Fast-Track proposals will not be accepted.
Number of anticipated awards: 2-3
Budget (total costs, per award):
PROPOSALS THAT EXCEED THE BUDGET OR PROJECT DURATION LISTED ABOVE MAY NOT BE FUNDED.
The goal of this topic is to see if Artificial Intelligence (AI) technology can be used to improve treatment planning for prostate cancer by developing algorithms to “read” standard Computerized Tomography (CT) images in context with clinical information and recommend suitable treatment plan approaches. The resulting tool may aid radiation oncologists in reaching unbiased consensus treatment planning, help train junior radiation oncologists, update practitioners, reduce professional costs, and improve quality assurance in clinical trials and patient care. Treatment planning for radiation therapy has become increasingly complex with the advent of image-guided radiation therapy and charged particle therapy. A substantial amount of physician time and effort are required to contour key tumor and normal tissue structures. The process involves assessing the patient’s risk for disease progression based on tumor volume; histological grade and biomarkers (e.g., prostate specific antigen or other tests); and assigning one of three risk groups as defined in the National Comprehensive Cancer Network (NCCN) guidelines: low, intermediate, or high. See NCCN guidelines here: https://www.trikobe.org/nccn/guideline/urological/english/prostate.pdf. Radiation treatment will use external beam radiation with or without androgen deprivation. Imaging uses CT and often magnetic resonance imaging. Based on these, the physician and medical physicists plan the target volume to be treated, radiation dose, and normal tissue to be spared. In practice, treatment guidelines are established by consensus papers. However, proposed plans among even world renowned experts often differ. Thus, it may be possible to go beyond verbal consensus text and understand the rationale among expert “preferences” in treatment plans by using AI-based contextual image analysis that uses feature extracting algorithms and/or interactive machine learning to formulate treatment plan. Such an approach would provide an initial plan to the physician upon which to facilitate treatment planning, build consensus, and help understand expert thinking.
The goal of this contract topic is to develop and evaluate the concept that AI can be used to understand and duplicate experts’ radiation therapy planning. The purpose is to understand how human cognition performs in work, focused in the context of developing radiation therapy treatment plans, and then incorporate such an understanding into machine learning with the intent to automate treatment planning to reduce subjective biases, improve treatment quality, and reduce cost. This contract topic does not intend to achieve a breakthrough in AI technology. The objective is to integrate recent advances in treatment planning systems and machine learning to improve radiation therapy by eliminating repetitive, time-consuming, and subjective biases in treatment delivery. Subjective biases could result in normal tissue injury and compromise therapeutic benefit. Machine learning approaches may involve extraction of relevant features from “consensus image datasets” of expert medical teams and then applying them to train machines with an initial focus on prostate cancer. The broad and highly impactful goal is to improve the outcome for patients with prostate cancer. By developing knowledge-based planning solutions, it may be possible to provide a more standardized treatment at a significantly lower cost. This may facilitate quality assurance, possibly extending it to facilities with limited expert personnel and enabling the conduct of research by reducing the variability and potential arbitrariness and/or preference that individuals incorporate in their treatment design. The goal of this project is to encourage creative small businesses to design, develop, and build approaches to AI-based treatment planning systems to improve radiation therapy. Progress here could be applied to other disease sites.
Activities supported by this topic:
Proposals that develop AI software that only outlines tumor and normal tissues but does not select a treatment plan for the three risk groups will not be considered for funding.
Closing date: October 20, 2017, 5:00 PM Eastern Daylight Time
Apply for this topic on the Contract Proposal Submission (eCPS) website.
Full FY18 Contract Solicitation is available HERE.