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RADIOTHERAPY AUTOMATION

UCSD has been a leader in developing and clincally implementing automation tools in radiotherapy. The aim of automation in radiation oncology is to utilize the capabilities of new computational and modeling methods to ensure the highest quality treatments for cancer patients while simultaneously reducing the resources required to deliver that care.

Knowledge-based external beam planning

UCSD has led the world in the clinical implementation of knowledge-based planning, an automated planning technique that utilizes the information of large sample of prior radiotherapy treatments to automatically design the treatment of new patients. In addition to facilitating more efficient treatment design processes, this technique has been shown by UCSD researchers to ensure higher quality treatment plans for patients. 


To learn more about how external beam knowledge-based planning is improving patient care, check out the following faculty labs:

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Kisling Lab

Kisling Lab


Moore Lab

Moore Lab


Ray Lab

Ray Lab


Automated brachytherapy treatment planning

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Because its delivery method is so distinct from external beam radiotherapy, brachytherapy presents a unique challenge for patient-specific dose estimation. This effort is dedicated to generating accurate knowledge-based dosimetric predictions in the service of distributed treatment plan quality control for GYN brachytherapy. We are also actively exploring automated planning techniques based on knowledge-based dose predictions, as well as three-dimensional dose estimation for both standard applicators and non-standard needle-based treatments.

 

Check out Faculty Labs investigating brachytherapy automation:

 

Meyers Lab

Meyers Lab


Moore Lab

Moore Lab


Large-scale data mining

To answer the most critical questions of how automation is performing relative the standard processes, it is necessary to process an incredibly large amount of data efficiently. To "automate the automation," UCSD researchers have developed and are utilizing novel batch processing techniques which facilitate efficient retrospective analyses and the design of new processes that best leverage automation's capabilities. 


To learn more about how these data mining efforts are leading to new innovations, check out the following Faculty Labs:

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Kisling Lab

Kisling Lab


Moore Lab

Moore Lab


Murphy Lab

Murphy Lab


 

Ray Lab

Ray Lab


Rose Lab

Rose Lab