Dosimetric accuracy of delivering SBRT using dynamic arcs on Cyberknife

Impact of technological advances in treatment planning, image guidance, and treatment delivery on target margin design for prostate cancer radiotherapy: an updated review

Recent innovations in image guidance, treatment delivery, and adaptive radiotherapy (RT) have created a new paradigm for planning target volume (PTV) margin design for patients with prostate cancer. We performed a review of the recent literature on PTV margin selection and design for intact prostate RT, excluding post-operative RT, brachytherapy, and proton therapy. Our review describes the increased focus on prostate and seminal vesicles as heterogenous deforming structures with further emergence of intra-prostatic GTV boost and concurrent pelvic lymph node treatment. To capture recent innovations, we highlight the evolution in cone beam CT guidance, and increasing use of MRI for improved target delineation and image registration and supporting online adaptive RT. Moreover, we summarize new and evolving image-guidance treatment platforms as well as recent reports of novel immobilization strategies and motion tracking. Our report also captures recent implementations of artificial intelligence to support image guidance and adaptive RT. To characterize the clinical impact of PTV margin changes via model-based risk estimates and clinical trials, we highlight recent high impact reports. Our report focusses on topics in the context of PTV margins but also showcase studies attempting to move beyond the PTV margin recipes with robust optimization and probabilistic planning approaches. Although guidelines exist for target margins conventional using CT-based image guidance, further validation is required to understand the optimal margins for online adaptation either alone or combined with real-time motion compensation to minimize systematic and random uncertainties in the treatment of patients with prostate cancer.

Planning issues on linac-based stereotactic radiotherapy

This work aims to summarize and evaluate the current planning progress based on the linear accelerator in stereotactic radiotherapy (SRT). The specific techniques include 3-dimensional conformal radiotherapy, dynamic conformal arc therapy, intensity-modulated radiotherapy, and volumetric-modulated arc therapy (VMAT). They are all designed to deliver higher doses to the target volume while reducing damage to normal tissues; among them, VMAT shows better prospects for application. This paper reviews and summarizes several issues on the planning of SRT to provide a reference for clinical application.

TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan

Properly selected beam angles contribute to the quality of radiotherapy treatment plans. However, the beam angle optimization (BAO) problem is difficult to solve to optimality due to its non-convex discrete nature with many local minima. In this study, we propose TBS-BAO, a novel approach for solving the BAO problem, and test it for non-coplanar robotic CyberKnife radiotherapy for prostate cancer. First, an ideal Pareto-optimal reference dose distribution is automatically generated usinga priorimulti-criterial fluence map optimization (FMO) to generate a plan that includes all candidate beams (total-beam-space, TBS). Then, this ideal dose distribution is reproduced as closely as possible in a subsequent segmentation/beam angle optimization step (SEG/BAO), while limiting the number of allowed beams to a user-selectable preset value. SEG/BAO aims at a close reproduction of the ideal dose distribution. For each of 33 prostate SBRT patients, 18 treatment plans with different pre-set numbers of allowed beams were automatically generated with the proposed TBS-BAO. For each patient, the TBS-BAO plans were then compared to a plan that was automatically generated with an alternative BAO method (Erasmus-iCycle) and to a high-quality manually generated plan. TBS-BAO was able to automatically generate plans with clinically feasible numbers of beams (∼25), with a quality highly similar to corresponding 91-beam ideal reference plans. Compared to the alternative Erasmus-iCycle BAO approach, similar plan quality was obtained for 25-beam segmented plans, while computation times were reduced from 10.7 hours to 4.8/1.5 hours, depending on the applied pencil-beam resolution in TBS-BAO. 25-beam TBS-BAO plans had similar quality as manually generated plans with on average 48 beams, while delivery times reduced from 22.3 to 18.4/18.1 min. TBS reference plans could effectively steer the discrete non-convex BAO.

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife

Objective. Despite the high-quality treatment, the long treatment time of the Cyberknife system is believed to be a drawback. The high flexibility of its robotic arm requires meticulous path-finding algorithms to deliver the prescribed dose in the shortest time.Approach. We proposed a Deep Q-learning based on Graph Neural Networks to find the subset of the beams and the order to traverse them. A complex reward function is defined to minimize the distance covered by the robotic arm while avoiding the selection of close beams. Individual beam scores are also generated based on their effect on the beam intensity and are incorporated in the reward function. Main results. The performance of the presented method is evaluated on three clinical cases suffering from lung cancer. Applying this approach leads to an average of 35% reduction in the treatment time while delivering the prescribed dose provided by the physicians.Significance. Shorter treatment times result in a better treatment experience for individual patients, reduces discomfort and the sides effects of inadvertent movements for them. Additionally, it creates the opportunity to treat a higher number of patients in a given time period at the radiation therapy centers.

Prostate re-irradiation: current concerns and future perspectives

INTRODUCTION

To date, the optimal management of locally relapsed prostate cancer patients after an initial course of radiotherapy remains a matter of debate. In recent years, local approaches have been proposed as a therapeutic option, which may potentially delay the initiation of hormone therapy. In the case of external beam radiotherapy (EBRT), re-irradiation has been supported by growing evidence in the literature, mostly represented by extreme hypofractionated schedules delivered with stereotactic body radiotherapy (SBRT).

AREAS COVERED

We performed a systematic review of the literature using the PICO methodology to explore the available evidence regarding the use of EBRT in the setting of locally relapsed prostate cancer, both in terms of safety, tolerability and preliminary clinical outcomes.

EXPERT OPINION

Current literature data report the use of EBRT and particularly of SBRT for the safe and feasible re-treatment of locally recurrent prostate cancer after an initial treatment course of radiotherapy. When extreme hypofractionation is adopted, only occasional grade ≥3 late adverse events are reported. Despite the current lack of high-level evidence and the short follow-up, preliminary clinical outcomes are promising and allow clinicians to hypothesize further prospective studies to evaluate SBRT as an alternative to the early initiation of androgen-deprivation therapy.