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Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.

ABSTRACT: Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model's forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.

SUBMITTER: Hormuth DA

PROVIDER: S-EPMC8067722 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis. METHODS:We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n?=?13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20?Gy or 40?Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient). RESULT:The selected model resulted in <?12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient?>?0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient?>?0.77 and?>?0.65, respectively). CONCLUSIONS:This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.

Project description:PURPOSE:To develop and validate a technique for radiation therapy gating using slow (?1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments. METHODS AND MATERIALS:The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported. RESULTS:Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm. CONCLUSIONS:This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.

Project description:PurposeTo identify key dosimetric parameters that have close associations with tumor treatment response and body weight change in SFRT treatments with a large range of spatial-fractionation scale at dose rates of several Gy/min.MethodsSix study arms using uniform tumor radiation, half-tumor radiation, 2mm beam array radiation, 0.3mm minibeam radiation, and an untreated arm were used. All treatments were delivered on a 320kV x-ray irradiator. Forty-two female Fischer 344 rats with fibrosarcoma tumor allografts were used. Dosimetric parameters studied are peak dose and width, valley dose and width, peak-to-valley-dose-ratio (PVDR), volumetric average dose, percentage volume directly irradiated, and tumor- and normal-tissue EUD. Animal survival, tumor volume change, and body weight change (indicative of treatment toxicity) are tested for association with the dosimetric parameters using linear regression and Cox Proportional Hazards models.ResultsThe dosimetric parameters most closely associated with tumor response are tumor EUD (R2 = 0.7923, F-stat = 15.26*; z-test = -4.07***), valley (minimum) dose (R2 = 0.7636, F-stat = 12.92*; z-test = -4.338***), and percentage tumor directly irradiated (R2 = 0.7153, F-stat = 10.05*; z-test = -3.837***) per the linear regression and Cox Proportional Hazards models, respectively. Tumor response is linearly proportional to valley (minimum) doses and tumor EUD. Average dose (R2 = 0.2745, F-stat = 1.514 (no sig.); z-test = -2.811**) and peak dose (R2 = 0.04472, F-stat = 0.6874 (not sig.); z-test = -0.786 (not sig.)) show the weakest associations to tumor response. Only the uniform radiation arm did not gain body weight post-radiation, indicative of treatment toxicity; however, body weight change in general shows weak association with all dosimetric parameters except for valley (minimum) dose (R2 = 0.3814, F-stat = 13.56**), valley width (R2 = 0.2853, F-stat = 8.783**), and peak width (R2 = 0.2759, F-stat = 8.382**).ConclusionsFor a single-fraction SFRT at conventional dose rates, valley, not peak, dose is closely associated with tumor treatment response and thus should be used for treatment prescription. Tumor EUD, valley (minimum) dose, and percentage tumor directly irradiated are the top three dosimetric parameters that exhibited close associations with tumor response.

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Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.

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