Direct incorporation of patient‐specific efficacy and toxicity estimates in radiation therapy plan optimization

Abstract

Purpose Current radiation therapy (RT) treatment planning relies mainly on pre‐defined dose‐based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population‐based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient‐specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity‐modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient‐specific dose response models into the planning process. In this strategy, we integrate the concept of utility‐based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non‐small cell lung cancer (NSCLC) patients. Methods and materials We developed a prioritized approach to patient‐specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet‐dose contributions to regions‐of‐interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade‐off based on individualized dose‐response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose‐based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3–5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors. Results The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some p...

Built-in wavelet-induced smoothness to reduce plan complexity in intensity modulated radiation therapy (IMRT)

“…There exists a wide variety of IMRT optimization frameworks including robust optimization (Unkelbach et al 2018), hierarchical optimization (Breedveld et al 2012, Zarepisheh et al 2022, multi-criteria optimization (Craft et al 2006), and knowledge-based optimization (Appenzoller et al 2012) in research and clinic with supports for a variety of objective functions and constraints including dose volume histogram (DVH) (Cho et al 1998, Mukherjee et al 2018, generalized equivalent uniform dose (gEUD) (Niemierko 1997), tumor control probability (TCP) (Polan et al 2022), and normal tissue complication probability (NTCP) (Lyman & Wolbarst 1987, Ajdari et al 2022, Polan et al 2022. One of the advantages of the proposed wavelet-based technique is that it is agnostic to the optimization framework and can be easily integrated with any framework by simply adding a set of linear constraints.…”

Unravelling Quality of Life for Head and Neck Cancer Patients after VMAT Radiation Therapy: Insights from Toxicity, Dosimetry and Symptoms Correlation

“…These scoring systems enable clinicians to objectively evaluate the severity of treatment-related toxicities, guide treatment modifications or supportive care interventions accordingly, and enhance patients’ overall treatment experience. Thus, the integration of dosimetric data, adherence to QUANTEC guidelines, and utilization of toxicity scoring systems play a pivotal role in optimizing treatment outcomes, minimizing adverse effects, and ultimately improving patients’ QoL during and after radiotherapy for head and neck cancer [ 13 , 14 ].…”