This report shows that robotic surgery can be used for safe removal of a large renal tumor in a minimally invasive fashion, maximizing preservation of renal function, and without compromising cancer control.

, "Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging," J. Med. Imag. 5(1), 011004 (2017), doi: 10.1117/1.JMI.5.1.011004. Abstract. Multiparametric magnetic resonance imaging (MP-MRI), including diffusion-weighted imaging, is commonly used to diagnose prostate cancer. This radiology-pathology study correlates prostate cancer grade and morphology with common b-value combinations for calculating apparent diffusion coefficient (ADC). Thirty-nine patients undergoing radical prostatectomy were recruited for MP-MRI prior to surgery. Diffusion imaging was collected with seven b-values, and ADC was calculated. Excised prostates were sliced in the same orientation as the MRI using 3-D printed slicing jigs. Whole-mount slides were digitized and annotated by a pathologist. Annotated samples were aligned to the MRI, and ADC values were extracted from annotated peripheral zone (PZ) regions. A receiver operating characteristic (ROC) analysis was performed to determine accuracy of tissue type discrimination and optimal ADC b-value combination. ADC significantly discriminates Gleason (G) G4-5 cancer from G3 and other prostate tissue types. The optimal b-values for discriminating high from low-grade and noncancerous tissue in the PZ are 50 and 2000, followed closely by 100 to 2000 and 0 to 2000. Optimal ADC cut-offs are presented for dichotomized discrimination of tissue types according to each b-value combination. Selection of b-values affects the sensitivity and specificity of ADC for discrimination of prostate cancer.

Purpose:The standard treatment for organ-confined prostate cancer (PC) is surgery or radiation, and locally advanced PC is typically treated with radiotherapy alone or in combination with androgen deprivation therapy.Here, we investigated whether Stat5a/b participates in regulation of double strand DNA break repair in PC, and whether Stat5 inhibition may provide a novel strategy to sensitize PC to radiation therapy.Experimental Design: Stat5a/b regulation of DNA repair in PC was evaluated by comet and clonogenic TRANSLATIONAL RELEVANCERadiation therapy is a key treatment option for both organ-confined and locally advanced prostate cancer (PC). However, irradiation is often associated with significant toxicities to the neighboring tissues, which can cause debilitating side-effects. In the present study, we demonstrated proof-ofconcept that targeting Stat5a/b signaling sensitizes PC to radiation through regulation of DNA repair.Our results provide, for the first time, mechanistic evidence that Jak2-Stat5a/b signaling is critical for Rad51 expression and Homologous-Recombination DNA repair in PC. Using human PC cell lines, xenograft tumors and ex vivo culture of clinical PCs, we show that genetic or pharmacological inhibition of Stat5a/b sensitizes PC to irradiation while not affecting the radiation sensitivity of the surrounding tissues. These findings provide a strong rationale for development of Stat5a/b inhibitors as adjuvant therapy for radiation treatment of PC.Research.on April 28,

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board–approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging.

Purpose:This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization.Methods and Materials:Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer.Results:The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer.Conclusions:We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy. © 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).