Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework-Reference-Cited by-同舟云学术

Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework

Published:2021-10 Issue: Volume:73 Page:102153
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Mehta Pritesh,Antonelli Michela,Ahmed Hashim U.,Emberton Mark,Punwani Shonit,Ourselin Sébastien

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference55 articles.

1. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study;Ahmed;Lancet,2017

2. PI-RADS version 2;American College of Radiology,2015

3. Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists;Antonelli;Eur. Radiol.,2019

4. Computed diffusion-weighted MR imaging may improve tumor detection;Blackledge;Radiology,2011

5. Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values;Bonekamp;Radiology,2018

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