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,V20, andV5were 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),fV20by 0.90% (p= 0.099), andfV5by 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.

Magnetic resonance imaging (MRI) provides excellent visualization of central nervous system (CNS) tumors due to its superior soft tissue contrast. Magnetic resonance-guided radiotherapy (MRgRT) has historically been limited to use in the initial treatment planning stage due to cost and feasibility. MRI-guided linear accelerators (MRLs) allow clinicians to visualize tumors and organs at risk (OARs) directly before and during treatment, a process known as online MRgRT. This novel system permits adaptive treatment planning based on anatomical changes to ensure accurate dose delivery to the tumor while minimizing unnecessary toxicity to healthy tissue. These advancements are critical to treatment adaptation in the brain and spinal cord, where both preliminary MRI and daily CT guidance have typically had limited benefit. In this narrative review, we investigate the application of online MRgRT in the treatment of various CNS malignancies and any relevant ongoing clinical trials. Imaging of glioblastoma patients has shown significant changes in the gross tumor volume over a standard course of chemoradiotherapy. The use of adaptive online MRgRT in these patients demonstrated reduced target volumes with cavity shrinkage and a resulting reduction in radiation dose to uninvolved tissue. Dosimetric feasibility studies have shown MRL-guided stereotactic radiotherapy (SRT) for intracranial and spine tumors to have potential dosimetric advantages and reduced morbidity compared with conventional linear accelerators. Similarly, dosimetric feasibility studies have shown promise in hippocampal avoidance whole brain radiotherapy (HA-WBRT). Next, we explore the potential of MRL-based multiparametric MRI (mpMRI) and genomically informed radiotherapy to treat CNS disease with cutting-edge precision. Lastly, we explore the challenges of treating CNS malignancies and special limitations MRL systems face.

BACKGROUND: Lattice radiation therapy (LRT) alternates regions of high and low doses within the target. The heterogeneous dose distribution is delivered to a geometrical structure of vertices segmented inside the tumor. LRT is typically used to treat patients with large tumor volumes with cytoreduction intent. Due to the geometric complexity of the target volume and the required dose distribution, LRT treatment planning demands additional resources, which may limit clinical integration.
OBJECTIVE: We introduce a fully automated method to (1) generate an ordered lattice of vertices with various sizes and center-to-center distances and (2) perform dose optimization and calculation. We aim to report the dosimetry associated with these lattices to help clinical decision-making.
METHODS: Sarcoma cancer patients with tumor volume between 100 cm3 and 1500 cm3 who received radiotherapy treatment between 2010 and 2018 at our institution were considered for inclusion. Automated segmentation and dose optimization/calculation were performed by using the Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices were modeled by spheres segmented within the gross tumor volume (GTV) with 1 cm/1.5 cm/2 cm diameters (LRT-1 cm/1.5 cm/2 cm) and 2 to 5 cm center-to-center distance on square lattices alternating along the superior-inferior direction. Organs at risk were modeled by subtracting the GTV from the body structure (body-GTV). The prescription dose was that 50% of the vertice volume should receive at least 20 Gy in one fraction. The automated dose optimization included three stages. The vertices optimization objectives were refined during optimization according to their values at the end of the first and second stages. Lattices were classified according to a score based on the minimization of body-GTV max dose and the maximization of GTV dose uniformity (measured with the equivalent uniform dose [EUD]), GTV dose heterogeneity (measured with the GTV D90%/D10% ratio), and the number of patients with more than one vertex inserted in the GTV. Plan complexity was measured with the modulation complexity score (MCS). Correlations were assessed with the Spearman correlation coefficient (r) and its associated p-value.
RESULTS: Thirty-three patients with GTV volumes between 150 and 1350 cm3 (median GTV volume = 494 cm3 , IQR = 272-779 cm3 were included. The median time required for segmentation/planning was 1 min/21 min. The number of vertices was strongly correlated with GTV volume in each LRT lattice for each center-to-center distance (r > 0.85, p-values < 0.001 in each case). Lattices with center-to-center distance = 2.5 cm/3 cm/3.5 cm in LRT-1.5 cm and center-to-center distance = 4 cm in LRT-1 cm had the best scores. These lattices were characterized by high heterogeneity (median GTV D90%/D10% between 0.06 and 0.19). The generated plans were moderately complex (median MCS ranged between 0.19 and 0.40).
CONCLUSIONS: The automated LRT planning method allows for the efficacious generation of vertices arranged in an ordered lattice and the refinement of planning objectives during dose optimization, enabling the systematic evaluation of LRT dosimetry from various lattice geometries.

