SU-E-T-38: An Overlap-Wall-Histogram Plan Quality Classifier for Automatic Prostate Cancer IMRT Treatment Plan Quality Control

2014 ◽  
Vol 41 (6Part11) ◽  
pp. 230-230
Author(s):  
T Song ◽  
Z Tian ◽  
X Jia ◽  
L Zhou ◽  
S Jiang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quality Control ◽  
Treatment Plan ◽  
Plan Quality ◽  
Imrt Treatment Plan

Patient geometry-driven information retrieval for IMRT treatment plan quality control

2009 ◽  
Vol 36 (12) ◽  
pp. 5497-5505 ◽  
Author(s):  
Binbin Wu ◽  
Francesco Ricchetti ◽  
Giuseppe Sanguineti ◽  
Misha Kazhdan ◽  
Patricio Simari ◽  
...  
Keyword(s):  
Quality Control ◽  
Information Retrieval ◽  
Treatment Plan ◽  
Plan Quality ◽  
Imrt Treatment Plan

Evaluation of MLC leaf transmission on IMRT treatment plan quality of patients with advanced lung cancer

2018 ◽  
Vol 43 (4) ◽  
pp. 313-318 ◽  
Author(s):  
Jiayun Chen ◽  
Guishan Fu ◽  
Minghui Li ◽  
Yixin Song ◽  
Jianrong Dai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Treatment Plan ◽  
Advanced Lung Cancer ◽  
Plan Quality ◽  
Imrt Treatment Plan

MO-D-105-05: A Novel Web-Based Tool for Quantification of VMAT/IMRT Treatment Plan Quality

2013 ◽  
Vol 40 (6Part23) ◽  
pp. 395-395
Author(s):  
M Fan ◽  
F DeBlois ◽  
K Sultanem ◽  
G Stroian
Keyword(s):  
Treatment Plan ◽  
Plan Quality ◽  
Web Based ◽  
Imrt Treatment Plan

Knowledge-based radiotherapy treatment planning

2015 ◽  
Author(s):  
◽  
Lindsey Appenzoller Olsen
Keyword(s):  
Quality Control ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Radiotherapy Planning ◽  
Patient Specific ◽  
Plan Quality ◽  
Radiotherapy Treatment Planning ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Radiotherapy Treatment

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Knowledge-based planning (KBP) has become a prominent area of research in radiation oncology in the last five years. The development of KBP aims to address the lack of systematic quality control and plan quality variability in radiotherapy treatment planning by providing achievable, patient-specific optimization objectives derived from a model trained with a cohort of previously treated, site-specific plans. This dissertation intended to develop, evaluate, and implement a knowledge-based planning system to reduce variability and improve radiotherapy treatment plan quality. The project aimed to 1) develop and validate an algorithm to train mathematical models that predict dose-volume histograms for organs at risk in radiotherapy planning, 2) implement the algorithm into a software application in order to transfer the technology into clinical practice, and 3) evaluate the impact of the software system (algorithm + application) on reducing variability and improving radiotherapy treatment plan quality through knowledge transfer. The presented work demonstrates that a KBP model is beneficial to radiotherapy planning. The developed models adequately describe what is dosimetrically achievable for patient specific anatomy and have proven useful in outlier detection for quality control of radiotherapy planning. The KBP paradigm has also demonstrated ability to improve treatment plan quality through benchmarking and transfer of knowledge between institutions.

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