In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting lymph node metastasis in gastric cancer. The dual-energy CT–based radiomics nomogram outweighed the single-energy model and the clinical model. The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies. Article: Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer Authors: Jing Li, Di Dong, Mengjie Fang, Rui Wang, Jie Tian, Hailiang Li & Jianbo Gao

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

