Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

Abstract

Recent theoretical research on spatiobiologically integrated radiotherapy has focused on optimization models that adapt fluence-maps to the evolution of tumor state, for example, cell densities, as observed in quantitative functional images acquired over the treatment course. We propose an optimization model that adapts the length of the treatment course as well as the fluence-maps to such imaged tumor state. Specifically, after observing the tumor cell densities at the beginning of a session, the treatment planner solves a group of convex optimization problems to determine an optimal number of remaining treatment sessions, and a corresponding optimal fluence-map for each of these sessions. The objective is to minimize the total number of tumor cells remaining (TNTCR) at the end of this proposed treatment course, subject to upper limits on the biologically effective dose delivered to the organs-at-risk. This fluence-map is administered in future sessions until the next image is available, and then the number of sessions and the fluence-map are re-optimized based on the latest cell density information. We demonstrate via computer simulations on five head-and-neck test cases that such adaptive treatment-length and fluence-map planning reduces the TNTCR and increases the biological effect on the tumor while employing shorter treatment courses, as compared to only adapting fluence-maps and using a pre-determined treatment course length based on one-size-fits-all guidelines.

Personalized mid-course FDG-PET based adaptive treatment planning for non-small cell lung cancer using machine learning and optimization

“…Nonetheless, taking all (or some) of these uncertainties into account would require robust optimization. We had previously researched the impact of some robust optimization methods on reducing parameter and model's uncertainties (Ajdari and Ghate 2016, Ajdari et al 2018, Eikelder 2018. Lastly, we only included four OARs in the Adaptation Module, which inevitably lead to oversimplification of the optimization problem and overestimating the extent to which the plan can be adapted.…”

Online adaptive planning methods for intensity-modulated radiotherapy

“…There are many interesting efforts ongoing in this space; the response during treatment could potentially be estimated using bio-markers and tracers that would be visible on an integrated PET image (Fonti et al 2019, Ten Eikelder et al 2020, Ajdari et al 2022. The acquired patient-specific information can be used to update the treatment plan with patient-specific radio-biological parameters (Ajdari et al 2022), scale the treatment plan (Ten Eikelder et al 2020), or adjust treatment duration (Ajdari et al 2018) for the optimal therapeutical effect. Biological adaptation is expected to serve a more crucial role in radiation therapy, providing further personalized treatment and enhancing treatment outcomes.…”

Optimal treatment plan adaptation using mid-treatment imaging biomarkers

“…If neither an adapted treatment plan nor the original treatment plan yields sufficient treatment plan quality, one can stop the treatment, and possibly switch to another treatment modality. In the context of OSRT, one can also opt to optimally adapt the number of treatment fractions; Ajdari et al (2018) and ten Eikelder et al (2019) study this problem from a theoretical perspective. This adaptation strategy is not considered in the current study.…”

“…The framework has been concretized with hypoxia as a biomarker (Saberian et al 2016). Ajdari et al (2018) assume temporally and spatially varying α and β parameters, and adaptively optimize the treatment-length based on (hypothetically) observed tumor density, in order maximize TCP subject to BED constraints. Ten Eikelder et al (2019) take an adjustable robust optimization approach to treatment-length optimization using the BED model, assuming biomarker data provides inexact estimates of α/β parameters.…”