Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy-Reference-Cited by-同舟云学术

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Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy

Published:2020-02-19 Issue:4 Volume:47 Page:1452-1459
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Jones Scott¹²,Hargrave Catriona¹³,Deegan Timothy¹³,Holt Tanya¹⁴,Mengersen Kerrie²

Affiliation:

1. Radiation Oncology Princess Alexandra Hospital Raymond Terrace Brisbane Qld 4101 Australia

2. Science and Engineering Faculty Queensland University of Technology Brisbane Qld 4000 Australia

3. School of Clinical Sciences Faculty of Health Queensland University of Technology Brisbane Qld 4000 Australia

4. University of Queensland Brisbane Qld 4072 Australia

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14044

Reference29 articles.

1. Inter-institutional analysis demonstrates the importance of lower than previously anticipated dose regions to prevent late rectal bleeding following prostate radiotherapy

2. Volumetric image-guided conformal radiotherapy for localized prostate cancer: Analysis of dosimetric and clinical factors affecting acute and late toxicity

3. SpaceOAR Hydrogel in Dose-escalated Prostate Cancer Radiotherapy: Rectal Dosimetry and Late Toxicity

4. Feasibility of and rectal dosimetry improvement with the use of SpaceOAR® hydrogel for dose-escalated prostate cancer radiotherapy

5. Treatment planning after hydrogel injection during radiotherapy of prostate cancer

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Building a Multi-Model Fusion Prostate Cancer Risk Prediction Model Based on Clinical Data and Machine Learning;2024

2. Cost‐effectiveness analysis of hydrogel spacer for rectal toxicity reduction in prostate external beam radiotherapy;Journal of Medical Imaging and Radiation Oncology;2021-08-16

3. Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging;Abdominal Radiology;2021-02-12

4. Predicting radiation treatment planning evaluation parameter using artificial intelligence and machine learning;IOP SciNotes;2020-06-01

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