A Bayesian Mixture Model Relating Dose to Critical Organs and Functional Complication in 3D Conformal Radiation Therapy

A goal of radiation therapy is to deliver maximum dose to the target tumor while minimizing complications due to irradiation of critical organs. Technological advances in 3D conformal radiation therapy has allowed great strides in realizing this goal, however complications may still arise. Critical organs may be adjacent to tumors or in the path of the radiation beam. Several mathematical models have been proposed that describe a relationship between dose and observed functional complication, however only a few published studies have successfully fit these models to data using modern statistical methods which make efficient use of the data. One complication following radiation therapy of head and neck cancers is the patient’s inability to produce saliva. Xerostomia (dry mouth) leads to high susceptibility to oral infection and dental caries and is, in general, unpleasant and an annoyance. We present a dose-damage-injury model that can accommodate any of the various mathematical models relating dose to damage. The model is a non-linear, longitudinal mixed effects model where the outcome (saliva flow rate) is modeled as a mixture of a Dirac measure at zero and a gamma distribution whose mean is a function of time and dose. Bayesian methods are used to estimate the relationship between dose delivered to the parotid glands and the observational outcome – saliva flow rate. A summary measure of the dose-damage relationship is modeled and assessed by a Bayesian x2 test for goodness-of-fit

Quantization of setup uncertainties in 3‐D dose calculations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135022/1/mp8756.pd

Introduction to machine and deep learning for medical physicists

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155469/1/mp14140_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155469/2/mp14140.pd

Combining handcrafted features with latent variables in machine learning for prediction of radiationâ induced lung damage

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/1/mp13497.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/2/mp13497_am.pd

A mathematical model for correcting patient setup errors using a tilt and roll device

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134812/1/mp8797.pd

A practical approach for quantitative estimates of voxel‐by‐voxel liver perfusion using DCE imaging and a compartmental model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135027/1/mp9773.pd

A method for incorporating organ motion due to breathing into 3D dose calculations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134856/1/mp8577.pd

Ideal spatial radiotherapy dose distributions subject to positional uncertainties

In radiotherapy a common method used to compensate for patient setup error and organ motion is to enlarge the clinical target volume (CTV) by a ‘margin’ to produce a ‘planning target volume’ (PTV). Using weighted power loss functions as a measure of performance for a treatment plan, a simple method can be developed to calculate the ideal spatial dose distribution (one that minimizes expected loss) when there is uncertainty. The spatial dose distribution is assumed to be invariant to the displacement of the internal structures and the whole patient. The results provide qualitative insights into the suitability of using a margin at all, and (if one is to be used) how to select a ‘good’ margin size. The common practice of raising the power parameters in the treatment loss function, in order to enforce target dose requirements, is shown to be potentially counter-productive. These results offer insights into desirable dose distributions and could be used, in conjunction with well-established inverse radiotherapy planning techniques, to produce dose distributions that are robust against uncertainties.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58093/2/pmb6_24_004.pd

Deep reinforcement learning for automated radiation adaptation in lung cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/1/mp12625.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/2/mp12625_am.pd

Dose reconstruction in deforming lung anatomy: Dose grid size effects and clinical implications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135133/1/mp9749.pd