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 using a priori multi-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.

Corrigendum: Estimation of the effects of radiotherapy treatment delays on tumour responses: A review

No abstract available.

Conventional and Algorithmic Music Listening before Radiotherapy Treatment: A Randomized Controlled Pilot Study

Music listening is a widespread approach in the field of music therapy. In this study, the effects of music listening on anxiety and stress in patients undergoing radiotherapy are investigated. Sixty patients with breast cancer who were candidates for postoperative curative radiotherapy were recruited and randomly assigned to three groups: Melomics-Health (MH) group (music listening algorithmically created, n = 20); individualized music listening (IML) group (playlist of preferred music, n = 20); no music group (n = 20). Music listening was administered for 15 min immediately before simulation and during the first five radiotherapy sessions. The State-Trait Anxiety Inventory (STAI) and the Psychological Distress Inventory (PDI) were administered before/after treatment. Cochran’s Q test and McNemar test for paired proportions were performed to evaluate if the proportion of subjects having an outcome score below the critical value by treatment and over time was different, and if there was a change in that proportion. The MH group improved in STAI and PDI. The IML group worsened in STAI at T1 and improved STAI-Trait at T2. The IML group worsened in PDI at T2. The No music group generally improved in STAI and PDI. Clinical and music listening-related implications are discussed defining possible research perspectives in this field.

Study Design: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

This document reports the design of a retrospective study to validate the clinical acceptability of a deep-learning-based model for the autosegmentation of organs-at-risk (OARs) for use in radiotherapy treatment planning for head & neck (H&N) cancer patients.

On the Evaluation of a Novel Hypoxic 3D Pancreatic Cancer Model as a Tool for Radiotherapy Treatment Screening

Tissue engineering is evolving to mimic intricate ecosystems of tumour microenvironments (TME) to more readily map realistic in vivo niches of cancerous tissues. Such advanced cancer tissue models enable more accurate preclinical assessment of treatment strategies. Pancreatic cancer is a dangerous disease with high treatment resistance that is directly associated with a highly complex TME. More specifically, the pancreatic cancer TME includes (i) complex structure and complex extracellular matrix (ECM) protein composition; (ii) diverse cell populations (e.g., stellate cells), cancer associated fibroblasts, endothelial cells, which interact with the cancer cells and promote resistance to treatment and metastasis; (iii) accumulation of high amounts of (ECM), which leads to the creation of a fibrotic/desmoplastic reaction around the tumour; and (iv) heterogeneous environmental gradients such as hypoxia, which result from vessel collapse and stiffness increase in the fibrotic/desmoplastic area of the TME. These unique hallmarks are not effectively recapitulated in traditional preclinical research despite radiotherapeutic resistance being largely connected to them. Herein, we investigate, for the first time, the impact of in vitro hypoxia (5% O2) on the radiotherapy treatment response of pancreatic cancer cells (PANC-1) in a novel polymer (polyurethane) based highly macroporous scaffold that was surface modified with proteins (fibronectin) for ECM mimicry. More specifically, PANC-1 cells were seeded in fibronectin coated macroporous scaffolds and were cultured for four weeks in in vitro normoxia (21% O2), followed by a two day exposure to either in vitro hypoxia (5% O2) or maintenance in in vitro normoxia. Thereafter, in situ post-radiation monitoring (one day, three days, seven days post-irradiation) of the 3D cell cultures took place via quantification of (i) live/dead and apoptotic profiles and (ii) ECM (collagen-I) and HIF-1a secretion by the cancer cells. Our results showed increased post-radiation viability, reduced apoptosis, and increased collagen-I and HIF-1a secretion in in vitro hypoxia compared to normoxic cultures, revealing hypoxia-induced radioprotection. Overall, this study employed a low cost, animal free model enabling (i) the possibility of long-term in vitro hypoxic 3D cell culture for pancreatic cancer, and (ii) in vitro hypoxia associated PDAC radio-protection development. Our novel platform for radiation treatment screening can be used for long-term in vitro post-treatment observations as well as for fractionated radiotherapy treatment.