Olya Stringfield

TL;DR: It is argued that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics, which has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.

TL;DR: It is demonstrated that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.

TL;DR: Computed tomography-based radiomic features of peripheral lung adenocarcinomas can capture useful information regarding tumor phenotype, and the model built can be useful to predict the presence of EGFR mutations in peripheral lung carcinomas in Asian patients when mutational profiling is not available or possible.

TL;DR: The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.

TL;DR: Imaging features derived from planning CT demonstrate prognostic value for recurrence following SBRT treatment, and might be helpful in patient stratification, and there was an improvement of Harrell's C‐index when adding imaging features to a clinical model.