Robustness comparative study of dose–volume–histogram prediction models for knowledge-based radiotherapy treatment planning

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

10.21203/rs.3.rs-16183/v1 ◽

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.

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Implement a knowledge‐based automated dose volume histogram prediction module in Pinnacle 3 treatment planning system for plan quality assurance and guidance

Journal of Applied Clinical Medical Physics ◽

10.1002/acm2.12689 ◽

2019 ◽

Vol 20 (8) ◽

pp. 134-140

Author(s):

Hao Xu ◽

Jiayu Lu ◽

Jiazhou Wang ◽

Jiawei Fan ◽

Weigang Hu

Keyword(s):

Quality Assurance ◽

Treatment Planning ◽

Planning System ◽

Treatment Planning System ◽

Plan Quality ◽

Dose Volume Histogram ◽

Knowledge Based ◽

Dose Volume ◽

Prediction Module

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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.

Letters in Oncology Science ◽

10.21641/los.2020.17.1.176 ◽

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.

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