Development and testing of a database of NIH research funding of AAPM members: A report from the AAPM Working Group for the Development of a Research Database (WGDRD).

PURPOSE: To produce and maintain a database of National Institutes of Health (NIH) funding of the American Association of Physicists in Medicine (AAPM) members, to perform a top-level analysis of these data, and to make these data (hereafter referred to as the AAPM research database) available for the use of the AAPM and its members. METHODS: NIH-funded research dating back to 1985 is available for public download through the NIH exporter website, and AAPM membership information dating back to 2002 was supplied by the AAPM. To link these two sources of data, a data mining algorithm was developed in Matlab. The false-positive rate was manually estimated based on a random sample of 100 records, and the false-negative rate was assessed by comparing against 99 member-supplied PI_ID numbers. The AAPM research database was queried to produce an analysis of trends and demographics in research funding dating from 2002 to 2015. RESULTS: A total of 566 PI_ID numbers were matched to AAPM members. False-positive and -negative rates were respectively 4% (95% CI: 1-10%, N = 100) and 10% (95% CI: 5-18%, N = 99). Based on analysis of the AAPM research database, in 2015 the NIH awarded $USD 110M to members of the AAPM. The four NIH institutes which historically awarded the most funding to AAPM members were the National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Heart Lung and Blood Institute, and National Institute of Neurological Disorders and Stroke. In 2015, over 85$116M, which is lower than the historic mean of $120M (in 2015 USD). CONCLUSIONS: A database of NIH-funded research awarded to AAPM members has been developed and tested using a data mining approach, and a top-level analysis of funding trends has been performed. Current funding of AAPM members is lower than the historic mean. The database will be maintained by members of the Working group for the development of a research database (WGDRD) on an annual basis, and is available to the AAPM, its committees, working groups, and members for download through the AAPM electronic content website. A wide range of questions regarding financial and demographic funding trends can be addressed by these data. This report has been approved for publication by the AAPM Science Council

The 2019 Mathematical Oncology Roadmap.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between Two Beasts: mathematics and cancer.NIH (R01CA16437, R01NS060752, U54CA210180, U54CA143970, U54193489, U01CA220378)James S. McDonnell FoundationBen & Catherine Ivy Foundatio

A three phase model to investigate the effects of dead material on the growth of avascular tumours

In vivo tumours are highly heterogeneous entities which often comprise intratumoural regions of hypoxia and widespread necrosis. In this paper, we develop a new three phase model of nutrient-limited, avascular tumour growth to investigate how dead material within the tumour may influence the tumour’s growth dynamics. We model the tumour as a mixture of tumour cells, dead cellular material and extracellular fluid. The model equations are derived using mass and momentum balances for each phase along with appropriate constitutive equations. The tumour cells are viewed as a viscous fluid pressure, while the extracellular fluid phase is viewed as inviscid. The physical properties of the dead material are intermediate between those of the tumour cells and extracellular fluid, and are characterised by three key parameters. Through numerical simulation of the model equations, we reproduce spatial structures and dynamics typical of those associated with the growth of avascular tumour spheroids. We also characterise novel, non-monotonic behaviours which are driven by the internal dynamics of the dead material within the tumour. Investigations of the parameter sub-space describing the properties of the dead material reveal that the way in which non-viable tumour cells are modelled may significantly influence the qualitative tumour growth dynamics

The importance of dead material within a tumour on the dynamics in response to radiotherapy

In vivo tumours are highly heterogeneous, often comprising regions of hypoxia and necrosis. Radiotherapy significantly alters the intratumoural composition. Moreover, radiation-induced cell death may occur via a number of different mechanisms that act over different timescales. Dead material may therefore occupy a significant portion of the tumour volume for some time after irradiation and may affect the subsequent tumour dynamics. We present a three phase tumour growth model that accounts for the effects of radiotherapy and use it to investigate how dead material within the tumour may affect the spatio-temporal tumour response dynamics. We use numerical simulation of the model equations to characterise qualitatively different tumour volume dynamics in response to fractionated radiotherapy. We demonstrate examples, and associated parameter values, for which the properties of the dead material significantly alter the observed tumour volume dynamics throughout treatment. These simulations suggest that for some cases it may not be possible to accurately predict radiotherapy response from pre-treatment, gross tumour volume measurements without consideration of the dead material within the tumour.</p

The importance of dead material within a tumour on the dynamics in response to radiotherapy

In vivo tumours are highly heterogeneous, often comprising regions of hypoxia and necrosis. Radiotherapy significantly alters the intratumoural composition. Moreover, radiation-induced cell death may occur via a number of different mechanisms that act over different timescales. Dead material may therefore occupy a significant portion of the tumour volume for some time after irradiation and may affect the subsequent tumour dynamics. We present a three phase tumour growth model that accounts for the effects of radiotherapy and use it to investigate how dead material within the tumour may affect the spatio-temporal tumour response dynamics. We use numerical simulation of the model equations to characterise qualitatively different tumour volume dynamics in response to fractionated radiotherapy. We demonstrate examples, and associated parameter values, for which the properties of the dead material significantly alter the observed tumour volume dynamics throughout treatment. These simulations suggest that for some cases it may not be possible to accurately predict radiotherapy response from pre-treatment, gross tumour volume measurements without consideration of the dead material within the tumour.</p