Evaluation of Screening Model for Advanced Colorectal Adenoma and Traditional Chinese Medicine Tongue Image Analysis Based on Real World Data
10.13288/j.11-2166/r.2023.21.008
- VernacularTitle:基于真实世界数据的结直肠进展期腺瘤的筛查模型评估与舌象分析
- Author:
Peidi HUANG
1
;
Zishao ZHONG
2
;
Shujun LIU
2
;
Zhenhao YE
2
;
Zhuolin LI
1
;
Sufen WEI
2
;
Haiyan ZHANG
2
;
Beiping ZHANG
1
Author Information
1. The Second Clinical Medical School of Guangzhou University of Chinese Medicine， Guangzhou 510405
2. The Second Affiliated Hospital of Guangzhou University of Chinese Medicine （Guangdong Provincial Hospital of Chinese Medicine）
- Publication Type:Journal Article
- Keywords:
advanced colorectal adenoma;
Asia-Pacific colorectal screening scoring model;
colorectal neoplasia predict model;
real world data;
tongue image
- From:
Journal of Traditional Chinese Medicine
2023;64(21):2197-2207
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo evaluate the effectiveness and consistency of three commonly used early colorectal cancer screening models for advanced colorectal adenoma as a noninvasive means， and to assess the predictive value of traditional Chinese medicine （TCM） tongue images in the models. MethodsPatients diagnosed with colorectal adenoma who underwent colonoscopy and pathological examination were selected as the study participants. Basic clinical data and tongue image were collected. The prediction models of Asia-Pacific colorectal screening （APCS） model， its revision （M-APCS） and colorectal neoplasia predict （CNP） model were applied to compare the predictive effects of the three models on advanced stage adenomas of the colon， the differences in clinical data and traditional Chinese medicine tongue characteristics among patients with different degrees of adenomas， and the similarities and differences in tongue characteristics among the models. The discriminative ability of the three risk models was evaluated using the area under the curve （AUC） and receiver operating characteristic （ROC） curves. The calibration was assessed using the Kuder-Richardson coefficient and the Hosmer-Lemeshow test for consistency analysis. ResultsA total of 227 patients with adenoma were analyzed， including 104 patients （45.82%） with advanced adenoma. In the detection of advanced adenoma， those with greasy coating （70 cases， 67.3%） were higher than those without greasy coating （34 cases， 32.7%， P＜0.05）. After multivariate analysis， the odds ratio （OR） value of non-greasy coating was 0.371 （0.204~0.673， P＜0.01）， indicating that non-greasy coating was a protective factor for advanced adenomas. Among the three risk models， the detection rate of advanced adenoma in the high-risk group with APCS was the highest （63.3%）， which was 1.49 times and 2.04 times that of the medium-risk group （42.6%） and the low-risk group （31.1%， P＜0.01）. The detection rate of advanced adenomas in high-risk groups of M-APCS and CNP was slightly higher than that in moderate or low risk groups （P＞0.05）. The proportion of yellow and greasy coating in high-risk group was higher than that in the medium-risk or low-risk group （P＜0.05）. For the ability to distinguish advanced and non-advanced adenomas， the AUC of APCS was 0.629 （95% CI： 0.556~0.702） and was higher than that of M-APCS （0.591） and CNP （0.586）. In calibration evaluation， Cronbach's alpha was 0.919 （＞0.7）， which indicated that the three models were consistent. In the correlation matrix， the correlation coefficients between APCS model and M-APCS model， and CNP model were 0.794 and 0.717， respectively， and the correlation coefficients between M-APCS model and CNP model were 0.873， Hosmer-Lemeshow χ2 =2.552， P＞0.05， which suggested that the three models had good calibration ability. ConclusionAll three models demonstrate the efficiency to identify advanced colorectal adenoma， and their calibration ability is considered to be good. Among the three models， the APCS exhibits the highest recognition efficiency， however， the recognition accuracy of the APCS model needs improvement. The presence of a greasy coating is identified as one of the potential predictors of advanced adenoma. Consequently， it can be considered for inclusion in the risk model of advanced colorectal adenoma to enhance the accuracy.
