This study aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in patients with bladder cancer (BCa). Using a retrospective cohort of 229 BCa patients, the authors determined that the radiomics-clinical nomogram was able to effectively assess BCa recurrence risk, outperforming both the radiomics model and the clinical model. Key points: Radiomics plays a vital role in predicting bladder cancer recurrence. Precise prediction of tumor recurrence risk is crucial for clinical management. MRI-based radiomics models excel in predicting bladder cancer recurrence. Article: Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics Authors: Guoqiang Yang, Jingjing Bai, Min Hao, Lu Zhang, Zhichang Fan & Xiaochun Wang

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

