Optimal treatment plan adaptation using mid-treatment imaging biomarkers

Online adaptive planning methods for intensity-modulated radiotherapy

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.

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

Objective. Traditional radiotherapy (RT) treatment planning of non-small cell lung cancer (NSCLC) relies on population-wide estimates of organ tolerance to minimize excess toxicity. The goal of this study is to develop a personalized treatment planning based on patient-specific lung radiosensitivity, by combining machine learning and optimization.Approach. Sixty-nine non-small cell lung cancer patients with baseline and mid-treatment [18]F-fluorodeoxyglucose (FDG)-PET images were retrospectively analyzed. A probabilistic Bayesian networks (BN) model was developed to predict the risk of radiation pneumonitis (RP) at three months post-RT using pre- and mid-treatment FDG information. A patient-specific dose modifying factor (DMF), as a surrogate for lung radiosensitivity, was estimated to personalize the normal tissue toxicity probability (NTCP) model. This personalized NTCP was then integrated into a NTCP-based optimization model for RT adaptation, ensuring tumor coverage and respecting patient-specific lung radiosensitivity. The methodology was employed to adapt the treatment planning of fifteen NSCLC patients.Main results. The magnitude of the BN predicted risks corresponded with the RP severity. Average predicted risk for grade 1-4 RP were 0.18, 0.42, 0.63, and 0.76, respectively (p< 0.001). The proposed model yielded an average area under the receiver-operating characteristic curve (AUROC) of 0.84, outperforming the AUROCs of LKB-NTCP (0.77), and pre-treatment BN (0.79). Average DMF for the radio-tolerant (RP grade = 1) and radiosensitive (RP grade ≥ 2) groups were 0.8 and 1.63,p< 0.01. RT personalization resulted in five dose escalation strategies (average mean tumor dose increase = 6.47 Gy, range = [2.67-17.5]), and ten dose de-escalation (average mean lung dose reduction = 2.98 Gy [0.8-5.4]), corresponding to average NTCP reduction of 15% [4-27].Significance. Personalized FDG-PET-based mid-treatment adaptation of NSCLC RT could significantly lower the RP risk without compromising tumor control. The proposed methodology could help the design of personalized clinical trials for NSCLC patients.

Conic formulation of fluence map optimization problems

The convexity of objectives and constraints in fluence map optimization (FMO) for radiation therapy has been extensively studied. Next to convexity, there is another important characteristic of optimization functions and problems, which has thus far not been considered in FMO literature: conic representation. Optimization problems that are conically representable using quadratic, exponential and power cones are solvable with advanced primal-dual interior-point algorithms. These algorithms guarantee an optimal solution in polynomial time and have good performance in practice. In this paper, we construct conic representations for most FMO objectives and constraints. This paper is the first that shows that FMO problems containing multiple biological evaluation criteria can be solved in polynomial time. For fractionation-corrected functions for which no exact conic reformulation is found, we provide an accurate approximation that is conically representable. We present numerical results on the TROTS data set, which demonstrate very stable numerical performance for solving FMO problems in conic form. With ongoing research in the optimization community, improvements in speed can be expected, which makes conic optimization a promising alternative for solving FMO problems.

Probabilistic target definition and planning in patients with prostate cancer

Intro.Current radiation therapy (RT) planning guidelines handle uncertainties in RT using geometric margins. This approach is simple to use but oversimplifies complex underlying processes and is cumbersome for non-homogeneous dose prescriptions. In this work, we characterize the performance of a novel probabilistic target definition and planning (PTP) approach, which uses voxel-level tumor likelihood information in treatment plan optimization.Methods.We expanded a treatment planning system with probabilistic therapy planning functionality that utilizes non-binary target maps (TM) as voxel-level input to dose plan optimization. Different dose plans were calculated and compared for twelve prostate cancer patients with multiparametric magnetic resonance imaging derived TMs. Dose plans were created using both classical and PTP approaches for uniform and integrated dose boost prescriptions. Dose performance between the different approaches was compared using dose benchmarks on target and organ-at-risk (OAR) volumes.Results.Over all dose metrics, PTP was shown to be comparable to classical planning. For plans of uniform dose prescription, the PTP approach created plans within 1 Gy of the classical planning approach across all dose metrics, with no significant differences (p > 0.2). For plans with the integrated dose boost, PTP plans exhibited higher dose heterogeneity, but still showed target doses comparable to the classical approach, without increasing doses to OAR.Conclusion.In this work we introduce direct incorporation of probabilistic target definition into treatment planning. This treatment planning approach can produce both uniform dose plans and plans with integrated dose boosts that are comparable to ones created using classical dose planning. PTP is a flexible way to optimize external beam radiotherapy, as it is not limited by the use of margins. PTP can produce dose plans equivalent to classical planning, while also allows for greater versatility in dose prescription and direct incorporation of patient target definition uncertainty into treatment planning.