AIMC Topic:
Prostatic Neoplasms

Clear Filters Showing 1281 to 1290 of 1323 articles

Deep Learning Role in Early Diagnosis of Prostate Cancer.

Technology in cancer research & treatment
The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted ma...

Impact of metformin on serum prostate-specific antigen levels: Data from the national health and nutrition examination survey 2007 to 2008.

Medicine
PURPOSE: A possible association between metformin use and the development of prostate cancer (PCa) has been reported. However, there is limited information on the impact of long-term metformin use on serum prostate-specific antigen (PSA) levels. We i...

Unexpected hemorrhage of a rare vessel, a pubic branch of the external iliac artery, after laparoscopic radical prostatectomy: Case report.

Medicine
RATIONALE: Postoperative hemorrhage is a rare complication after laparoscopic radical prostatectomy (LRP), with no case reports of bleeding from the external iliac artery in the literature.

[Downregulation of PTTG1 expression inhibits the proliferation and invasiveness and promotes the apoptosis of human prostate cancer LNCaP-AI cells].

Zhonghua nan ke xue = National journal of andrology
OBJECTIVE: To investigate the effects of down-regulation of PTTG1 expression on the proliferation, invasiveness and apoptosis of androgen-independent human prostate cancer LNCaP-AI cells and their sensitivity to androgen antagonists.

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.

Journal of biomedical optics
We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason gradi...

MRI-based prostate cancer detection with high-level representation and hierarchical classification.

Medical physics
PURPOSE: Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation constructing hierarchical classification to refine the detection results.