The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting lymph node (LN) metastases in lung cancer patients compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. The authors determined that radiomics showed good discrimination power, regardless of the modelling technique, in detecting LN metastases in lung cancer patients. Key points Radiomics applied to contrast-enhanced computed tomography is feasible in detecting lymph node metastases in patients with proven lung cancer. The least absolute shrinkage and selection operator (LASSO) classifier is suitable as a diagnostic tool applied to radiomics in this setting. Radiomics failed to improve clinical benefit as a prescreening tool. Article: Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning Authors: Boris Gorodetski, Philipp Hendrik Becker, Alexander Daniel Jacques Baur, Alexander Hartenstein, Julian Manuel Michael Rogasch, Christian Furth, Holger Amthauer, Bernd Hamm, Marcus Makowski & Tobias Penzkofer
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
