Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients. (2nd September 2021) - Tambe, Nilesh S; Pires, Isabel M; Moore, Craig; Wieczorek, Andrew; Upadhyay, Sunil; Beavis, Andrew W
Abstract: Objectives . anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the planned distribution and minimise detriment to predicted treatment outcome. Typically, patients who may benefit from adaptive planning are identified via a re-planning process, i.e., re-simulation, re-contouring, re-planning and treatment plan quality assurance (QA). This time-intensive process significantly increases workload, can introduce delays and increases unnecessary stress to those patients who will not actually gain benefit. We consider it crucial to develop efficient models to predict changes to target coverage and trigger ART, without the need for re-planning. Methods. knowledge-based planning (KBP) models were developed using data for 20 patients' (400 fractions) to predict changes in PTV V95 coverage Δ V 95 P T V . Initially, this change in coverage was calculated on the synthetic computerised tomography (sCT) images produced using the Velocity adaptive radiotherapy software. Models were developed using patient (cell death bio-marker) and treatment fraction (PTV characteristic) specific parameters to predict Δ V 95 P T V and verified using five patients (100 fractions) data. Results . three models were developed using combinations of patient and fraction specific terms. The prediction accuracy of the model developed using biomarker (PD-L1 expression) and the difference in 'planning' and 'fraction' PTV centre of the mass (characterised by mean square difference, MSD) had the higher prediction accuracy, predicting the Δ V 95 P T V within ± 1.0% for 77% of the total fractions; with 59% for the model developed using, PTV size, PD-L1 and MSD and 48% PTV size and MSD respectively. Conclusion . the KBP models can predict Δ V 95 P T V very effectively and efficiently for advanced-stage NSCLC patients treated using volumetric modulated arc therapy and to identify patients who may benefit from adaption for a specific fraction.