An Integrative Medicine Colorectal Screening (IMCS) Model for Predicting Colorectal Neoplasm: A Cross-Sectional Study of 2,047 Participants
5 affiliations
- 1Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
- 2College of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
- 3Shenzhen Longgang Second People’s Hospital, Shenzhen, People’s Republic of China
- 4The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
- 5Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
Abstract
Purpose: To develop and validate an integrative prediction model for colorectal neoplasm (CN) screening to enhance detection effectiveness and provide a noninvasive diagnostic method.
Methods: This cross-sectional study included 2,047 participants undergoing colonoscopy at Guangdong Provincial Hospital of Chinese Medicine between March 2022 and July 2023. Predictive models were constructed using epidemiological and Traditional Chinese Medicine factors via five machine learning algorithms. Performance was assessed using the area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, DeLong test, calibration curves, and decision curve analysis.
Results: Among 1,814 included participants (39.25% with CN), significant predictors included male, age, hypertension, drinking, tongue features, and pigmented nevus of the facial and perioral triangle (all P <0.05). Ten features were selected through univariate and multivariate logistic regression. The Integrative Medicine Colorectal Screening (IMCS) model using extreme gradient boosting (XGBoost) achieved the highest training-set AUC (0.817, 95% CI: 0.794–0.841), while the logistic regression (LR) model performed best on the validation set (AUC = 0.690, 95% CI: 0.644–0.736). The IMCS (LR) model outperformed existing models (APCS, M-APCS, CNP; Delong test, all P <0.05), demonstrated strong calibration (mean square error = 0.212, P = 0.425), and showed potential clinical utility (net benefit: 0.144–0.265).
Conclusion: The IMCS model improves predictive capability for CN risk and may assist in CN risk stratification. Further large-scale prospective studies are warranted to validate its clinical applicability.
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BibTeX
@article{liu2026integrative,
title = {{An Integrative Medicine Colorectal Screening (IMCS) Model for Predicting Colorectal Neoplasm: A Cross-Sectional Study of 2,047 Participants}},
author = {Chang Liu and Yuxing Shuai and Huizhen Ye and Chong Shan Ng and Fubin Hu and Longxiu Fan and Shaoyu Cheng and Beiping Zhang and Haiyan Zhang and Tianwen Liu and Xin-Lin Chen and Cailing Zhong},
journal = {International Journal of General Medicine},
year = {2026},
month = nov,
doi = {10.2147/ijgm.s640776},
url = {https://doi.org/10.2147/ijgm.s640776},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - An Integrative Medicine Colorectal Screening (IMCS) Model for Predicting Colorectal Neoplasm: A Cross-Sectional Study of 2,047 Participants
AU - Chang Liu
AU - Yuxing Shuai
AU - Huizhen Ye
AU - Chong Shan Ng
AU - Fubin Hu
AU - Longxiu Fan
AU - Shaoyu Cheng
AU - Beiping Zhang
AU - Haiyan Zhang
AU - Tianwen Liu
AU - Xin-Lin Chen
AU - Cailing Zhong
PY - 2026
DA - 2026/11/05
JO - International Journal of General Medicine
DO - 10.2147/ijgm.s640776
UR - https://doi.org/10.2147/ijgm.s640776
AB - Purpose: To develop and validate an integrative prediction model for colorectal neoplasm (CN) screening to enhance detection effectiveness and provide a noninvasive diagnostic method. Methods: This cross-sectional study included 2,047 participants undergoing colonoscopy at Guangdong Provincial Hospital of Chinese Medicine between March 2022 and July 2023. Predictive models were constructed using epidemiological and Traditional Chinese Medicine factors via five machine learning algorithms. Performance was assessed using the area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, DeLong test, calibration curves, and decision curve analysis. Results: Among 1,814 included participants (39.25% with CN), significant predictors included male, age, hypertension, drinking, tongue features, and pigmented nevus of the facial and perioral triangle (all P <0.05). Ten features were selected through univariate and multivariate logistic regression. The Integrative Medicine Colorectal Screening (IMCS) model using extreme gradient boosting (XGBoost) achieved the highest training-set AUC (0.817, 95% CI: 0.794–0.841), while the logistic regression (LR) model performed best on the validation set (AUC = 0.690, 95% CI: 0.644–0.736). The IMCS (LR) model outperformed existing models (APCS, M-APCS, CNP; Delong test, all P <0.05), demonstrated strong calibration (mean square error = 0.212, P = 0.425), and showed potential clinical utility (net benefit: 0.144–0.265). Conclusion: The IMCS model improves predictive capability for CN risk and may assist in CN risk stratification. Further large-scale prospective studies are warranted to validate its clinical applicability.
SN - 1178-7074
ER -