients with high prostate volume (>80 ml) and high PSA levels make it difficult to decide on prostate biopsy. In this study, author aimed to detect of predictive factors to distinguish malignant or benign prostatic lesions in patients with prostate size over 80 ml.Methods: A total of 299 patients underwent TRUSBP at the clinics between 2012-2017. Cases with prostate volume over 80 ml were divided into groups according to the pathology by benign (group 1) or malign (group 2). Author evaluated the predictive factors in two groups. Patient’s age, grading and findings of digital rectal examination, prostate volume, number of received cores, total (tPSA) and free PSA (fPSA) before biopsy, rate of percentage of free to total prostate specific antigen (f/tPSA) and PSA density was compared in both groups.Results: Benign prostate hyperplasia was detected in 217 patients (72.58%) and prostate adenocarcinoma was detected in 82 patients (27.42%). The patient’s age, tPSA, fPSA and PSA density were 63.81 years, 9.71 ng/ml, 1.78 ng/ml and 0.10 g/ml2 in group 1 and 69.10 years, 38.32 ng/ml, 5.86 ng/ml and 0.42 ng/ml2 respectively. Patient’s age, tPSA, fPSA and PSA density was statistically significant between in two groups (p80 ml) has a significant influence in PSA values and results of the biopsy, PSA density is extremely important in performing prostate biopsy decisions

What should be the PSA threshold value? 2.5 or 4 ng/mL?

Background: In this study, author aimed to detect of threshold value of prostate-specific antigen (PSA) to distinguish malignant or benign prostatic lesions in PSA evaluation.Methods: A total of 61 patients underwent TRUSBP due to high PSA values (2.5-4 ng/mL) at the clinic between 2012-2017. Digital rectal examinations of all patients were normal. Cases with PSA elevation were divided into groups according to the pathology by benign (group 1) or malign (group 2). Author evaluated the predictive factors with the exception of digital rectal examination findings in two groups.Results: Benign prostate hyperplasia was detected in 35 patients (57.4%) and prostate adenocarcinoma was detected in 26 patients (42.6%). The patient’s age, tPSA, fPSA and PSA density were 62.07 years, 3.55 ng/mL, 0.65 ng/mL and 0.09 ng/ml2 in group 1 and 58.54 years, 3.55 ng/mL, 0.74 ng/mL and 0.10 ng/ml2 in group 2, respectively. Patient’s age was statistically significant between in two groups (p<0.05). Number of received cores and rate of f/tPSA were 12.24-12 and 20.51-18.45% in group 1 and 2, respectively. tPSA, fPSA and PSA density, number of received cores and rate of f/tPSA were similar in both groups. In group 2, prostate adenocarcinoma was most common detected with Gleason score 3+3 in 19 of 26 patients (73.1%).Conclusions: There is a need different assessment to distinguish of malignant lesions from benign lesions. Nowadays, it was impossible to make this difference in patients without digital rectal examination findings, so accepted threshold of PSA should be 2.5 ng/mL

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions

Does the experience of the bedside assistant effect the results of robotic surgeons in the learning curve of robot assisted radical prostatectomy?

ABSTRACT Introduction: The success of the robot assisted radical prostatectomy (RARP) procedures depend on a successful team, however the literature focuses on the performance of a console surgeon. The aim of this study was to evaluate surgical outcomes of the surgeons during the learning curve in relation to the bedside assistant's experience level during RARP. Materials and Methods: We retrospectively reviewed two non - laparoscopic, beginner robotic surgeon's cases, and we divided the patients into two groups. The first surgeon completed the operations on 20 patients with a beginner bedside assistant in February - May 2009 (Group-1). The second surgeon completed operations on 16 patients with an experienced (at least 150 cases) bedside assistant in February 2015 - December 2015 (Group-2). The collected data included age, prostate volume, prostate specific antigen (PSA), estimated blood loss, complications and percent of positive surgical margins. In addition, the elapsed time for trocar insertion, robot docking, console surgery, specimen extraction and total anesthesia time were measured separately. Results: There were no significant differences between the groups in terms of age, comorbidity, prostate volume, PSA value, preoperative Gleason score, number of positive cores, postoperative Gleason score, pathological grade, protection rate of neurovascular bundles, surgical margin positivity, postoperative complications, length of hospital stay, or estimated blood loss. The robot docking, trocar placement, console surgery, anesthesia and specimen extraction times were significantly shorter in group 2 than they were in group 1 (17.75 ± 3.53 min vs. 30.20 ± 7.54 min, p ≤ 0.001; 9.63 ± 2.71 min vs. 14.40 ± 4.52 min, p = 0.001; 189.06 ± 27.70 min vs. 244.95 ± 80.58 min, p = 0.01; 230.94 ± 30.83 min vs. 306.75 ± 87.96 min, p = 0.002; 10.19 ± 2.54 min vs. 17.55 ± 8.79 min, p = 0.002; respectively). Conclusion: Although the bedside assistant's experience in RARP does not appear to influence the robotic surgeon's oncological outcomes during the learning curve, it may reduce the potential complications by shortening the total operation time

