ArticleHeliyon2023
Development and validation of a nomogram predictive model for colorectal adenoma with low-grade intraepithelial neoplasia using routine laboratory tests: A single-center case-control study in China.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data.Frontiers in oncology · 2025Article
- Multicenter prospective cohort study on colorectal adenoma recurrence and malignant transformation risk based on integrative medicine: a study protocol.Frontiers in oncology · 2025Article
- Development and validation of a nomogram model for predicting the risk of hypertension in Bangladesh.Heliyon · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) is the third most common cancer in the world and has a high mortality rate. Colorectal adenoma (CRA) is precancerous lesions of CRC. The purpose of the present study was to construct a nomogram predictive model for CRA with low-grade intraepithelial neoplasia (LGIN) in order to identify high-risk individuals, facilitating early diagnosis and treatment, and ultimately reducing the incidence of CRC. Methods: We conducted a single-center case-control study. Based on the results of colonoscopy and pathology, 320 participants were divided into the CRA group and the control group, the demographic and laboratory test data were collected. A development cohort (n = 223) was used for identifying the risk factors for CRA with LGIN and to develop a predictive model, followed by an internal validation. An independent validation cohort (n = 97) was used for external validation. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate discrimination ability, accuracy and clinical practicability of the model. Results: Four predictors, namely sex, age, albumin and monocyte count, were included in the predictive model. In the development cohort, internal validation and external validation cohort, the area under the curve (AUC) of this risk predictive model were 0.946 (95%CI: 0.919-0.973), 0.909 (95 % CI: 0.869-0.940) and 0.928 (95%CI: 0.876-0.980), respectively, which demonstrated the model had a good discrimination ability. The calibration plots showed a good agreement and the decision curve analysis (DCA) suggested the predictive model had a high clinical net benefit. Conclusion: The nomogram model exhibited good performance in predicting CRA with LGIN, which can aid in the early detection of high-risk patients, improve early treatment, and ultimately reduce the incidence of CRC.
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