Stereotactic body radiotherapy (SBRT) is an effective radiation therapy technique that has allowed for shorter treatment courses, as compared to conventionally dosed radiation therapy. As its name implies, SBRT relies on daily image guidance to ensure that each fraction targets a tumor, instead of healthy tissue. Magnetic resonance imaging (MRI) offers improved soft-tissue visualization, allowing for better tumor and normal tissue delineation. MR-guided RT (MRgRT) has traditionally been defined by the use of offline MRI to aid in defining the RT volumes during the initial planning stages in order to ensure accurate tumor targeting while sparing critical normal tissues. However, the ViewRay MRIdian and Elekta Unity have improved upon and revolutionized the MRgRT by creating a combined MRI and linear accelerator (MRL), allowing MRgRT to incorporate online MRI in RT. MRL-based MR-guided SBRT (MRgSBRT) represents a novel solution to deliver higher doses to larger volumes of gross disease, regardless of the proximity of at-risk organs due to the (1) superior soft-tissue visualization for patient positioning, (2) real-time continuous intrafraction assessment of internal structures, and (3) daily online adaptive replanning. Stereotactic MR-guided adaptive radiation therapy (SMART) has enabled the safe delivery of ablative doses to tumors adjacent to radiosensitive tissues throughout the body. Although it is still a relatively new RT technique, SMART has demonstrated significant opportunities to improve disease control and reduce toxicity. In this review, we included the current clinical applications and the active prospective trials related to SMART. We highlighted the most impactful clinical studies at various tumor sites. In addition, we explored how MRL-based multiparametric MRI could potentially synergize with SMART to significantly change the current treatment paradigm and to improve personalized cancer care.

This study aimed to assess the incidental radiation exposure of the hippocampus (HC) in locoregionally-advanced oropharyngeal cancer patients undergoing volumetric modulated arc therapy and the feasibility of HC-sparing plan optimization. The initial plans were generated without dose-volume constraints to the HC and were compared with the HC-sparing plans. The incidental Dmean_median doses to the bilateral, ipsilateral and contralateral HC were 2.9, 3.1, and 2.5 Gy in the initial plans and 1.4, 1.6, and 1.3 Gy with HC-sparing. It was feasible to reduce the HC dose with HC-sparing plan optimization without compromising target coverage and/or dose constraints to other OARs.

Objective.Protons offer a more conformal dose delivery compared to photons, yet they are sensitive to anatomical changes over the course of treatment. To minimize range uncertainties due to anatomical variations, a new CT acquisition at every treatment session would be paramount to enable daily dose calculation and subsequent plan adaptation. However, the series of CT scans results in an additional accumulated patient dose. Reducing CT radiation dose and thereby decreasing the potential risk of radiation exposure to patients is desirable, however, lowering the CT dose results in a lower signal-to-noise ratio and therefore in a reduced quality image. We hypothesized that the signal-to-noise ratio provided by conventional CT protocols is higher than needed for proton dose distribution estimation. In this study, we aim to investigate the effect of CT imaging dose reduction on proton therapy dose calculations and plan optimization.Approach.To verify our hypothesis, a CT dose reduction simulation tool has been developed and validated to simulate lower-dose CT scans from an existing standard-dose scan. The simulated lower-dose CTs were then used for proton dose calculation and plan optimization and the results were compared with those of the standard-dose scan. The same strategy was adopted to investigate the effect of CT dose reduction on water equivalent thickness (WET) calculation to quantify CT noise accumulation during integration along the beam.Main results.The similarity between the dose distributions acquired from the low-dose and standard-dose CTs was evaluated by the dose-volume histogram and the 3D Gamma analysis. The results on an anthropomorphic head phantom and three patient cases indicate that CT imaging dose reduction up to 90% does not have a significant effect on proton dose calculation and plan optimization. The relative error was employed to evaluate the similarity between WET maps and was found to be less than 1% after reducing the CT imaging dose by 90%.Significance.The results suggest the possibility of using low-dose CT for proton therapy dose estimation, since the dose distributions acquired from the standard-dose and low-dose CTs are clinically equivalent.

