The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.

Purpose: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC). Methods: This retrospective study included 335 patients from dual centers....

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Publicado en:	Abdominal Radiology Vol. 50; no. 11; pp. 5090 - 5103
Autores principales:	Xu, Lihang, Li, Mingyu, Dong, Xianling, Wang, Zhongxiao, Tong, Ying, Feng, Tao, Xu, Shuangyan, Shang, Hui, Zhao, Bin, Lin, Jianpeng, Cao, Zhendong, Zheng, Yi
Formato:	Journal Article
Publicado:	Springer Nature Nov2025
Acceso en línea:	Ver este registro en EBSCOhost