scholarly journals Evaluation of Photon and Proton Radiotherapy Plan Quality With the Knowledge-Based Approach in NRG BN001 Clinical Trial

2020 ◽  
Vol 108 (3) ◽  
pp. e344-e345
Author(s):  
D. Wang ◽  
V. Gondi ◽  
C. Tsien ◽  
T. Chenevert ◽  
M. Gilbert ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Plan Quality ◽  
Proton Radiotherapy ◽  
Knowledge Based

scholarly journals Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiang Hu ◽  
Boji Liu ◽  
Weihao Xie ◽  
Jinhan Zhu ◽  
Xiaoli Yu ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Prediction Model ◽  
Intensity Modulated Radiation Therapy ◽  
Optic Chiasm ◽  
Conformity Index ◽  
Plan Quality ◽  
Target Dose ◽  
Knowledge Based ◽  
Knowledge Based Planning

Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

AccrualNet: A novel approach to addressing clinical trial accruals through a knowledge-based, community of practice platform.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. e16621-e16621
Author(s):  
H. Massett ◽  
L. K. Parreco ◽  
R. M. Padberg ◽  
E. Richmond ◽  
D. M. Dilts
Keyword(s):  
Clinical Trial ◽  
Community Of Practice ◽  
Knowledge Based ◽  
Novel Approach

SU-E-T-234: An Automated Tool for Treatment Plan Quality Evaluation and Clinical Trial Quality Assurance

2013 ◽  
Vol 40 (6Part14) ◽  
pp. 258-258
Author(s):  
J Wang ◽  
W Chen ◽  
M Studenski ◽  
Y Cui ◽  
A Lee ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Quality Assurance ◽  
Quality Evaluation ◽  
Treatment Plan ◽  
Plan Quality ◽  
Trial Quality ◽  
Automated Tool

scholarly journals PD-0449: A knowledge based model for VMAT in esophageal cancer: plan quality assessment among different centres

2015 ◽  
Vol 115 ◽  
pp. S219
Author(s):  
A. Clivio ◽  
A. Fogliata ◽  
G. Nicolini ◽  
E. Vanetti ◽  
S. Laksar ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Quality Assessment ◽  
Plan Quality ◽  
Knowledge Based ◽  
Cancer 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.

PD-0191: Knowledge-based planning improves plan quality for high-risk prostate cancer with four dose levels

2020 ◽  
Vol 152 ◽  
pp. S94-S95
Author(s):  
K. Fjellanger ◽  
H.E.S. Pettersen ◽  
J.A. Hundvin ◽  
B. Nygaard ◽  
K. Revheim ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Plan Quality ◽  
Knowledge Based ◽  
High Risk Prostate Cancer ◽  
Risk Prostate Cancer ◽  
Knowledge Based Planning ◽  
Dose Levels

scholarly journals Knowledge-Based Statistical Inference Method for Plan Quality Quantification

2019 ◽  
Vol 18 ◽  
pp. 153303381985775 ◽  
Author(s):  
Jiang Zhang ◽  
Q. Jackie Wu ◽  
Yaorong Ge ◽  
Chunhao Wang ◽  
Yang Sheng ◽  
...  
Keyword(s):  
Organs At Risk ◽  
Quality Analysis ◽  
Patient Specific ◽  
Plan Quality ◽  
Inference Method ◽  
Knowledge Based ◽  
Plan Selection ◽  
Parotid Sparing ◽  
Treatment Plans ◽  
Over Time

Aim: The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method. Methods and Materials: We propose a knowledge-based plan quality inference method that references to similar plans in the historical database for patient-specific plan quality evaluation. First, a novel plan similarity metric with high-dimension geometrical difference quantification is utilized to retrieve similar plans. Subsequently, dosimetric statistical inferences are obtained from the selected similar plans. Two plan quality metrics—dosimetric result probability and dose deviation index—are proposed to quantify plan quality among prior similar plans. To evaluate the performance of the proposed method, we exported 927 clinically approved head and neck treatment plans. Eight organs at risk, including brain stem, cord, larynx, mandible, pharynx, oral cavity, left parotid and right parotid, were analyzed. Twelve suboptimal plans identified by dosimetric result probability were replanned to validate the capability of the proposed methods in identifying inferior plans. Results: After replanning, left and right parotid median doses are reduced by 31.7% and 18.2%, respectively; 83% of these cases would not be identified as suboptimal without the proposed similarity plan selection. Analysis of population plan quality reveals that average parotid sparing has been improving significantly over time (21.7% dosimetric result probability reduction from year 2006-2007 to year 2016-2017). Notably, the increasing dose sparing over time in retrospective plan quality analysis is strongly correlated with the increasing dose prescription ratios to the 2 planning targets, revealing the collective trend in planning conventions. Conclusions: The proposed similar plan retrieval and analysis methodology has been proven to be predictive of the current plan quality. Therefore, the proposed workflow can potentially be applied in the clinics as a real-time plan quality assurance tool. The proposed metrics can also serve the purpose of plan quality analytics in finding connections and historical trends in the clinical treatment planning workflow.

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