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

• Published in: International journal of radiation oncology, biology, physics Vol. 119; no. 5; pp. 1429 - 1436
• Main Authors: Tsang, Derek S., Tsui, Grace, Santiago, Anna T., Keller, Harald, Purdie, Thomas, Mcintosh, Chris, Bauman, Glenn, La Macchia, Nancy, Parent, Amy, Dama, Hitesh, Ahmed, Sameera, Laperriere, Normand, Millar, Barbara-Ann, Liu, Valerie, Hodgson, David C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2024
• Subjects: Adolescent; Adult; Aged; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - radiotherapy; Child; Child, Preschool; Dose Fractionation, Radiation; Female; Humans; Machine Learning; Male; Middle Aged; Organs at Risk - radiation effects; Prospective Studies; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; Young Adult
• ISSN: 0360-3016; 1879-355X; 1879-355X
• DOI: 10.1016/j.ijrobp.2024.02.022

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. 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. 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). 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.