Machine learning-based prediction of upgrading on magnetic resonance imaging targeted biopsy in patients eligible for active surveillance
- 17 March 2022
- journal article
- research article
- Published by Elsevier in Urologic Oncology: Seminars and Original Investigations
- Vol. 40 (5) , 191.e15-191.e20
Abstract
No abstract availableKeywords
This publication has 10 references indexed in Scilit:
- Trends in the use of active surveillance and treatments in Medicare beneficiaries diagnosed with localized prostate cancerUrologic Oncology: Seminars and Original Investigations, 2021
- Molecular Biomarkers in Localized Prostate Cancer: ASCO GuidelineJournal of Clinical Oncology, 2020
- 17-Gene Genomic Prostate Score Test Results in the Canary Prostate Active Surveillance Study (PASS) CohortJournal of Clinical Oncology, 2020
- Barriers to obtaining prostate multi‐parametric magnetic resonance imaging in African‐American men on active surveillance for prostate cancerCancer Medicine, 2019
- Association Between Radiation Therapy, Surgery, or Observation for Localized Prostate Cancer and Patient-Reported Outcomes After 3 YearsJAMA, 2017
- 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate CancerNew England Journal of Medicine, 2016
- Practical Barriers to Obtaining Pre-Biopsy Prostate MRI: Assessment in Over 1,500 Consecutive Men Undergoing Prostate Biopsy in a Single Urologic PracticeUrologia Internationalis, 2016
- SMOTE: Synthetic Minority Over-sampling TechniqueJournal of Artificial Intelligence Research, 2002
- Random ForestsMachine Learning, 2001
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997