Machine learning and augmented data for automated treatment planning in complex external beam radiation therapy

Sammanfattning: External beam radiation therapy is currently one of the most commonly used modalities for treating cancer. With the rise of new technologies and increasing computational power, machine learning, deep learning and artificial intelligence applications used for classification and regression problems have begun to find their way into the field of radiation oncology. One such application is the automated generation of radiotherapy treatment plans, which must be optimized for every single patient. The department of radiation physics in Lund, Sweden, has developed an autoplanning software, which in combination with a commercially available treatment planning system (TPS), can be used for automatic creation of clinical treatment plans. The parameters of a multivariable cost function are changed iteratively, making it possible to generate a great amount of different treatment plans for a single patient. The output leads to optimal, near-optimal, clinically acceptable or even non-acceptable treatment plans. In this thesis, the possibility of using machine and deep learning to minimize the amount of treatment plans generated by the autoplanning software as well as the possibility of finding cost function parameters that lead to clinically acceptable optimal or near-optimal plans is evaluated. Data augmentation is used to create matrices of optimal treatment plan parameters, which are stored in a training database.  Patient specific training features are extracted from the TPS, as well as from the bottleneck layer of a trained deep neural network autoencoder. The training features are then matched against the same features extracted for test patients, using a k-nearest neighbor algorithm. Finally, treatment plans for a new patient are generated using the output plan parameter matrices of its nearest neighbors. This allows for a reduction in computation time as well as for finding suitable cost function parameters for a new patient.