Serum Lipid, Lipoprotein and Oxidatively Modified Low Density Lipoprotein Levels in Active or Inactive Patients with Behçet's Disease

Aim: To determine serum lipid, lipoproteins and oxidized low density lipoprotein (oxLDL) levels in Behçet′s disease (BD) and to evaluate the relationship of these parameters with the clinical activity of the disease. Materials and Methods: Sixty-two patients (25 active, 37 inactive) and -26 healthy controls were included in the study. We measured serum oxLDL levels using the enzyme-linked immunosorbent assay method, and serum total cholesterol (TC), triglyceride (TG) and high density lipoprotein-cholesterol (HDL-C) levels by spectrophotometric method. Results: Serum TG (108±70 mg/dL and 79±40 mg/dL, respectively; P<0.05), LDL-C (124±35 mg/dL and 108±26 mg/dL, respectively; P<0.05) and oxLDL (65±19 U/L and 53±10 U/L, respectively; P<0.01) levels were significantly higher in patients than in controls, but HDL-C levels were significantly lower in patients than in controls (39±11 mg/dL and 50±13 mg/dL, respectively; P<0.05). The levels of oxLDL in patients were found to correlate with those of TC and LDL-C. Neither the lipid parameters nor the oxLDL levels in the patients with active disease (n=25) were different than those in the patients who were in inactive stage (n=37). Serum levels of oxLDL in the patients with active and inactive disease were significantly higher than those in controls (66±19 U/L, 65±19 U/L, and 53±10 U/L, respectively; P<0.05). Conclusions: We conclude that the increase of TG, LDL-C and oxLDL levels and the decrease of HDL-levels may indicate that there is a tendency to atherothrombotic process in patients with BD. Inflammation and immunologic reactions in BD may be caused by a response to elevated oxLDL. TG, LDL-C and oxLDL are not useful markers for the severity of the disease activity

Normalizing Flows for Out-of-Distribution Detection: Application to Coronary Artery Segmentation

Coronary computed tomography angiography (CCTA) is an effective imaging modality, increasingly accepted as a first-line test to diagnose coronary artery disease (CAD). The accurate segmentation of the coronary artery lumen on CCTA is important for the anatomical, morphological, and non-invasive functional assessment of stenoses. Hence, semi-automated approaches are currently still being employed. The processing time for a semi-automated lumen segmentation can be reduced by pre-selecting vessel locations likely to require manual inspection and by submitting only those for review to the radiologist. Detection of faulty lumen segmentation masks can be formulated as an Out-of-Distribution (OoD) detection problem. Two Normalizing Flows architectures are investigated and benchmarked herein: a Glow-like baseline, and a proposed one employing a novel coupling layer. Synthetic mask perturbations are used for evaluating and fine-tuning the learnt probability densities. Expert annotations on a separate test-set are employed to measure detection performance relative to inter-user variability. Regular coupling-layers tend to focus more on local pixel correlations and to disregard semantic content. Experiments and analyses show that, in contrast, the proposed architecture is capable of capturing semantic content and is therefore better suited for OoD detection of faulty lumen segmentations. When compared against expert consensus, the proposed model achieves an accuracy of 78.6% and a sensitivity of 76%, close to the inter-user mean of 80.9% and 79%, respectively, while the baseline model achieves an accuracy of 64.3% and a sensitivity of 48%