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Exploring AI for radiotherapy planning with Mayo Clinic

More than 18 million new cancer cases are diagnosed globally each year, and radiotherapy is one of the most common cancer treatments—used to treat over half of cancers in the United States. But planning for a course of radiotherapy treatment is often a time-consuming and manual process for clinicians. The most labor-intensive step in planning is a technique called “contouring” which involves segmenting both the areas of cancer and nearby healthy tissues that are susceptible to radiation damage during treatment. Clinicians have to painstakingly draw lines around sensitive organs on scans—a time-intensive process that can take up to seven hours for a single patient.

Technology has the potential to augment the work of doctors and other care providers, like the specialists who plan radiotherapy treatment. We’re collaborating with Mayo Clinic on research to develop an AI system that can support physicians, help reduce treatment planning time and improve the efficiency of radiotherapy. In this research partnership, Mayo Clinic and Google Health will work to develop an algorithm to assist clinicians in contouring healthy tissue and organs from tumors, and conduct research to better understand how this technology could be deployed effectively in clinical practice. 

Mayo Clinic is an international center of excellence for cancer treatment with world-renowned radiation oncologists. Google researchers have studied how AI can potentially be used to augment other areas of healthcare—from mammographies to the early deployment of an AI system that detects diabetic retinopathy using eye scans. 

In a previous collaboration with University College London Hospitals, Google researchers demonstrated how an AI system could analyze and segment medical scans of patients with head and neck cancer— similar to how expert clinicians would. Our research with Mayo Clinic will also focus on head and neck cancers, which are particularly challenging areas to contour, given the many delicate structures that sit close together. 

In this first phase of research with Mayo Clinic, we hope to develop and validate a model as well as study how an AI system could be deployed in practice. The technology will not be used in a clinical setting and algorithms will be developed using only de-identified data. 

While cancer rates continue to rise, the shortage of radiotherapy experts continues to grow as well. Waiting for a radiotherapy treatment plan can be an agonizing experience for cancer patients, and we hope this research will eventually support a faster planning process and potentially help patients to access treatment sooner.

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