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.

Public Health Relevance

The goals of the project are to (1) acquire expertise in the medical radiation physics field to achieve the goals of the candidate's career development plan to become a high- caliber independent investigator and (2) investigate issues related to and develop an effective and efficient robust optimization scheme for intensity-modulated proton therapy (IMPT) to treat cancers in which the target is close to or side the organs at risk and has measurable regular or irregular anatomy motion. We expect this project to result in reduced margins, reduced dose to normal tissues and greater increased dose delivery capability in future patients receiving proton radiation therapy for treatment of cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Mentored Quantitative Research Career Development Award (K25)
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Subcommittee G - Education (NCI)
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Jakowlew, Sonia B
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University of Texas MD Anderson Cancer Center
Other Domestic Higher Education
United States
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Liu, Chenbin; Sio, Terence T; Deng, Wei et al. (2018) Small-spot intensity-modulated proton therapy and volumetric-modulated arc therapies for patients with locally advanced non-small-cell lung cancer: A dosimetric comparative study. J Appl Clin Med Phys 19:140-148
Shan, Jie; An, Yu; Bues, Martin et al. (2018) Robust optimization in IMPT using quadratic objective functions to account for the minimum MU constraint. Med Phys 45:460-469
Liu, Chenbin; Schild, Steven E; Chang, Joe Y et al. (2018) Impact of Spot Size and Spacing on the Quality of Robustly Optimized Intensity Modulated Proton Therapy Plans for Lung Cancer. Int J Radiat Oncol Biol Phys 101:479-489
An, Yu; Shan, Jie; Patel, Samir H et al. (2017) Robust intensity-modulated proton therapy to reduce high linear energy transfer in organs at risk. Med Phys 44:6138-6147
Zhang, Pengfei; Fan, Neng; Shan, Jie et al. (2017) Mixed integer programming with dose-volume constraints in intensity-modulated proton therapy. J Appl Clin Med Phys 18:29-35
Zaghian, Maryam; Cao, Wenhua; Liu, Wei et al. (2017) Comparison of linear and nonlinear programming approaches for ""worst case dose"" and ""minmax"" robust optimization of intensity-modulated proton therapy dose distributions. J Appl Clin Med Phys 18:15-25
Shen, Jiajian; Lentz, Jarrod M; Hu, Yanle et al. (2017) Using field size factors to characterize the in-air fluence of a proton machine with a range shifter. Radiat Oncol 12:52
An, Yu; Liang, Jianming; Schild, Steven E et al. (2017) Robust treatment planning with conditional value at risk chance constraints in intensity-modulated proton therapy. Med Phys 44:28-36
Liu, Wei; Patel, Samir H; Harrington, Daniel P et al. (2017) Exploratory study of the association of volumetric modulated arc therapy (VMAT) plan robustness with local failure in head and neck cancer. J Appl Clin Med Phys 18:76-83
Liu, Wei; Schild, Steven E; Chang, Joe Y et al. (2016) Exploratory Study of 4D versus 3D Robust Optimization in Intensity Modulated Proton Therapy for Lung Cancer. Int J Radiat Oncol Biol Phys 95:523-33

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