Quantitative Radiology Solutions Set To Improve Radiotherapy Treatment Planning

Quantitative Radiology Solutions Set To Improve Radiotherapy Treatment Planning
Radiotherapy is an important tool for treating cancer, and despite its serious risk about half of all cancer patients receive it. Rapid and accurate treatment planning is key, and tumor and organ structures are identified on MRI and CT images via contouring to maximize delivery of radiation to the cancer while minimizing exposure of healthy organs. However, contouring is still performed with low levels of automation and is time-consuming and an error-prone process. Developing new technologies could not only help reduce costs, but lead to improved patient outcomes with fewer side effects.

The Philadelphia-based startup Quantitative Radiology Solutions is determined to improve physician decision-making, and help the treatment planning process by quantification of medical images. Their product, Automatic Anatomy Recognition software, uses MRI, CT and PET/CT images to recognize, delineate and identify anatomical structures and diseased tissue, accelerating medical image analysis. The software reduces the amount of time required to delineate organs from several hours to less than 5 minutes, and can be used as a standalone solution or in integration with existing radiation treatment planning systems.

Radiotherapy treatment planning can be lengthy, and the contouring process can take several hours. In addition, changes occur in tumor and organ anatomy during the course of treatment and re-contouring is rarely done due to the time-consuming nature, leading to serious side effects. Quantitative Radiology Solutions has developed an innovative solution that seek to speed the treatment planning process, and improve physicians’ productivity. The software not only dramatically reduce the time oncologists and dosimetrists need to delineate organs, significantly increasing productivity, but it could allow physicians to easier visualize changes to the tumor and surrounding tissue during the treatment process. This allow physicians to re-plan dose delivery, reducing side effects and significantly improving patient outcomes. The faster and more accurate planning solution will reduce associated cost and allow highly skilled professionals more time with patients.

Quantitative Radiology Solutions was founded in 2013 by Jayaram Udupa and Drew Torigian, a computer scientist and radiologist from the University of Pennsylvania. The startup won “Best on INVEST Pitch Perfect” 2017 in the Health IT category, a pitch competition by MedCity News hosting promising startups trying to solve some of the biggest healthcare problems, and was selected as one of the Top 30 Life Sciences startups in the RESI Innovation Challenge. They have previously secured Phase 1 STTR grants from NSF and NCI, and in 2016 raised $213,000 from Phase 1 Ventures, an accelerator program at Philadelphia’s University City Science Center.