Bridging the Gap of Radiotherapy Treatment Planning Quality between High-Income, and Low- and Middle-Income Countries Using Knowledge-Based Planning

Abstract

Radiotherapy departments in low- and middle-income countries (LMICs) like Guatemala have recently introduced intensity-modulated radiotherapy (IMRT). IMRT has become the standard of care in high-income countries (HIC) due to reduced toxicity and improved outcomes in some cancers. The purpose of this work is to show the feasibility of adapting knowledge-based (KB) models established in a HIC to a LMIC lacking experience in IMRT to improve plan quality and planning efficiency. A Halcyon Linac was installed at our clinic in Guatemala in 2019 and has been used to treat approximately 90 patients daily with IMRT. A model developed on a cohort of head and neck cancer patients at a US academic radiotherapy center were applied at our center to create 20head and neck VMAT plans with different prescriptions, including simultaneous-integrated and sequential boosts. The plans created using the KB models achieved similar coverage of the planning target volume for each plan KB plans showed better 1) Parotid sparing with a mean dose reduction between 5%-25% and spinal cord maximum dose reduction between 3%-15%. The time efficiency to create VMAT plans using KB model versus manual planning improved four-fold, on average one hour versus more than 4 hours, respectively. Despite different prescriptions, guidelines and demographics of cancer patients between two institutions in a HIC and LMIC, this work demonstrates that KB planning can be used to generate better and more consistent VMAT plans versus manually created plans. In addition, KB planning has the potential to greatly increase planning efficiency higher efficiency and help address the shortage of medical physicists and dosimetrists in LMICs.