Lei YP, Song QZ, Liu S, Xie JY, Lv GQ. Predicting lymph node metastasis in colorectal cancer: An analysis of influencing factors to develop a risk model. World J Gastrointest Surg 2023; 15(10): 2234-2246 [PMID: 37969707 DOI: 10.4240/wjgs.v15.i10.2234]
Corresponding Author of This Article
Guo-Qing Lv, MD, MS, Attending Doctor, Department of Gastrointestinal Surgery, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Peking University Shenzhen Hospital, No. 120 Lianhua Road, Futian District, Shenzhen 518036, Guangdong Province, China. 365973269@qq.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Table 2 Representative patches of tumor tissue for each of the top 10 clusters
Cluster
Description
1
Poorly differentiated tumor cells with a high nuclear-cytoplasmic ratio, irregular glandular formation, and sparse stroma
2
Well-differentiated tumor cells with low nuclear-cytoplasmic ratio, regular glandular formation, and abundant stroma
3
Tumor cells with moderate differentiation, moderate nuclear-cytoplasmic ratio, and moderate stroma
4
Tumor cells with signet-ring cell differentiation, high nuclear-cytoplasmic ratio, and mucin production
5
Tumor cells with neuroendocrine differentiation, high nuclear-cytoplasmic ratio, and rosette-like structures
6
Tumor cells with serrated adenocarcinoma differentiation, low nuclear-cytoplasmic ratio, and serrated glandular formation
7
Tumor cells with mucinous differentiation, low nuclear-cytoplasmic ratio, and abundant extracellular mucin
8
Tumor cells with medullary carcinoma differentiation, high nuclear-cytoplasmic ratio, and solid growth pattern
9
Tumor cells with micropapillary carcinoma differentiation, high nuclear-cytoplasmic ratio, and papillary projections
10
Tumor cells with mixed adenoneuroendocrine carcinoma differentiation, high nuclear-cytoplasmic ratio, and dual expression of neuroendocrine and epithelial markers
Table 3 Coefficients and odds ratios of the predictors in the final model
Table 4 Performance of the risk prediction model and the existing models in the validation set
Model
NRI
IDI
Brier score
Our model
0.28
0.11
0.10
Kikuchi’s model
-0.04
-0.03
0.17
Ueno’s model
-0.01
-0.01
0.15
Krogue’s model
0.12
0.05
0.12
Table 5 Distribution of patients and lymph node metastasis in each risk group in the validation set (%)
Risk group
Predicted probability of LNM
Number of patients
Number of LNMs
Low risk
< 10
27 (38.6)
1 (5.6)
Intermediate risk
10-30
26 (37.1)
6 (33.3)
High risk
> 30
17 (24.3)
11 (61.1)
Citation: Lei YP, Song QZ, Liu S, Xie JY, Lv GQ. Predicting lymph node metastasis in colorectal cancer: An analysis of influencing factors to develop a risk model. World J Gastrointest Surg 2023; 15(10): 2234-2246