Stability analysis of patient‐specific 4DCT‐ and 4DCBCT‐based correspondence models

Abstract

AbstractBackgroundSurrogate‐based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient‐specific correspondence model.PurposeThis study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone‐beam computed tomography (4DCBCT) data and assessing the relation to patient‐specific clinical parameters.MethodsFor correspondence modeling, a regression‐based approach is applied, correlating patient‐specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume‐based analysis: 4DCBCT‐based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix‐based analysis: 4DCBCT‐based regression models are compared to 4DCT‐based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV‐based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners).ResultsConsistent results across the two complementary analysis approaches (Spearman correlation coefficient of between system matrix‐based MSD and GTV‐based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction‐wise models not surpassing a threshold of for the GTV, and only 14/46 patients demonstrating a in all fractions. Model stability did not degrade over the course of treatment. The mean GTV‐based DSC is (mean ASSD of ) and the respective ITV‐based DSC is (mean ASSD of ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increased stability.ConclusionsThe proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal–external motion variations throughout the course of treatment.