René Monshouwer

TL;DR: The data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer, which may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

TL;DR: Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach using a qualitative approach to solve the mystery of why tumourigenicity is associated with certain types of cancer.

TL;DR: In this article, temperature-dependent fluorescence quantum yield and lifetime measurements on the LH-1 and LH-2 complexes of Rhodobacter sphaeroides and on the isolated B820 subunit of Rhodospirillum rubrum were presented.

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: The results show that a subset of radiomic features extracted from CT and CBCT images are interchangeable using simple linear regression, and a previously developed radiomics signature has prognostic value for overall survival in three CBCT cohorts, showing the potential of CBCT radiomics to be used as prognostic imaging biomarker.