This systematic review looked at the literature reporting the application of radiomics to imaging techniques in ovarian lesion patients. The authors found that radiomics showed promising results and great potential as a clinical diagnostic tool in patients with ovarian masses when it comes to improving lesion stratification, treatment selection, and outcome prediction. However, much larger and more diverse patient cohorts are required before real-world evaluation. Key points Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation. Article: Radiomics in the evaluation of ovarian masses — a systematic review Authors: Pratik Adusumilli, Nishant Ravikumar, Geoff Hall, Sarah Swift, Nicolas Orsi & Andrew Scarsbrook
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
