This study shows that the combination of CT imaging and clinical factors pre-neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG) could help stratify potential responsiveness to NAC, which can also result in helping to provide a basis for clinicians to develop more customized treatment plans for patients. Key points Radiomics method can predict that AEG patients can achieve pCR after NAC. The combination of radiomics and clinical factors can improve the predicting performance. Radiomics–clinical model can stratify patients according to potential responsiveness to NAC. Radiomics–clinical model can help clinicians to develop individualized and precise treatment plans. Article: Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study Authors: Wenpeng Huang, Liming Li, Siyun Liu, Yunjin Chen, Chenchen Liu, Yijing Han, Fang Wang, Pengchao Zhan, Huiping Zhao, Jing 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
28. April 2025
