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. For each patient, one ML-assisted RT plan was created and compared with 1 or 2 plans created using standard ("manual") planning procedures. Plans were evaluated by the treating oncologist, who was blinded to the method of plan creation. The primary endpoint was the proportion of ML plans that were clinically acceptable for treatment. Secondary endpoints included the frequency with which ML plans were selected as preferable for treatment, and dosimetric differences between ML and manual plans. RESULTS: A total of 116 manual plans and 61 ML plans were evaluated across 61 patients. Ninety-four percent of ML plans and 93% of manual plans were judged to be clinically acceptable (P = 1.0). Overall, the quality of ML plans was similar to manual plans. ML plans comprised 34.5% of all plans evaluated and were selected for treatment in 36.1% of cases (P = .82). Similar tumor target coverage was achieved between both planning methods. Normal brain (brain minus planning target volume) received an average of 1 Gy less mean dose with ML plans (compared with manual plans, P < .001). ML plans required an average of 45.8 minutes less time to create, compared with manual plans (P < .001). CONCLUSIONS: ML-assisted automated planning creates high-quality plans for patients with brain tumors, including children. Plans created with ML assistance delivered slightly less dose to normal brain tissues and can be designed in less time.

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. Material and methods: Patients undergoing LINAC-based cnsRT (primary tumors or resected metastases) were enrolled into two arms: control (T-mask) and investigational (3Dp-mask). For the latter, an in-house designed 3Dp-mask was generated from MR images to recreate the head positioning during MR acquisition and allow coupling with the LINAC tabletop. Differences in inter-fraction motion were compared between both arms. Tolerability was assessed using patient-reported questionnaires at various time points. Results: Between January 2020 - July 2022, forty patients were enrolled (20 per arm). All participants completed the prescribed cnsRT and study evaluations. Average 3Dp-mask design and printing completion time was 36 h:50 min (range 12 h:56 min - 42 h:01 min). Inter-fraction motion analyses showed three-axis displacements comparable to the acceptable tolerance for the current standard-of-care. No differences in patient-reported tolerability were seen at baseline. During the last week of cnsRT, 3Dp-mask resulted in significantly lower facial and cervical discomfort and patients subjectively reported less pressure and confinement sensation when compared to the T-mask. No adverse events were observed. Conclusion: The proposed total inverse planning paradigm using a 3D-printed immobilization device is feasible and renders comparable inter-fraction performance while offering a better patient experience, potentially improving cnsRT workflows and its cost-effectiveness.