Objective. To develop and evaluate a deep learning based fast volumetric modulated arc therapy (VMAT) plan generation method for prostate radiotherapy.Approach. A customized 3D U-Net was trained and validated to predict initial segments at 90 evenly distributed control points of an arc, linked to our research treatment planning system (TPS) for segment shape optimization (SSO) and segment weight optimization (SWO). For 27 test patients, the VMAT plans generated based on the deep learning prediction (VMATDL) were compared with VMAT plans generated with a previously validated automated treatment planning method (VMATref). For all test cases, the deep learning prediction accuracy, plan dosimetric quality, and the planning efficiency were quantified and analyzed.Main results. For all 27 test cases, the resulting plans were clinically acceptable. TheV95%for the PTV2 was greater than 99%, and theV107%was below 0.2%. Statistically significant difference in target coverage was not observed between the VMATrefand VMATDLplans (P = 0.3243 > 0.05). The dose sparing effect to the OARs between the two groups of plans was similar. Small differences were only observed for the Dmean of rectum and anus. Compared to the VMATref, the VMATDLreduced 29.3% of the optimization time on average.Significance. A fully automated VMAT plan generation method may result in significant improvement in prostate treatment planning efficiency. Due to the clinically acceptable dosimetric quality and high efficiency, it could potentially be used for clinical planning application and real-time adaptive therapy application after further validation.

BACKGROUND: Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time.
METHODS: In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the \"sparsity\" issue.
RESULTS: The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average.
CONCLUSIONS: In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

A new tandem applicator with tungsten shield for Ir-192 radiation source used in intra-cavitary brachytherapy (ICBT) enabled intensity modulated brachytherapy (IMBT) in cervical cancer treatment through fluence-modulation by rotating shield. Our previous work employed group-wise and element-wise sparsity constraints for plan optimization of tandem applicator to minimizes the number of activated angles and source dwell points for delivery efficiency. It, however, did not incorporate the ovoid applicators into the optimizing process, which is generally used to prevent cancer recurrence. To integrate ovoid applicators to the new tandem applicator, this work proposed a comprehensive framework that modifies 1) dose deposition matrix for inverse planning, and 2) plan optimizing algorithm. The dose deposition matrix was newly formulated by the Monte-Carlo simulated dose distribution for 10 positions of ovoid applicators, followed by combining those with tandem-associated dose deposition matrix. The plan optimizing algorithm decomposed entire elements into tandem and ovoid applicators, which were governed by different constraints adaptive to specified plan objectives. The integrated framework was compared against conventional ICBT, and IMBT with tandem only for three patients with asymmetric dose distributions. Integrated IMBT framework resulted in the most optimal plans. Including fluence-modulation by rotating-shield outperformed conventional ICBT in dose sparing to critical organs. Adopting ovoid applicators to the optimization yielded more conformal dose distribution around inferior, laterally expanded region of target volume. The resulting plans reduced D5cc and D2cc by 30.9% and 27.8% for critical organs over conventional ICBT, and by 20.6% and 21.5% for target volume over IMBT with tandem only.

Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage ofanisotropicradiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today\'s fancy is tomorrow\'s reality. The future is bright for brachytherapy.