Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

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

Written by

European Radiology