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Current status and challenges of deep learning in predicting lymph node status in colorectal cancer
ZHAO Wanting  ZHANG Guangwen  GAO Hui  ZHANG Jinsong 

Cite this article as: ZHAO W T, ZHANG G W, GAO H, et al. Current status and challenges of deep learning in predicting lymph node status in colorectal cancer[J]. Chin J Magn Reson Imaging, 2025, 16(1): 222-227. DOI:10.12015/issn.1674-8034.2025.01.036.


[Abstract] Colorectal cancer (CRC) is one of the most prevalent malignant neoplasms within the gastrointestinal tract. Clarifying the status of lymph node involvement in CRC is essential for formulating personalized treatment strategies and evaluating prognosis. Compared to visual assessments by specialists and radiomics techniques, the neural network based deep learning (DL) approaches with the characteristics of high automaticity, adaptability, and scalability has demonstrated promising potential for evaluating lymph node status in CRC. Therefore, this article will provide a comprehensive review on the application of DL methods in predicting the lymph node status in CRC patients with CT, MR, and digital pathology images, and explore future research directions in this field, with the objective of providing novel methodologies and references for enhancing the accuracy of lymph node status prediction in CRC patients.
[Keywords] colorectal cancer；lymph node metastasis；magnetic resonance imaging；deep learning；prediction

ZHAO Wanting1, 2   ZHANG Guangwen2   GAO Hui1   ZHANG Jinsong2*  

1 Medical School of Yan'an University, Yan'an 716000, China

2 Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an 710032, China

Corresponding author: ZHANG J S, E-mail: stspine@163.com

Conflicts of interest   None.

Received  2024-12-31
Accepted  2025-01-18
DOI: 10.12015/issn.1674-8034.2025.01.036
Cite this article as: ZHAO W T, ZHANG G W, GAO H, et al. Current status and challenges of deep learning in predicting lymph node status in colorectal cancer[J]. Chin J Magn Reson Imaging, 2025, 16(1): 222-227. DOI:10.12015/issn.1674-8034.2025.01.036.

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