4D Robust Optimization in Intensity-Modulated Proton Therapy

Project: Research project

Project Details


DESCRIPTION (provided by applicant): The applicant's immediate career goal is to make the transition to a high-caliber independent researcher and to establish a small laboratory in an intense and supportive research environment. In the long term, the applicant hopes to develop a solid academic career focusing on translational research in the area of medical radiation physics that impacts various aspects of the state-of-the-art radiotherapy modalities. The candidate, who obtained his PhD from Princeton University in 2007 and later worked at the Los Alamos National Laboratory as a postdoctoral researcher, has extensive prior experience in computational physics, mathematics and algorithms, and software development, especially code development in massive parallel high-performance computing (HPC). The candidate also has solid analytical and mathematical skills to solve complicated physics problems, along with an interdisciplinary background. In July 2010, the candidate joined the faculty of the Department of Radiation Physics of The University of Texas MD Anderson Cancer Center as an assistant professor (research track). MD Anderson is a research-driven, comprehensive cancer hospital and is the leading cancer center in the United States. The Department of Radiation Physics provides the clinical, research, and educational resources necessary to support physics and dosimetry research related to cancer therapy. The department's Proton Therapy Center-Houston (PTC-H) began patient treatments in May 2006. PTC-H is one of few proton treatment centers in the world to have the capability to deliver intensity- and energy-modulated treatments with scanned proton beams, which is the major focus of the proposed research. During the award period, the candidate will focus on the development and validation of novel advanced radiation therapy methodologies. With the support of this K25 grant, the applicant plans to 1) obtain in-depth knowledge and hands-on experience in the clinical and research aspects of radiation therapy; 2) obtain in- depth knowledge of anatomy; 3) obtain in-depth knowledge of medical imaging; 4) obtain moderate knowledge of biostatistics; and 5) obtain moderate knowledge of radiobiology. To achieve these objectives, the applicant will work with a group of experienced mentors and collaborating researchers on joint projects focusing on the radiotherapy of cancers, take academic courses at Rice University and The University of Texas-Health Science Center at Houston, undergo clinical training in radiation therapy, and attend seminars and conferences. Four-dimensional (4D) robust optimization of intensity-modulated proton therapy (IMPT) has been chosen as the research topic for this K25 training program to help the applicant gain the experience necessary to become an independent investigator. The use of IMPT to treat lung cancers presents numerous challenges that need to be addressed through research to maximize the therapeutic benefits of this promising modality. What the candidate learns during this research will be widely applicable to many areas of research in this field. In principle, IMPT has the greatest potential to provide highly conformal tumor target coverage, while sparing adjacent healthy organs. However, characteristics of protons (e.g., the abrupt drop-off of dose beyond the range and scattering) make IMPT highly vulnerable to uncertainties. Sources of uncertainty include tumor shrinkage, weight loss, variation in patient setup, respiratory motion, uncertainties in CT numbers and stopping power ratios, and approximations in proton dose calculation algorithms. The current practice for managing uncertainties in IMPT is similar to that for intensity-modulated radiation therapy (IMRT), i.e., assigning a safety margin around the clinical target volume to produce a planning target volume. The resulting dose distributions are, in general, not robust in the face of uncertainties, i.e., what is delivered to the patient may be significantly different from what is seen on the computer-designed treatment plan and may lead to unforeseen clinical consequences. Therefore, investigations leading to the development of suitable 4D robust optimization methods to improve the optimality and robustness of IMPT plans to uncertainties, including regular and irregular motion, are vital. Our hypothesis is that 4D robust optimization can render IMPT plans less sensitive to uncertainties and achieve better sparing of normal tissues (both by at least a factor of two) than conventional plans optimized on the basis of margins. We propose to test our hypothesis in the following specific aims: (1) to quantify anatomy motion and its uncertainty; (2) to develop and implement 4D IMPT optimization; (3) to enhance the IMPT plan robustness; and (4) to validate IMPT 4D robust optimization. Compared to previous 4D robust optimization approaches in IMRT, the research proposed by the applicant has several innovative aspects, including a customized, as-small-as-necessary margin optimized spontaneously to handle uncertainties and regular motion, the use of perturbation theory, widely used in quantum mechanics to solve the Schr¿dinger equation, to handle irregular motion, and memory-distributed parallelization to solve the challenging high-computer-memory requirement problem. We expect that our pioneering 4D robust optimization research in IMPT will fill gaps in our knowledge about appropriate ways to minimize the influence of uncertainties in IMPT and lead to significant benefits for cancer patients, especially those with thoracic and abdominal cancers. This project doesn't involve activities outside of the United States or partnerships with international collaborators.
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