How should we model and evaluate breathing interplay effects in IMPT?

Abstract

Breathing interplay effects in Intensity Modulated Proton Therapy (IMPT) arise from the interaction between target motion and the scanning beam. Assessing the detrimental effect of interplay and the clinical robustness of several mitigation techniques requires statistical evaluation procedures that take into account the variability of breathing during dose delivery. In this study, we present such a statistical method to model intra-fraction respiratory motion based on breathing signals and assess clinical relevant aspects related to the practical evaluation of interplay in IMPT such as how to model irregular breathing, how small breathing changes affect the final dose distribution, and what is the statistical power (number of different scenarios) required for trustworthy quantification of interplay effects. First, two data-driven methodologies to generate artificial patient-specific breathing signals are compared: a simple sinusoidal model, and a precise probabilistic deep learning model generating very realistic samples of patient breathing. Second, we investigate the highly fluctuating relationship between interplay doses and breathing parameters, showing that small changes in breathing period result in large local variations in the dose. Our results indicate that using a limited number of samples to calculate interplay statistics introduces a bigger error than using simple sinusoidal models based on patient parameters or disregarding breathing hysteresis during the evaluation. We illustrate the power of the presented statistical method by analyzing interplay robustness of 4DCT and Internal Target Volume (ITV) treatment plans for a 8 lung cancer patients, showing that, unlike 4DCT plans, even 33 fraction ITV plans systematically fail to fulfill robustness requirements.

Dosimetric Evaluation of Target Motion Effects in Spot-Scanning Proton Therapy: A Phantom Study

“…The local dose varied by up to 19% for different irradiation sessions. These data confirm the conclusions of Bert et al 9 and Pastor-Serrano et al 7 that variations in period and initial phase significantly influence local dose. Changes in the local dose, when period and peak-to-peak motion were constant, were apparently due to randomization of the initial irradiation phase and variations in the temporal structure of the proton beam delivery.…”

“…In addition, when period and peak-to-peak motion were constant, the local dose varied by 19% in different irradiation sessions, apparently due to randomization of the initial irradiation phase and variations in the temporal structure of the proton beam delivery. These considerations confirm the conclusions of Bert et al 9 and Pastor-Serrano et al 7 that variations in period and initial phase significantly influence local dose.…”

“… 28 This fact may affect the results, because small variations in period can lead to large variations in local dose. 7 Only 1 target volume of 125 cm 3 was used, but the real tumor size varies from tens to hundreds of cubic centimeter and may affect the motion effects. 8 The relationship between target size and motion amplitude may also be important for the interplay effect.…”

“…Previous studies have mostly investigated the motion effects using four-dimensional (4D) calculations based on 4D computed tomography (4DCT) data sets. 2 , 3 , 4 , 5 , 6 , 7 , 8 Experimental evaluation has been performed using 4D phantoms, radiochromic films, and 2-dimensional (2D) ion chamber (IC) array detectors. 9 , 10 , 11 Intrafractional tumor motion limits the ability to use free-breathing irradiation and cannot be compensated by the margin around the tumor, 2 requiring the advanced motion mitigation techniques recommended in the AAPM TG-290 report.…”

A review of the clinical introduction of 4D particle therapy research concepts

“…In terms of PT with ultra-high FLASH dose rates even more attention should be paid to the timing of motion and beam delivery potentially changing the complexity of intrafractional motion compensation [24] . In cases when motion compensation is not feasible during initial or adaptive treatment planning, the adequate classification of breathing motion and evaluation of the interplay effect becomes crucial in PT [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] . While organ movements can be predicted theoretically, the timing of data extraction, processing, synchronisation and correlation modelling needs consideration for a time-efficient treatment planning process.…”