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.

scholarly journals Automated planning, knowledge-based planning and other novel developments in radiotherapy treatment planning - how do they work and perform at the ESTRO 36th conference, Vienna, Austria.

2020 ◽  
Vol 17 (1) ◽  
pp. 64-40
Author(s):  
Agnieszka Skrobała
Keyword(s):  
Treatment Planning ◽  
Automated Planning ◽  
Annual Conference ◽  
Radiotherapy Treatment Planning ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Research Problems ◽  
Radiotherapy Treatment ◽  
Planning Knowledge

The aim of this study was to review and presentation system of automated planning, knowledge-based planning and other novel developments in radiotherapy treatment planning, with the answer on the question; how do these system work and perform.  The review was based on selected reports and research problems presented during ESTRO 36th annual conference held in Vienna, Austria.

scholarly journals The role of PET-CT in radiotherapy planning of solid tumours

2015 ◽  
Vol 49 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Stasa Jelercic ◽  
Mirjana Rajer
Keyword(s):  
Treatment Planning ◽  
Planning Process ◽  
Diagnostic Procedures ◽  
Solid Tumours ◽  
Radiotherapy Planning ◽  
Radiotherapy Treatment Planning ◽  
Pet Ct ◽  
Target Volumes ◽  
Radiotherapy Treatment

AbstractBackground. PET-CT is becoming more and more important in various aspects of oncology. Until recently it was used mainly as part of diagnostic procedures and for evaluation of treatment results. With development of personalized radiotherapy, volumetric and radiobiological characteristics of individual tumour have become integrated in the multistep radiotherapy (RT) planning process. Standard anatomical imaging used to select and delineate RT target volumes can be enriched by the information on tumour biology gained by PET-CT. In this review we explore the current and possible future role of PET-CT in radiotherapy treatment planning. After general explanation, we assess its role in radiotherapy of those solid tumours for which PET-CT is being used most.Conclusions. In the nearby future PET-CT will be an integral part of the most radiotherapy treatment planning procedures in an every-day clinical practice. Apart from a clear role in radiation planning of lung cancer, with forthcoming clinical trials, we will get more evidence of the optimal use of PET-CT in radiotherapy planning of other solid tumours

scholarly journals ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning

2021 ◽  
pp. 134-142 ◽  
Author(s):  
Brent M. Covele ◽  
Kartikeya S. Puri ◽  
Karoline Kallis ◽  
James D. Murphy ◽  
Kevin L. Moore
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Estimation Methods ◽  
Web Based ◽  
Knowledge Based ◽  
Validation Set ◽  
Control Feedback ◽  
Dicom Rt

PURPOSE Access to knowledge-based treatment plan quality control has been hindered by the complexity of developing models and integration with different treatment planning systems (TPS). Online Real-time Benchmarking Information Technology for RadioTherapy (ORBIT-RT) provides a free, web-based platform for knowledge-based dose estimation that can be used by clinicians worldwide to benchmark the quality of their radiotherapy plans. MATERIALS AND METHODS The ORBIT-RT platform was developed to satisfy four primary design criteria: web-based access, TPS independence, Health Insurance Portability and Accountability Act compliance, and autonomous operation. ORBIT-RT uses a cloud-based server to automatically anonymize a user's Digital Imaging and Communications in Medicine for RadioTherapy (DICOM-RT) file before upload and processing of the case. From there, ORBIT-RT uses established knowledge-based dose-volume histogram (DVH) estimation methods to autonomously create DVH estimations for the uploaded DICOM-RT. ORBIT-RT performance was evaluated with an independent validation set of 45 volumetric modulated arc therapy prostate plans with two key metrics: (i) accuracy of the DVH estimations, as quantified by their error, DVHclinical − DVHprediction and (ii) time to process and display the DVH estimations on the ORBIT-RT platform. RESULTS ORBIT-RT organ DVH predictions show < 1% bias and 3% error uncertainty at doses > 80% of prescription for the prostate validation set. The ORBIT-RT extensions require 3.0 seconds per organ to analyze. The DICOM upload, data transfer, and DVH output display extend the entire system workflow to 2.5-3 minutes. CONCLUSION ORBIT-RT demonstrated fast and fully autonomous knowledge-based feedback on a web-based platform that takes only anonymized DICOM-RT as input. The ORBIT-RT system can be used for real-time quality control feedback that provides users with objective comparisons for final plan DVHs.

scholarly journals The benefits evaluation of abdominal deep inspiration breath hold based on knowledge-based radiotherapy treatment planning for left-sided breast cancer

2020 ◽  
Author(s):  
Jiaqi Xu ◽  
Jiazhou Wang ◽  
Feng Zhao ◽  
Weigang Hu ◽  
Luyi Bu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Treatment Planning ◽  
Breath Hold ◽  
Deep Inspiration ◽  
Breast Cancer Patients ◽  
Radiotherapy Treatment Planning ◽  
Deep Inspiration Breath Hold ◽  
Knowledge Based ◽  
Radiotherapy Treatment

