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 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 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

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

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients’ clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited

Imaging for Assessment of Radiation-Induced Normal Tissue Effects

Imaging can provide quantitative assessment of radiation-induced normal tissue effects. Identifying an early sign of normal tissue damage with imaging would have the potential to predict organ dysfunction, thereby allowing re-optimization of treatment strategies based upon individual patients’ risks and benefits. Early detection with non-invasive imaging may enable interventions to mitigate therapy-associated injury prior to its clinical manifestation. Further, successive imaging may provide an objective assessment of the impact of such mitigation therapies. However, many problems make application of imaging to normal tissue assessment challenging, and further work is required to establish imaging biomarkers as surrogate endpoints of clinical outcome. The performance of clinical trials where normal tissue injury is a clearly defined endpoint would greatly aid in realization of these goals