AIMC Topic: Prostatic Neoplasms

Clear Filters Showing 961 to 970 of 1447 articles

Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11.

European urology oncology
BACKGROUND: Owing to the large variation in treatment response among patients with high-risk prostate cancer, it would be of value to use objective tools to monitor the status of bone metastases during clinical trials. Automated Bone Scan Index (aBSI...

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detecti...

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

Virchows Archiv : an international journal of pathology
Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate b...

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound.

IEEE transactions on medical imaging
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missin...

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.

Journal of biomedical informatics
OBJECTIVE: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated...

A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.

Medical physics
PURPOSE: Real-time image-guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three-dimensional (3D) motion based on two-dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior...

Prostate cancer detection using residual networks.

International journal of computer assisted radiology and surgery
PURPOSE: To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI).