K Hendrickson

Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach

Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of the survival of groups of patients treated similarly, but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome. We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard-of-care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumors growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find that the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross-validation technique. This relationship predicts the tumor size post-therapy to within inter-observer tumor volume uncertainty. The results of this study suggest that a mathematical model can create a virtual in silico tumor with the same growth kinetics as a particular patient and can not only predict treatment response in individual patients in vivo but also provide a basis for evaluation of response in each patient to any given therapy.

Hypoxia Imaging with [F-18] FMISO-PET in Head and Neck Cancer: Potential for Guiding Intensity Modulated Radiation Therapy in Overcoming Hypoxia-Induced Treatment Resistance

BACKGROUND AND PURPOSE Positron emission tomography (PET) imaging with [F-18] fluoromisonidazole (FMISO) has been validated as a hypoxic tracer. Head and neck cancer exhibits hypoxia, inducing aggressive biologic traits that impart resistance to treatment. Delivery of modestly higher radiation doses to tumors with stable areas of chronic hypoxia can improve tumor control. Advanced radiation treatment planning (RTP) and delivery techniques such as intensity modulated radiation therapy (IMRT) can deliver higher doses to a small volume without increasing morbidity. We investigated the utility of co-registered FMISO-PET and CT images to develop clinically feasible RTPs with higher tumor control probabilities (TCP). MATERIALS AND METHODS FMISO-PET images were used to determine hypoxic sub-volumes for boost planning. Example plans were generated for 10 of the patients in the study who exhibited significant hypoxia. We created an IMRT plan for each patient with a simultaneous integrated boost (SIB) to the hypoxic sub-volumes. We also varied the boost for two patients. RESULT A significant (mean 17%, median 15%) improvement in TCP is predicted when the modest additional boost dose to the hypoxic sub-volume is included. CONCLUSION Combined FMISO-PET imaging and IMRT planning permit delivery of higher doses to hypoxic regions, increasing the predicted TCP (mean 17%) without increasing expected complications.

Hypoxia imaging-directed radiation treatment planning

Increasing evidence supports the role of the tumor microenvironment in modulating cancer behavior. Tissue hypoxia, an important and common condition affecting the tumor microenvironment, is well established as a resistance factor in radiotherapy. Increasing evidence points to the ability of hypoxia to induce the expression of gene products, which confer aggressive tumor behavior and promote broad resistance to therapy. These factors suggest that determining the presence or absence of tumor hypoxia is important in planning cancer therapy. Recent advances in PET hypoxia imaging, conformal radiotherapy, and imaging-directed radiotherapy treatment planning now make it possible to perform hypoxia-directed radiotherapy. We review the biological aspects of tumor hypoxia and PET imaging approaches for measuring tumor hypoxia, along with methods for conformal radiotherapy and image-guided treatment, all of which provide the underpinnings for hypoxia-directed therapy. As a case example, we review emerging data on PET imaging of hypoxia to direct radiotherapy.

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patients disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [18F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.

Challenges and opportunities in patient-specific, motion-managed and PET/CT-guided radiation therapy of lung cancer: review and perspective

The increasing interest in combined positron emission tomography (PET) and computed tomography (CT) to guide lung cancer radiation therapy planning has been well documented. Motion management strategies during treatment simulation PET/CT imaging and treatment delivery have been proposed to improve the precision and accuracy of radiotherapy. In light of these research advances, why has translation of motion-managed PET/CT to clinical radiotherapy been slow and infrequent? Solutions to this problem are as complex as they are numerous, driven by large inter-patient variability in tumor motion trajectories across a highly heterogeneous population. Such variation dictates a comprehensive and patient-specific incorporation of motion management strategies into PET/CT-guided radiotherapy rather than a one-size-fits-all tactic. This review summarizes challenges and opportunities for clinical translation of advances in PET/CT-guided radiotherapy, as well as in respiratory motion-managed radiotherapy of lung cancer. These two concepts are then integrated into proposed patient-specific workflows that span classification schemes, PET/CT image formation, treatment planning, and adaptive image-guided radiotherapy delivery techniques.

