Does larger prostate size provide protection for cancer specific outcomes in localized prostate cancer.

OBJECTIVE: Benign prostatic hyperplasia is common in the aging population and frequently comorbid with localized prostate cancer. Large prostate volume places significant challenges in robotic prostatectomy including reduced mobility and visualization. The goal of this study is to evaluate the effect of prostate volume as a continuous variable on cancer specific outcomes. METHODS: Three thousand four hundred and twenty five patients with localized prostate cancer at a single institution who underwent robotic prostatectomy were retrospectively reviewed. A number of preoperative, operative, and postoperative variables were collected to evaluate cancer specific outcomes including pathologic stage, tissue margins, and biochemical recurrence (BCR). Logistic regression models and univariate and multivariate analyses were implemented for pathologic stage T3 and BCR respectively. RESULTS: The median follow up time was 52 months (IQR 18-95). 37.4% of the patients had a final pathologic stage of T3 or higher, 21.2% experienced positive surgical margins, and 24.7% of patients experienced BCR. Prostate size was a significant predictor of all three outcomes of interest. Increasing prostate size was protective against both higher pathologic stage and positive surgical margins (odds ratio = 0.989, 0.990 respectively, p < 0.001). There was a modest increase in the risk of BCR with increasing gland size (hazard ratio = 1.006, p < 0.001). These results were most significant for patients with Gleason Grade Groups 1 and 2 prostate cancer. CONCLUSION: Prostate size is a commonly determined clinical factor that effects both surgical planning and cancer specific outcomes. Increasing prostate size may offer protection against higher stage disease and positive surgical margins. While surgically challenging, favorable oncologic outcomes can be consistently achieved for patients with low-intermediate risk disease.

Comparison of Perioperative Outcomes and Clinical Characteristics of Calcium, Matrix and Struvite Stones From a Single Institution.

OBJECTIVE: To define risk factors and perioperative outcomes for matrix stones and compare these outcomes with struvite and calcium stone cohorts. METHODS: A retrospective cohort study comparing matrix stones (n=32), struvite stones (n=23) and a matched, calcium stone control group (n=32) was performed. Two-way ANOVA was used to compare the groups for continuous variables. Chi-square tests were used to compare categorical variables. Significance was set at P <.05. All statistical tests were performed using R (v1.73). RESULTS: We identified no differences in age, gender, or BMI between the three groups. Matrix and struvite stones were more likely to have a history of prior stone surgery and recurrent UTIs compared to calcium stones (P=.027 and P <.001, respectively). Struvite stones were more likely to present as staghorn calculi compared to matrix or calcium stones (56.5% vs 21.7% vs 18.8%, P=.006). There were no significant differences in postoperative stone free rates (P=.378). No significant differences in postoperative infectious complications were identified. Matrix stones were more likely to have Candida on stone culture compared to the struvite or calcium stones (P <.0001). CONCLUSION: Matrix and struvite stones were more likely have a history of stone surgery and preoperative recurrent UTIs. Struvite stones were more likely to present as staghorn calculi. Matrix stones were more likely to have Candida present in stone cultures. However, no difference in postoperative infectious outcomes or stone free rates were identified. Further study with larger cohorts is necessary to distinguish matrix stone postoperative outcomes from struvite and calcium stones.

A case of ureteral fungal mass removal in a patient taking empagliflozin.

With increased use of sodium-glucose co-transporter 2 (SGLT2) inhibitors as antidiabetic agents, the risk of serious fungal urinary tract infection (UTI) may be increased. We present the case of a 67-year-old Caucasian female who was admitted for emphysematous pyelitis and found to have a fungal ball in the renal pelvis. Candida glabrata was cultured and the patient was managed with percutaneous nephrostomy tube placement and antifungal treatment. The fungal ball persisted and required surgical removal with ureteroscopy and basket extraction. Fungal balls can be a difficult sequelae of UTIs requiring a combination of antifungal and surgical intervention for definitive management.

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.

Segmentation of abdominal organs in computed tomography using a generalized statistical shape model.

Segmentation of anatomical structures in computed tomography images remains an important stage in computer-aided diagnostics and therapy. Due to the complexity of anatomical structures in the abdominal cavity, the occurrence of anatomical variants and pathological changes of organs in computed tomography images, segmentation is still treated as a current research problem. The paper presents the segmentation method based on the generalized statistical shape model. The method was tested in the application to segmentation based on 40 cases of computed tomography with contrast: 20 cases were included in training set and 20 in the testing set. For each case, expert outlines were made for the following organs: spleen, kidney, liver, pancreas, and duodenum. The following average results of the DICE coefficient were obtained: 0.96, 093, 0.88, 0.86, 0.81. The obtained results on the developed method can be treated as a step towards a universal method of segmentation in normalized scaled images, because the method does not require the selection of new parameter values when applied to the segmentation of a diverse group of parenchymal anatomical organs.

Supporting diagnostics and therapy planning for percutaneous ablation of liver and abdominal tumors and pre-clinical evaluation.

Percutaneous ablation methods are used to treat primary and metastatic liver tumors. Image guided navigation support minimally invasive interventions of rigid anatomical structures. When working with the displacement and deformation of soft tissues during surgery, as in the abdomen, imaging navigation systems are in the preliminary implementation stage. In this study a multi-stage approach has been developed to support percutaneous liver tumors ablation. It includes CT image acquisition protocol with the amplitude of respiratory motion that yields images subjected to a semi-automatic method able to deliver personalized abdominal model. Then, US probe and ablation needle calibration, as well as patient position adjustment method during the procedure for the preoperative anatomy model, have been combined. Finally, an advanced module for fusion of the preoperative CT with intraoperative US images was designed. These modules have been tested on a phantom and in the clinical environment. The final average Spatial calibration error was 1,7 mm, the average error of matching the position of the markers was about 2 mm during the entire breathing cycle, and average markers fusion error 495 mm. The obtained results indicate the possibility of using the developed method of navigation in clinical practice.