Abstract Purpose Study the impact of abdominal deep inspiration breath hold (DIBH) technique on knowledge-based radiotherapy treatment planning for left-sided breast cancer to guide the application of DIBH radiotherapy technology. Methods and Materials Two kernel density estimation (KDE) models were developed based on 40 left-sided breast cancer patients with two CT acquisitions of free breathing (FB-CT) and DIBH (DIBH-CT). Each KDE model was used to predict DVHs based on DIBH-CT and FB-CT for another 10 new patients similar to our training datasets. The predicted DVHs were taken as a substitute to dose constraints and objective functions in the Eclipse treatment planning system, with the same requirements for the planning target volume (PTV). The mean doses to the heart, the left anterior descending coronary artery (LADCA) and the ipsilateral lung were evaluated and compared using the T-test among clinical plans, KDE predictions, and KDE plans.Results Our study demonstrated that the KDE model can generate deliverable simulations equivalent to clinically applicable plans. The T-test was applied to test the consistency hypothesis on another 10 left-sided breast cancer patients. In cases of the same breathing status, there was no statistically significant difference between the predicted and the clinical plans for all clinically relevant dose volume histogram (DVH) indices (p>0.05), and all predicted DVHs can be transferred into deliverable plans. For DIBH-CT images, significant differences were observed in Dmean between FB model predictions and the clinical plans (p<0.05). DIBH model prediction cannot be optimized to a deliverable plan based on FB-CT, with a counsel of perfection. Conclusion This study demonstrated that the KDE prediction results were well fitted for the same breathing condition but degrade with different breathing conditions. The benefits of DIBH can be evaluated quickly and effectively by the specific knowledge-based treatment planning for left-sided breast cancer radiotherapy. This study will help to further realize the goal of automatic treatment planning.

scholarly journals AutoLock: a semiautomated system for radiotherapy treatment plan quality control

2015 ◽  
Vol 16 (3) ◽  
pp. 339-350 ◽  
Author(s):  
Joseph M. Dewhurst ◽  
Matthew Lowe ◽  
Mark J. Hardy ◽  
Christopher J. Boylan ◽  
Philip Whitehurst ◽  
...  
Keyword(s):  
Quality Control ◽  
Treatment Plan ◽  
Plan Quality ◽  
Radiotherapy Treatment

Patient-specific dosimetric endpoints based treatment plan quality control in radiotherapy

2015 ◽  
Vol 60 (21) ◽  
pp. 8213-8227 ◽  
Author(s):  
Ting Song ◽  
David Staub ◽  
Mingli Chen ◽  
Weiguo Lu ◽  
Zhen Tian ◽  
...  
Keyword(s):  
Quality Control ◽  
Treatment Plan ◽  
Patient Specific ◽  
Plan Quality

scholarly journals Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Nicholas Hardcastle ◽  
Olivia Cook ◽  
Xenia Ray ◽  
Alisha Moore ◽  
Kevin L. Moore ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real Time ◽  
Kidney Cancer ◽  
Treatment Plan ◽  
Plan Quality ◽  
Knowledge Based ◽  
Trade Offs ◽  
Stereotactic Ablative Body Radiotherapy ◽  
Knowledge Based Planning ◽  
Treatment Plans

Abstract Introduction Quality assurance (QA) of treatment plans in clinical trials improves protocol compliance and patient outcomes. Retrospective use of knowledge-based-planning (KBP) in clinical trials has demonstrated improved treatment plan quality and consistency. We report the results of prospective use of KBP for real-time QA of treatment plan quality in the TROG 15.03 FASTRACK II trial, which evaluates efficacy of stereotactic ablative body radiotherapy (SABR) for kidney cancer. Methods A KBP model was generated based on single institution data. For each patient in the KBP phase (open to the last 31 patients in the trial), the treating centre submitted treatment plans 7 days prior to treatment. A treatment plan was created by using the KBP model, which was compared with the submitted plan for each organ-at-risk (OAR) dose constraint. A report comparing each plan for each OAR constraint was provided to the submitting centre within 24 h of receiving the plan. The centre could then modify the plan based on the KBP report, or continue with the existing plan. Results Real-time feedback using KBP was provided in 24/31 cases. Consistent plan quality was in general achieved between KBP and the submitted plan. KBP review resulted in replan and improvement of OAR dosimetry in two patients. All centres indicated that the feedback was a useful QA check of their treatment plan. Conclusion KBP for real-time treatment plan review was feasible for 24/31 cases, and demonstrated ability to improve treatment plan quality in two cases. Challenges include integration of KBP feedback into clinical timelines, interpretation of KBP results with respect to clinical trade-offs, and determination of appropriate plan quality improvement criteria.

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