PortPy: Planning and Optimization for Radiation Therapy

PortPy (<strong>P</strong>lanning and <strong>O</strong>ptimization for <strong>R</strong>adiation <strong>T</strong>herapy) is a community effort to develop an open source python library to facilitate the development and clinical translation of radiotherapy cancer treatment planning algorithms. PortPy includes: 1. Research-ready data and code to promote <em>benchmarking</em>, <em>transparency</em>, <em>reproducibility </em>and <em>community</em>-<em>driven </em>development 2. Interface to an open-source optimization package CVXPy for easy/quick prototyping and out-of-the-box access to commercial/open-source optimization engines (e.g., Mosek, Gorubi, CPLEX, IPOPT) 3. Visualization modules to visualize relevant plan information (e.g, dose volume histograms, dose distribution, fluence map) 4. Evaluation modules to quantify plan quality with respect to established clinical metrics (e.g., RTOG metrics, dose conformality, tumor control probability, normal tissue control probability) Data needed for optimization and algorithm development (e.g., a set of beams/beamlets/voxels, dose contribution of each beamlet to each voxel) are provided for a set of pre-specified machine parameters (e.g., beam/colimator/couch angles). We initially provide these data for a set of publicly available dataset from TCIA. We hope to expand our dataset in the future. The data needed for optimization is extracted from the research version of Eclipse^TM treatment planning system (Varian Medical Systems) using its API.