Lumonus, developers of Lumonus AI, a platform for radiation oncology clinical workflow and automated treatment planning, have formed a strategic collaboration with Memorial Sloan Kettering Cancer Centre (MSK).
Through this collaboration, Lumonus will license and co-develop MSK’s Echo mathematical optimisation engine into the Lumonus platform and will invest in joint research to advance automated radiation therapy treatment planning.
Tim Fox, chief product officer at Lumonus, said, “Over the last twenty years, automated planning systems have demonstrated real clinical value, improving dosimetric quality, consistency and efficiency across multiple studies. But scaled adoption has remained elusive. The core challenge is that each plan must navigate complex, competing constraints that are specific to the individual patient, and there has been no robust way to consistently produce the optimal solution and confirm it meets the treating physician’s clinical intent.
“Our strategic collaboration with MSK represents a decisive step forward. The next generation of planning automation requires a mathematically principled solver capable of consistently delivering the optimal plan, working in concert with intelligent review tools that capture the nuanced clinical judgement of physicians in real-world practice. Together with MSK, we are building the infrastructure to move automation from promising results to scaled, routine deployment.”
MSK’s Echo optimisation engine uses constraint-based mathematical methods to compute radiation therapy treatment plans from first principles. Created over years of clinical use and supported by a body of peer-reviewed research, Echo is one of the most rigorously validated approaches to automated treatment planning in radiation oncology.
Masoud Zarepisheh, associate attending and lead developer of Echo at MSK, said, “Our goal in developing ECHO has always been to improve the quality, consistency, and efficiency of radiation therapy treatment planning. Our collaboration with Lumonus creates an important opportunity to combine clinically validated optimisation methods with modern workflow tools and help expand the adoption of automated treatment planning in radiation oncology.”
