SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of novel real-time cloud-based treatment guidance software for radiation cancer treatment (adaptive radiotherapy), which will improve radiation treatment efficacy, patient's quality of life, and ultimately reduce patient re-hospitalizations. Radiotherapy is the most common approach for treating cancer. The radiotherapy treatment procedures happen for multiple days (5-35 days) during which the radiation treatment efficacy may be lowered because of changes in the patient anatomy. This project provides the clinician the ability to obtain information regarding the radiation dose to be delivered to both the tumor as well as the surrounding normal organs before the treatment is delivered. The successful outcome of this project will allow radiation oncologists to plan and deliver advanced treatments where the radiation dose delivered can be quickly modified to suit the current tumor location and motion, to only encompass the tumor and effectively spare normal tissues. The project outcome will also enable physicians at remote clinics to receive guidance from experts to perform online adaptive radiotherapy, enabling a new era of radiotherapy treatment world-wide. The cloud-based framework will work with most image-guided radiotherapy equipment. This Small Business Innovation Research (SBIR) Phase I project will enable development of technology to effectively perform quality assurance tests to determine a more accurate radiation dose and reduce the number of fractions in the treatment of many cancerous tumors. The soft tissue surrounding the cancer varies in its physiological behavior, which alters the treatment efficacy when not accounted for. While a conventional hypothesis would dictate that better tumor targeting during radiotherapy would yield an improved treatment response, patient survival statistics dictate otherwise, indicating that accurate treatment guidance has not been facilitated to date. The research will focus on developing a cloud-based framework of computed tomography and magnetic resonance image processing for fast deformable image registration and radiation dose accumulation estimation, with an automated methodology for quantifying the deformable image registration performance. The technical results may facilitate an automated framework for accurately tracking a patient's anatomy, computing the accumulated dose delivered and reporting dosimetric endpoints for critical structures in near real-time, which will be vital for enabling online adaptive radiotherapy.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is helping Community Cancer Centers serving the estimated 65% of patients receiving radiation therapies. Cancer is still the second leading cause of death in the United States. Currently accomplished only by significant investment in new equipment, successful therapy hinges upon the ability to delineate and adapt to a precise tumor location "on the day of" radiation delivery. Community Centers cannot afford to replace existing machines with more accurate and expensive modalities. Especially in rural communities with limited resources, few people diagnosed with cancer can afford to travel and incur the cost of staying 4-6 weeks in a local hotel to receive daily treatments from state of the art technologies located only in the wealthy institutions. Using a novel cloud-based architecture and proprietary software, this Phase II award will allow remote clinics, and any hospital, to treat patients with highly improved real-time accuracy. By enabling their existing radiotherapy machines to deliver an adaptive dose to the current location of the tumor, radiation oncologists can significantly improve treatment efficacy, a patient's quality of life, reduce patient re-hospitalizations, and reduce the cost of therapy. This Small Business Innovation Research (SBIR) Phase II project addresses the unmet need to see daily changes in anatomy throughout the course of cancer radiation treatment and effectively target only the tumor. Today, a physician designs a treatment plan and dose prescription based on images that are captured days or even weeks prior to initiation of radiation therapy, creating a margin of error around the tumor that encompasses healthy tissues. Unaccounted anatomical changes are quite frequent, leading to a diminished quality of treatment delivery, inferior outcomes, and a decreased quality of life. The phase I project investigated and developed a treatment-equipment agnostic approach to adapt the treatment to account for these anatomical changes. Phase II will focus on integrating artificial intelligence and deep neural analytics to predict two critical parameters for the treatment adaptivity: (a) treatment prognostics and (b) registration error quantification. Leveraging the company's cloud based, scalable, GPU computational framework, the project will develop and integrate patient-specific biomechanical models to automatically validate results and accurately predict future trends in patient-specific anatomical changes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.