Optimal treatment plan adaptation using mid-treatment imaging biomarkers

Abstract

Previous studies on personalized radiotherapy (RT) have mostly focused on baseline patient stratification, adapting the treatment plan according to mid-treatment anatomical changes, or dose boosting to selected tumor subregions using mid-treatment radiological findings. However, the question of how to find the optimal adapted plan has not been properly tackled. Moreover, the effect of information uncertainty on the resulting adaptation has not been explored. In this paper, we present a framework to optimally adapt radiation therapy treatments to early radiation treatment response estimates derived from pre- and mid-treatment imaging data while considering the information uncertainty. The framework is based on the optimal stopping in radiation therapy (OSRT) framework. Biological response is quantified using tumor control probability (TCP) and normal tissue complication probability (NTCP) models, and these are directly optimized for in the adaptation step. Two adaptation strategies are discussed: (1) uniform dose adaptation and (2) continuous dose adaptation. In the first strategy, the original fluence-map is simply scaled upwards or downwards, depending on whether dose escalation or de-escalation is deemed appropriate based on the mid-treatment response observed from the radiological images. In the second strategy, a full NTCP-TCP-based fluence map re-optimization is performed to achieve the optimal adapted plans. We retrospectively tested the performance of these strategies on 14 canine head and neck cases treated with tomotherapy, using as response biomarker the change in the 3’-deoxy-3’[(18)F]-fluorothymidine (FLT)-PET signals between the pre- and mid-treatment images, and accounting for information uncertainty. Using a 10% uncertainty level, the two adaptation strategies both yield a noteworthy average improvement in guaranteed (worst-case) TCP.

Online adaptive planning methods for intensity-modulated radiotherapy

“…Biological response to radiation may be a better predictor of tumor control and normal tissue toxicity than physical dose and integrating biological information with OnART would result in plans that are optimized not only for the patientʼs daily anatomy but also for biological tumor control or toxicity reduction. 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.…”

Probabilistic target definition and planning in patients with prostate cancer

“…Intensity modulated radiation therapy (IMRT) dose plans were calculated using WiscPlan, an in-house treatment planning software for Tomotherapy delivery systems (Prajapati et al 2014, Ten Eikelder et al 2020. Dose was calculated for helical deliveries using 64 binary multileaf collimators, 51 angles for the x-ray source position per rotation, a pitch of 0.86 and a 2.5 cm jaw width (Flynn et al 2008).…”

Adaptive radiotherapy from past to future frontiers

“…On the other hand, it also leads to improve dramatically the Normal Tissue Control Probability (NTCP) concept as well. 4,5 ART has been an active field essentially by scientific forces behind the medical process in radiotherapy during the last decades and it is now being implemented in clinics by merging different applicable ideas regarding the treatment regions by using newly established alternative technologies.…”