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Objective. This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART). Approach. The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection. Main results. Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose, V 20, and V 5 were significantly reduced by 1.13 Gy (p <.001), 2.01% (p <.001), and 6.66% (p <.001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p <.01), fV 20 by 0.90% (p = 0.099), and fV 5 by 5.07% (p <.01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection. Significance. AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans. [ABSTRACT FROM AUTHOR]

Background: Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning, however, poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods. Purpose: A deep learning–based approach to automatic beam angle selection is proposed for the proton pencil‐beam scanning treatment planning of liver lesions. Methods: We cast beam‐angle selection as a multi‐label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance–based regularization and multi‐label circular–smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners' clinical practice is employed in post‐processing to improve the output of the model. Forty‐nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI‐selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge‐based treatment planning. Results: For 7 of the 11 cases in the test set, AI‐selected beam angles agreed with those chosen by human planners to within 20° (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10° of the human‐selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI‐ and human‐selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ‐at‐risk sparing. Conclusions: This pilot study demonstrated the feasibility of a novel deep learning–based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human‐selected beam angles. In tandem with auto‐contouring and knowledge‐based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy. [ABSTRACT FROM AUTHOR]

Lung cancer patients struggle with high toxicity rates. This study investigates if IMRT plans with individually set beam angles or uni-lateral VMAT plans results in dose reduction to OARs. We investigate if introduction of a RapidPlan model leads to reduced dose to OARs. Finally, the model is validated prospectively. Seventy-four consecutive lung cancer patients treated with IMRT were included. For all patients, new IMRT plans were made by an experienced dose planner re-tuning beam angles aiming for minimized dose to the lungs and heart. Additionally, VMAT plans were made. The IMRT plans were selected as input for a RapidPlan model, which was used to generate 74 new IMRT plans. The new IMRT plans were used as input for a second RapidPlan model. This model was clinically implemented and used for generation of clinical treatment plans. Dosimetric parameters were compared using a Wilcoxon signed rank test or a 1-sided student's t-test. p <.05 was considered significant. IMRT plans significantly reduced mean doses to lungs (MLD) and heart (MHD) by 1.6 Gy and 1.7 Gy in mean compared to VMAT plans. MLD was significantly (p <.001) reduced from 10.8 Gy to 9.4 Gy by using the second RapidPlan model. MHD was significantly (p <.001) reduced from 4.9 Gy to 3.9 Gy. The model was validated in prospectively collected treatment plans showing significantly lower MLD after the implementation of the second RapidPlan model. Introduction of RapidPlan and beam angles selected based on the target and OARs position reduces dose to OARs. [ABSTRACT FROM AUTHOR]

Beam angle selection (BAS) is an important part of intensity-modulated radiation therapy and can be very challenging due to its huge solution space and computational difficulty. In this research, we have developed a nested partitions (NP) framework to optimize beam angles. NP is a metaheuristic algorithm which successively partitions the entire solution space, evaluates the quality of each sub-region formed by partitioning, and concentrates the search for the optimum in promising sub-regions. Moreover, we construct a machine learning (ML) model to quickly estimate performance of the selected angle vectors so that thousands of angle vectors can be evaluated within seconds. We compare the ML-based NP (MLNP) framework with five other BAS methods. Numerical tests for five head and neck cases are performed. The results show that MLNP can generate solutions with better quality and achieve higher computational efficiency than the compared methods. [ABSTRACT FROM PUBLISHER]

Purpose: Iterative methods for beam angle selection (BAS) for intensity-modulated radiation therapy (IMRT) planning sequentially construct a beneficial ensemble of beam directions. In a naïve implementation, the nth beam is selected by adding beam orientations one-by-one from a discrete set of candidates to an existing ensemble of (n-1) beams. The best beam orientation is identified in a time consuming process by solving the fluence map optimization (FMO) problem for every candidate beam and selecting the beam that yields the largest improvement to the objective function value. This paper evaluates two alternative methods to accelerate iterative BAS based on surrogates for the FMO objective function value. Methods:We suggest to select candidate beams not based on the FMO objective function value after convergence but (1) based on the objective function value after five FMO iterations of a gradient based algorithm and (2) based on a projected gradient of the FMO problem in the first iteration. The performance of the objective function surrogates is evaluated based on the resulting objective function values and dose statistics in a treatment planning study comprising three intracranial, three pancreas, and three prostate cases. Furthermore, iterative BAS is evaluated for an application in which a small number of noncoplanar beams complement a set of coplanar beam orientations. This scenario is of practical interest as noncoplanar setups may require additional attention of the treatment personnel for every couch rotation. Results: Iterative BAS relying on objective function surrogates yields similar results compared to naive BAS with regard to the objective function values and dose statistics. At the same time, early stopping of the FMO and using the projected gradient during the first iteration enable reductions in computation time by approximately one to two orders of magnitude. With regard to the clinical delivery of noncoplanar IMRT treatments, we could show that optimized beam ensembles using only a few noncoplanar beam orientations often approach the plan quality of fully noncoplanar ensembles. Conclusions: We conclude that iterative BAS in combination with objective function surrogates can be a viable option to implement automated BAS at clinically acceptable computation times. [ABSTRACT FROM AUTHOR]