Peter van Ooijen

University of Groningen, University Medical Center Groningen

Detection and Prediction of Radiation Toxicity and Tumor Response using Deep Learning - Towards Personalized Adaptive Radiotherapy

With the increased survival rate of cancer patients because of successful treatment with (chemo)radiation, the risk of toxicity and related side effects also increased. Prediction or early detection of toxicity is of utmost importance to allow adaptation of the treatment to avoid toxicity related problems. In addition, the early identification of patients that exhibit a lower expected tumor response allows to choose a more aggressive treatment regime for those patients.

Aim of this project is to facilitate personalized adaptive radiotherapy to improve treatment outcome. We will investigate determination of treatment efficacy (tumor response) and detection and prediction of side effect severity (toxicity) of radiation therapy for head-and-neck cancer using deep learning techniques to identify predictive image features. Ultimately, the goal is to facilitate personalized adaptive radiotherapy, where the treatment plan is adjusted on-the-go based on both the daily anatomical changes and the predictions of the models for toxicity and tumor response.

Brief summary of progress / results as of spring 2021

During the first months of the project, a detailed planning was made in which several sub-projects are defined each with their own specific complexity and risk. Next, the datasets required for the sub-projects were defined and data collection was started. At this moment, the first Artificial Intelligence models have been implemented and training on the already collected data is started. Goal of these models is to automatically recognize and delineate head and neck tumors on the medical images. The first results obtained are very promising.

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