SU‐E‐J‐199: An Image‐Based Model of Glioblastoma Growth for Treatment Response Assessment

• Published in: Medical Physics 2013-06, Vol.40 (6), p.197-197
• Main Authors: Schreibmann, E, Crocker, I, Shu, H, Fox, T
• Format: Article
• Language: English
• Subjects: Cancer; Cell growth; Diffusion; Magnetic resonance imaging; Medical image segmentation; Medical imaging; Radiation therapy; Radiation treatment; Tissue engineering; Tissues
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4814411

Purpose: To develop a mathematical model of tumor growth based on MRI imaging to predict tumor growth, with potential applications in treatment response assessment to differentiate responding and non‐responding tumor types. Method: The model is composed of 3 terms describing tumor proliferation, diffusion in various tissue types, and radiation treatment effect. The proliferation terms describes the rate at which the cell replicates while the diffusion term describes tumor invasion into adjacent tissue. The radiotherapy effect term models cell response to radiation through a linear quadratic radiobiological model. Tissue‐specific evolution taking into account the differential diffusion in white and gray matter is accomplished by segmenting T1Pre, T1POST and FLAIR images using a support vector machine (SVM) algorithm. Patient specific model parameters of perfusion and diffusion are deduced from 3 serial apparent diffusion coefficients (ADC) imaging maps that are evolved under the guidance of a partial differential equation to deduce tumor evolution at later times. Results: The model tumor was applied on 19 glioblastoma patients and initialized from their ADC maps acquired before, after and 1 month after treatment completion. Voxel wise maps of the perfusion, diffusion and tumor spreading speed were created to assess treatment efficiency. The proliferation was measured in region enhancing in the pre‐treatment imaging and ranged between −0.0189 and 0.00820 mm/day while the diffusion coefficients ranged between 0.0317 and 0.4721 per day. Conclusion: We have developed a tumor growth model and applied it on clinical patients to estimate response to treatment. The model may find also applications follow‐up method for radiation treatments as well as in designing treatment margins that take into account tumor diffusion speed and direction.