Tim Lustberg

TL;DR: Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research as mentioned in this paper.

TL;DR: User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards.

TL;DR: Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers, and one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against.

TL;DR: An overview of the factors that are associated with outcome in radiation oncology are delivered and the methodology behind the development of accurate prediction models, which is a multi‐faceted process are discussed.

TL;DR: A prototype for an online platform for proton decision support (PRODECIS) comparing photon and proton treatments on dose metric, toxicity and cost-effectiveness levels was developed and an evaluation was performed with head and neck cancer datasets.