Risk factors analysis and prediction model construction of submucosal deep infiltration of early colorectal tumor.
10.3760/cma.j.cn112152-20211201-00886
- VernacularTitle:早期结直肠肿瘤黏膜下深浸润的危险因素分析和预测模型构建
- Author:
Zhi Hao CHEN
1
;
Li Zhou DOU
1
;
Yue Ming ZHANG
1
;
Yong LIU
1
;
Shun HE
1
;
Yan KE
1
;
Xu Dong LIU
1
;
Yu Meng LIU
1
;
Hai Rui WU
1
;
Shuang Mei ZOU
2
;
Gui Qi WANG
1
Author Information
1. Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
2. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
- Publication Type:Journal Article
- Keywords:
Colorectal neoplasms;
Infiltration depth;
JNET;
Pit pattern;
White light endoscope
- MeSH:
Humans;
Retrospective Studies;
Colorectal Neoplasms/pathology*;
Nomograms;
Neoplasm Staging;
Risk Factors
- From:
Chinese Journal of Oncology
2023;45(7):613-620
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the risk factors for the development of deep infiltration in early colorectal tumors (ECT) and to construct a prediction model to predict the development of deep infiltration in patients with ECT. Methods: The clinicopathological data of ECT patients who underwent endoscopic treatment or surgical treatment at the Cancer Hospital, Chinese Academy of Medical Sciences from August 2010 to December 2020 were retrospectively analyzed. The independent risk factors were analyzed by multifactorial regression analysis, and the prediction models were constructed and validated by nomogram. Results: Among the 717 ECT patients, 590 patients were divided in the within superficial infiltration 1 (SM1) group (infiltration depth within SM1) and 127 patients in the exceeding SM1 group (infiltration depth more than SM1). There were no statistically significant differences in gender, age, and lesion location between the two groups (P>0.05). The statistically significant differences were observed in tumor morphological staging, preoperative endoscopic assessment performance, vascular tumor emboli and nerve infiltration, and degree of tumor differentiation (P<0.05). Multivariate regression analysis showed that only erosion or rupture (OR=4.028, 95% CI: 1.468, 11.050, P=0.007), localized depression (OR=3.105, 95% CI: 1.584, 6.088, P=0.001), infiltrative JNET staging (OR=5.622, 95% CI: 3.029, 10.434, P<0.001), and infiltrative Pit pattern (OR=2.722, 95% CI: 1.347, 5.702, P=0.006) were independent risk factors for the development of deep submucosal infiltration in ECT. Nomogram was constructed with the included independent risk factors, and the nomogram was well distinguished and calibrated in predicting the occurrence of deep submucosal infiltration in ECT, with a C-index and area under the curve of 0.920 (95% CI: 0.811, 0.929). Conclusion: The nomogram prediction model constructed based on only erosion or rupture, local depression, infiltrative JNET typing, and infiltrative Pit pattern has a good predictive efficacy in the occurrence of deep submucosal infiltration in ECT.
