Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.

Journal: Abdominal radiology (New York)

Published Date: May 26, 2024

Abstract

PURPOSE: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.

Authors

Jingjing Zhang

Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
Qiang Zhang

Yunan Provincial Center for Disease Control and Prevention, Kunming 650022, China.
Bo Zhao

State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Gaofeng Shi

Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China. gaofengs1962@hebmu.edu.cn.

Keywords

Adult Aged Chemotherapy, Adjuvant Contrast Media Deep Learning Female Humans Male Middle Aged Neoadjuvant Therapy Nomograms Predictive Value of Tests Retrospective Studies Stomach Neoplasms Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (38796795)

Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.

Abstract

Authors

Keywords

External Resources

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