Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics

Abstract

Background and purpose: Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis. Material and methods: A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort). Results: For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and − 4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: − 0.1 ± 1.1 Gy and − 0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning. Conclusion: An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

“…Additional methods have recently been proposed which could aid in better quantifying and objectively assessing a plan's dosimetric quality. Tools such as a total plan quality index (PQI), which collapses several indices into an overall plan score [31][32][33][34][35], the use of machine and deep learning [36][37][38], and new kinds of dose distribution metrics [39][40][41][42][43] can provide quantification of parameters which are currently mainly evaluated subjectively. We believe using such tools will facilitate consensus and standardization on how to define a treatment plan's quality.…”

Standardizing and improving dose predictions for head and neck cancers using complete sets of OAR contours

“…[1][2][3] The ability of the treatment planning team to develop acceptable plans depends greatly on the methods used to generate the plan, the experience of the planner, and the required complexity of treatment. 4 In the current treatment planning workflow, OAR and PTV contours are defined manually by the physician or dosimetrist and requires an average of 2.7-3 h per HNC. 3 Following structure delineation, the treatment plan is created in an iterative process where the dosimetrist and physician make small adjustments to the plan to meet tumor dose coverage and appropriately minimize dose to normal tissue.…”

“…These treatment sites have multiple large planning target volumes (PTVs), many radiosensitive organs at risk (OAR), and strict dose requirements, which make meeting clinical objectives and creating the ideal plan a challenging, ill‐posed, and time‐consuming task 1–3 . The ability of the treatment planning team to develop acceptable plans depends greatly on the methods used to generate the plan, the experience of the planner, and the required complexity of treatment 4 …”