Congratulations Drs. Holdsworth and Corwin on their new paper in Physics in Medicine and Biology

Adaptive IMRT using a multiobjective evolutionary algorithm integrated with a diffusion-invasion model of glioblastoma.


Department of Radiation Oncology, University of Washington Medical Center, 1959 N E Pacific Street, Seattle, WA 98195, USA. Department of Radiation Oncology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115, USA.


We demonstrate a patient-specific method of adaptive IMRT treatment for glioblastoma using a multiobjective evolutionary algorithm (MOEA). The MOEA generates spatially optimized dose distributions using an iterative dialogue between the MOEA and a mathematical model of tumor cell proliferation, diffusion and response. Dose distributions optimized on a weekly basis using biological metrics have the potential to substantially improve and individualize treatment outcomes. Optimized dose distributions were generated using three different decision criteria for the tumor and compared with plans utilizing standard dose of 1.8 Gy/fraction to the CTV (T2-visible MRI region plus a 2.5 cm margin). The sets of optimal dose distributions generated using the MOEA approach the Pareto Front (the set of IMRT plans that delineate optimal tradeoffs amongst the clinical goals of tumor control and normal tissue sparing). MOEA optimized doses demonstrated superior performance as judged by three biological metrics according to simulated results. The predicted number of reproductively viable cells 12 weeks after treatment was found to be the best target objective for use in the MOEA.

Presentations at 2012 Annual SNO Meeting

Ten abstracts including 9 posters and 1 talk from the Swanson research lab have been accepted for presentation at the annual Soceity for NeuroOncology meeting in Washington D.C. Wide-ranging and novel results include clinically translational applications of patient-specific mathematical modeling using IDH-1, stem cell transplant therapy, radiation therapy optimization and predictive outcomes based on extent of surgery.

See selected abstracts in the special issue of Neuro-Oncology here

Discriminating time to progression and survival using a response metric tuned to patient-specific glioblastoma kinetics

New manuscript published in PLoS ONE (in press).

Discriminating time to progression and survival using a response metric tuned to patient-specific glioblastoma kinetics

Neal ML, Trister AD, Cloke T, Sodt R, Ahn S, Baldock AL, Bridge CA, Boone A, Rockne R, Swanson KR.

Accurate clinical assessment of a patient’s response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients’ tumors, we developed a method for assessing response that discriminates progression-free and overall survival following therapy for GBM. Applying these models as untreated virtual controls, we generate a patient-specific “Days Gained” response metric that estimates the number of days a therapy delayed imageable tumor progression. We assessed treatment response in terms of Days Gained scores for 33 patients at the time of their first MRI scan following first-line radiation therapy. Based on Kaplan-Meier analyses, patients with significant treatment response (characterized by Days Gained scores of 100 or more) had improved progression-free survival and improved overall survival. Our results demonstrate that the Days Gained response metric calculated at the routinely acquired first post-radiation treatment time point provides prognostic information regarding progression and survival outcomes. Applied prospectively, our model-based approach has the potential to improve GBM treatment by accounting for patient-to-patient heterogeneity in GBM dynamics and responses to therapy.

Virtual FMISO-PET Manuscript Published

Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images

S Gu; G Chakraborty; K Champley; A M Alessio; J Claridge; R Rockne; M Muzi; K A Krohn; A M Spence; E C Alvord Jr; A R A Anderson; P E Kinahan; K R Swanson
Mathematical Medicine and Biology 2011

Read the article for free here:

Article DOI10.1093/imammb/dqr002

IDH1 manuscript downloaded over 500 times!

The role of IDH1 mutated tumour cells in secondary glioblastomas:

an evolutionary game theoretical view

in Physical Biology, Vol 8, pp015016 (2011), has been downloaded 500 times so far.

This was achieved in 85 days from the date of publication. To put this into context, across all IOP journals 3% of articles were accessed over 500 times this year.

The article is free to read here: