Published December 2018
| Version v1
Journal article
Development of a Ready-to-Use Graphical Tool Based on Artificial Neural Network Classification: Application for the Prediction of Late Fecal Incontinence After Prostate Cancer Radiation Therapy
Creators
- 1. Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan (Italy)
- 2. Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan (Italy)
- 3. Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan (Italy)
Description
This study was designed to apply artificial neural network (ANN) classification methods for the prediction of late fecal incontinence (LFI) after high-dose prostate cancer radiation therapy and to develop a ready-to-use graphical tool.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.ijrobp.2018.07.2014Additional details
Identifiers
- DOI
- 10.1016/j.ijrobp.2018.07.2014;
- PII
- S0360301618334825;
Publishing Information
- Journal Title
- International Journal of Radiation Oncology, Biology and Physics
- Journal Volume
- 102
- Journal Issue
- 5
- Journal Page Range
- p. 1533-1542
- ISSN
- 0360-3016
- CODEN
- IOBPD3
INIS
- Country of Publication
- United States
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 52123534
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- CLASSIFICATION; FORECASTING; NEOPLASMS; NEURAL NETWORKS; PROSTATE; RADIOTHERAPY
- Descriptors DEC
- BODY; DISEASES; GLANDS; MALE GENITALS; MEDICINE; NUCLEAR MEDICINE; ORGANS; RADIOLOGY; THERAPY
Optional Information
- Copyright
- Copyright (c) 2018 Elsevier Inc. All rights reserved.