An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

In current standard radiation therapy process, patient anatomy is represented by the snapshot of computed tomographic images at the simulation for treatment planning. However, patient anatomy during the treatment course is not static, and the changes can be in the order of centimeters. The goal of the adaptive radiation therapy (ART) is to measure and account these variations in the treatment process, so that the optimal planned dose distribution is the same as the final delivered dose distribution. The field of the ART is rapidly evolving. The implementation of the ART principle is built on technical components in 3 main areas: image guidance, dose verification, and plan adaptation. The purpose of this review was to present different ART methods currently developed and used by different investigators.

In prostate radiation therapy, inter-fractional organ motion/deformation has posed significant challenges on reliable daily dose delivery. To correct for this issue, off-line re-optimization and online re-positioning have been used clinically. In this paper, we propose an adaptive images guided radiation therapy (AIGRT) scheme that combines these two correction methods in an anatomy-driven fashion. The AIGRT process first tries to find a best plan for the daily target from a plan pool, which consists of the original CT plan and all previous re-optimized plans. If successful, the selected plan is used for daily treatment with translational shifts. Otherwise, the AIGRT invokes the re-optimization process of the CT plan for the anatomy of the day, which is afterward added to the plan pool as a candidate for future fractions. The AIGRT scheme is evaluated by comparisons with daily re-optimization and online re-positioning techniques based on daily target coverage, organs at risk (OAR) sparing and implementation efficiency. Simulated treatment courses for 18 patients with re-optimization alone, re-positioning alone and AIGRT shows that AIGRT offers reliable daily target coverage that is highly comparable to daily re-optimization and significantly improves from re-positioning. AIGRT is also seen to provide improved OAR sparing compared to re-positioning. Apart from dosimetric benefits, AIGRT in addition offers an efficient scheme to integrate re-optimization to current re-positioning-based IGRT workflow.

The purpose of this paper is to evaluate the feasibility and clinical dosimetric benefit of an on-line, that is, with the patient in the treatment position, Adaptive Radiation Therapy (ART) system for prostate cancer treatment based on daily cone-beam CT imaging and fast volumetric reoptimization of treatment plans. A fast intensity-modulated radiotherapy (IMRT) plan reoptimization algorithm is implemented and evaluated with clinical cases. The quality of these adapted plans is compared to the corresponding new plans generated by an experienced planner using a commercial treatment planning system and also evaluated by an in-house developed tool estimating achievable dose-volume histograms (DVHs) based on a database of existing treatment plans. In addition, a clinical implementation scheme for ART is designed and evaluated using clinical cases for its dosimetric qualities and efficiency.

It is feasible to perform automatic online reoptimization in ~2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality reoptimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency, and time allowed for plan adaptation.

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This paper reports an inverse arc-modulated radiation therapy planning technique based on linear models. It is implemented with a two-step procedure. First, fluence maps for 36 fixed-gantry beams are generated using a linear model-based intensity-modulated radiation therapy (IMRT) optimization algorithm. The 2D fluence maps are decomposed into 1D fluence profiles according to each leaf pair position. Second, a mixed integer linear model is used to construct the leaf motions of an arc delivery that reproduce the 1D fluence profile previously derived from the static gantry IMRT optimization. The multi-leaf collimator (MLC) sequence takes into account the starting and ending leaf positions in between the neighbouring apertures, such that the MLC segments of the entire treatment plan are deliverable in a continuous arc. Since both steps in the algorithm use linear models, implementation is simple and straightforward. Details of the algorithm are presented, and its conceptual correctness is verified with clinical cases representing prostate and head-and-neck treatments.