A Prospective Study of Machine Learning-Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain.

Purpose: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planning efficiency for adults and children with primary brain tumors.

Methods And Materials: In this prospective study, children and adults receiving 54 Gy fractionated RT for a primary brain tumor were enrolled.

A total inverse planning paradigm: Prospective clinical trial evaluating the performance of a novel MR-based 3D-printed head immobilization device.

Background And Purpose: Brain radiotherapy (cnsRT) requires reproducible positioning and immobilization, attained through redundant dedicated imaging studies and a bespoke moulding session to create a thermoplastic mask (T-mask). Innovative approaches may improve the value of care. We prospectively deployed and assessed the performance of a patient-specific 3D-printed mask (3Dp-mask), generated solely from MR imaging, to replicate a reproducible positioning and tolerable immobilization for patients undergoing cnsRT.

Technique for stereotactic body radiotherapy for spinal metastases.

Stereotactic body radiotherapy (SBRT) is an emerging technique for spinal tumours that is a natural succession to brain radiosurgery. The spine is an ideal site for SBRT due to its relative immobility and the potential clinical benefits of high dose delivery, particularly to optimise local control and avoid disease progression that can result in spinal cord compression. However, the proximity of the tumour to the spinal cord, with the potential for radiation myelopathy if the dose is delivered inaccurately or if the spinal cord dose limit is set too high, demands technical accuracy with radiation myelopathy a feared complication.