CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.

Journal: Journal of X-ray science and technology

Published Date: Jan 1, 2024

Abstract

BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration.

Authors

Tianyu Lu

Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China.
Jianbing Ma

Department of Knee Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Jiajun Zou

Guangzhou Xinhua University, Guangzhou, China.
Chenxu Jiang

Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China.
Yangyang Li

Institute of Urology, The Third Affiliated Hospital of Shenzhen University, Shenzhen, 518000, P. R. China.
Jun Han

School of Basic Medical Sciences, Yunnan Traditional Chinese Medical College, Kunming 650500, China. Electronic address: hanzjn@126.com.

Keywords

Adult Aged Carcinoma, Non-Small-Cell Lung Deep Learning Female Humans Lung Neoplasms Lymph Nodes Lymphatic Metastasis Male Middle Aged Retrospective Studies Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (38578874)

CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.

Abstract

Authors

Keywords

External Resources

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