radiation-planning-assistant-web-based-tool-to-support-high-quality

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer ...

The University of Texas MD Anderson Cancer Center

A retrospective analysis is performed to determine if pre-treatment [18 F]-2-fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT) image derived parameters can predict radiation pneumonitis (RP) clinical symptoms in lung cancer patients.

Pre-radiotherapy FDG PET predicts radiation pneumonitis in lung cancer.

Fifteen fraction treatment schedules are increasingly used to deliver high doses of radiation therapy (RT) to both lung and hepatobiliary malignancies. The purpose of our study was to examine the incidence and predictors of chest wall (CW) toxicity in patients treated with this regimen.

Incidence and predictors of chest wall toxicity after high-dose radiation therapy in 15 fractions.

We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process.

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow.

This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose-volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper-lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re-generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge-based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically-generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic.

Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.

Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines.

Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors.

To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward-planning algorithms.

Automation of radiation treatment planning for rectal cancer.

This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans.

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.

A novel compensator-based system has been proposed which delivers intensity-modulated radiation therapy (IMRT) with cobalt-60 beams. This could improve access to advanced radiotherapy in low- and middle-income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low- and middle-income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS).

Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning.

In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

Article	Year
Pre-radiotherapy FDG PET predicts radiation pneumonitis in lung cancer. Radiation oncology (London, England)\| PubMed ID: 24625207	2014
Incidence and predictors of chest wall toxicity after high-dose radiation therapy in 15 fractions. Practical radiation oncology\| PubMed ID: 27637136	2017
Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society\| PubMed ID: 33845433	2021
Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer. Journal of applied clinical medical physics\| PubMed ID: 35488508	2022
Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors. Journal of applied clinical medical physics\| PubMed ID: 35791594	2022
Automation of radiation treatment planning for rectal cancer. Journal of applied clinical medical physics\| PubMed ID: 35808871	2022
Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans. Practical radiation oncology\| PubMed ID: 36697347	2023
Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning. Medical physics\| PubMed ID: 37086040	2023
Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers. Medical physics\| PubMed ID: 37706560	2023

Adenike Olanrewaju

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