A patient safety education program in a medical physics residency

Abstract Education in patient safety and quality of care is a requirement for radiation oncology residency programs according to accrediting agencies. However, recent surveys indicate that most programs lack a formal program to support this learning. The aim of this report was to address this gap and share experiences with a structured educational program on quality and safety designed specifically for medical physics therapy residencies. Five key topic areas were identified, drawn from published recommendations on safety and quality. A didactic component was developed, which includes an extensive reading list supported by a series of lectures. This was coupled with practice‐based learning which includes one project, for example, failure modes and effect analysis exercise, and also continued participation in the departmental incident learning system including a root‐cause analysis exercise. Performance was evaluated through quizzes, presentations, and reports. Over the period of 2014–2016, five medical physics residents successfully completed the program. Evaluations indicated that the residents had a positive experience. In addition to educating physics residents this program may be adapted for medical physics graduate programs or certificate programs, radiation oncology residencies, or as a self‐directed educational project for practicing physicists. Future directions might include a system that coordinates between medical training centers such as a resident exchange program.

SU-E-P-26: Oncospace: A Shared Radiation Oncology Database System Designed for Personalized Medicine, Decision Support, and Research

Purpose: Advancement in Radiation Oncology (RO) practice develops through evidence based medicine and clinical trial. Knowledge usable for treatment planning, decision support and research is contained in our clinical data, stored in an Oncospace database. This data store and the tools for populating and analyzing it are compatible with standard RO practice and are shared with collaborating institutions. The question is - what protocol for system development and data sharing within an Oncospace Consortium? We focus our example on the technology and data meaning necessary to share across the Consortium. Methods: Oncospace consists of a database schema, planning and outcome data import and web based analysis tools.1) Database: The Consortium implements a federated data store; each member collects and maintains its own data within an Oncospace schema. For privacy, PHI is contained within a single table, accessible to the database owner.2) Import: Spatial dose data from treatment plans (Pinnacle or DICOM) is imported via Oncolink. Treatment outcomes are imported from an OIS (MOSAIQ).3) Analysis: JHU has built a number of webpages to answer analysis questions. Oncospace data can also be analyzed via MATLAB or SAS queries.These materials are available to Consortium members, who contribute enhancements and improvements. Results: 1) The Oncospace Consortium now consists of RO centers at JHU, UVA, UW and the University of Toronto. These members have successfully installed and populated Oncospace databases with over 1000 patients collectively.2) Members contributing code and getting updates via SVN repository. Errors are reported and tracked via Redmine. Teleconferences include strategizing design and code reviews.3) Successfully remotely queried federated databases to combine multiple institutions’ DVH data for dose-toxicity analysis (see below – data combined from JHU and UW Oncospace). Conclusion: RO data sharing can and has been effected according to the Oncospace Consortium model: http://oncospace.radonc.jhmi.edu/. John Wong - SRA from Elekta; Todd McNutt - SRA from Elekta; Michael Bowers - funded by Elekta

Ethical violations and discriminatory behavior in the MedPhys Match

Abstract Purpose The purpose of this survey study is to investigate behaviors in conflict with the ethical standards of the Medical Physics Residency (MedPhys) Match (MPM) process as stated in the MPM rules (a) and with the nondiscrimination regulations of the Equal Employment Opportunity Commission (EEOC) (b), in addition to other behaviors that may in other ways erode the fairness of the system. Methods A survey was sent to all applicants and program directors registered for the 2015 and 2016 MPM. Survey questions asked about application, interview, and postinterview experiences, match results, and overall satisfaction with the process. Results Thirteen percent of 2015 respondents and 20% of 2016 respondents were asked by at least one program how highly they planned to rank them or which program they would rank first. Thirty‐seven percent of 2015 and 40% of 2016 program directors indicated that candidates communicated to the program their rank intent, with 22.0% in 2015 and 12.5% in 2016 being told that their program would be ranked first. Twenty‐three percent of 2015 respondents indicated being asked by at least one program during the interview about children or plans to have children; including 19% of males and 33% of females. In 2016, these values were 28% overall, 22% male, and 36% female. Fifty‐seven percent of 2015 respondents who were asked this question indicated being uncomfortable or very uncomfortable answering, including 27.3% of males and 88.9% of females. In 2016, 42.9% of all respondents indicated being uncomfortable or very uncomfortable answering, including 10.0% of males and 80.0% of females. Conclusions In the first two years of the MPM, there were widespread instances of ethical violations and discriminatory questioning during the interview process. Educating both interviewers and candidates on the MPM rules and general EEOC guidelines should decrease these instances and increase the fairness of the residency selection process.

