Deep Learning and Radiomics for Gastric Cancer Lymph Node Metastasis: Automated Segmentation and Multi-Machine Learning Study from Two Centers

Radiomics and deep learning features derived from automated spleen segmentation to construct a nomogram demonstrate efficacy in predicting lymph node metastasis in GC. Concurrently, fully automated segmentation provides a novel and reproducible approach for radiomics research.

Authors

Yi Zheng (ORCID: https://orcid.org/0000-0003-2671-0820)
Yuyang Zhao (ORCID: https://orcid.org/0000-0002-4754-0325)
Zhendong Cao (ORCID: https://orcid.org/0009-0008-9844-9960)
Hui Shang (ORCID: https://orcid.org/0000-0003-3260-213X)
Nan Mi
Yue Fang (ORCID: https://orcid.org/0000-0002-5863-6103)

Institutions

Chengde Medical University (CN)

Publication Details

Journal: Oncology
Published: 2025-02-13
DOI: https://doi.org/10.1159/000544179
Citations: 1
Primary Topic: Gastric Cancer Management and Outcomes
Type: article
Field-Weighted Citation Impact: 1.52

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article

Deep Learning and Radiomics for Gastric Cancer Lymph Node Metastasis: Automated Segmentation and Multi-Machine Learning Study from Two Centers

Yi Zheng, Yuyang Zhao, Zhendong Cao, Hui Shang et al.

Feb 13, 2025

1 citations

Oncology

Gastric Cancer Management and Outcomes

1.52

article

Deep Learning and Radiomics for Gastric Cancer Lymph Node Metastasis: Automated Segmentation and Multi-Machine Learning Study from Two Centers

Yi Zheng, Yuyang Zhao, Zhendong Cao, Hui Shang, Nan Mi, Yue Fang

February 13, 2025article en

1 citations

Abstract

Radiomics and deep learning features derived from automated spleen segmentation to construct a nomogram demonstrate efficacy in predicting lymph node metastasis in GC. Concurrently, fully automated segmentation provides a novel and reproducible approach for radiomics research.

OncologyVol. 104(1)

Chengde Medical University (CN)

Reduced inequalities

Openalex Percentile: Top 22%

Gastric Cancer Management and Outcomes

1.52

DOI Source OpenAlex

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