Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements.

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression-0.88, for decision tree classifier-0.78 and for the dense neural network-0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.

Prognostic markers and new, innovative treatments in renal cell carcinoma.

We reviewed the current medical literature to present the prognostic markers and current treatments of Renal Cell Carcinoma (RCC) and to propose new and innovative treatments for RCC. A PubMed search (http://www.ncbi.nlm.nih.gov/PubMed/) was performed and prognostic markers in RCC were reviewed and presented here. We hypothesize our own innovative theoretical research in new treatments for RCC. This includes a combination formed of an anticancer vaccine (VAX-x), which prevents the emergence of drug-resistance to (DRUG-x), with its corresponding anticancer drug (DRUG-x), administrated after immunological response to (VAX-x), can be employed to successfully treat and cure RCC in its various stages. We therefore propose that new treatment options of RCC are possibly VAX-x with either of the tyrosine kinase inhibitors-sunitinib or sorafenib.

Prognostic markers and targeted therapies for renal cell carcinoma.

Management of advanced renal cell carcinoma remains a persistent clinical challenge with high morbidity and mortality for a large proportion of patients. Until recently, available medical immunotherapy regimens yielded a therapeutic response in only 20% of patients. Advances in the understanding of molecular mechanisms of renal cell carcinoma have led to a rapidly expanding body of work exploring biomarkers for the disease and targeted therapeutics. We review current investigations into biomarkers and novel therapies for renal cell carcinoma, discuss the concept of anticancer vaccines, and propose a novel target for anticancer vaccine development.