Navigating the medical physics education and training landscape

Abstract Purpose The education and training landscape has been profoundly reshaped by the ABR 2012/2014 initiative and the MedPhys Match. This work quantifies these changes and summarizes available reports, surveys, and statistics on education and training. Methods We evaluate data from CAMPEP‐accredited program websites, annual CAMPEP graduate and residency program reports, and surveys on the MedPhys Match and Professional Doctorate degree (DMP). Results From 2009–2015, the number of graduates from CAMPEP‐accredited graduate programs rose from 210 to 332, while CAMPEP‐accredited residency positions rose from 60 to 134. We estimate that approximately 60% of graduates of CAMPEP‐accredited graduate programs intend to enter clinical practice, however, only 36% of graduates were successful in acquiring a residency position in 2015. The maximum residency placement percentage for a graduate program is 70%, while the median for all programs is only 22%. Overall residency placement percentage for CAMPEP‐accredited program graduates from 2011–2015 was approximately 38% and 25% for those with a PhD and MS, respectively. The disparity between the number of clinically oriented graduates and available residency positions is perceived as a significant problem by over 70% of MedPhys Match participants responding to a post‐match survey. Approximately 32% of these respondents indicated that prior knowledge of this situation would have changed their decision to pursue graduate education in medical physics. Conclusion These data reveal a substantial disparity between the number of residency training positions and graduate students interested in these positions, and a substantial variability in residency placement percentage across graduate programs. Comprehensive data regarding current and projected supply and demand within the medical physics workforce are needed for perspective on these numbers. While the long‐term effects of changes in the education and training infrastructure are still unclear, available survey data suggest that these changes could negatively affect potential entrants to the profession.

SU-F-P-22: Will the Low Residency Placement Rate Scare Talented Candidates Away from Our Profession?

PURPOSE The low placement rate (∼25%) of medical physics residency match applicants is well-known to medical physics graduate students, and is beginning to be appreciated by prospective students. We examine this placement rate in detail and its potential impact on future recruitment into our profession based on survey data of both match applicants and program directors. METHODS To evaluate and validate residency placement rates, we analyzed annual CAMPEP graduate and residency program reports and data from individual graduate program websites. Programs were excluded if they had no graduates or ambiguous placement statistics. We surveyed medical physics match applicants and directors of CAMPEP-accredited residency programs to gauge their attitudes toward this placement rate. RESULTS Cumulative data extracted from program websites agreed with CAMPEP reports to within 20 graduates (7% error) and 2 residents (2% error) in 2013. The 2013 residency placement rate was 25.5% for MS and 30.8% for PhD graduates. Overall placement rates range from 0% to 66%. 108 match applicants and 40 program directors responded to the survey. The vast majority of graduates (85.2%) responded that the current residency placement rate is a problem for our profession, compared to only 40% of program directors. More strikingly, 40% of match applicants responded that they would reconsider entering the profession if they had prior knowledge of this residency placement rate. CONCLUSION Our current residency placement rate presents a bleak picture to prospective students interested in board certified clinical practice. Survey data indicates that this may deter future students from our profession. While our profession seeks solutions, we have an ethical obligation to ensure that incoming students are fully educated about their professional prospects. Moreover, we must evaluate effects of current initiatives on the attractiveness of our education and training infrastructure to prevent the loss of talented future medical physicists.