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| A nomogram model for distinguishing factors of synchronous multiple colorectal carcinoma from single
colorectal carcinoma |
| CHENG Jinkun, LI Qi, CUI Wei. |
| Department of Colorectal Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo 315041, China |
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| Cite this article: |
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CHENG Jinkun,LI Qi,CUI Wei.. A nomogram model for distinguishing factors of synchronous multiple colorectal carcinoma from single
colorectal carcinoma[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2023, 53(12): 980-984.
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Abstract Objective: To explore the differentiation factors related to the synchronous multiple colorectal carcinoma (SMCRC) and single colorectal carcinoma(CRC), and to construct a nomogram prediction model. Methods: The clinical data of 42 patients with SMCRC and 120 patients with single CRC who underwent surgery in Ningbo Medical Center Lihuili Hospital from January 2016 to December 2021 were retrospectively collected.Univariate analysis and multivariate Logistic regression were used to analyze the differentiation factors related to the SMCRC and single CRC, based on which a nomogram prediction model was constructed. The predictive efficiency of the model was analyzed by ROC curve and calibration curve. Results: Univariate analysis showed that sex, adenomatous polyp, hypertension, serum carcinoembryonic antigen level, albumin content, and hemoglobin content were statistically difference between the two groups (P<0.05). Multivariate Logistic regression analysis showed that male (OR=3.108, 95%CI=1.113-8.682), adenomatous polyp (OR=2.941, 95%CI=1.036-8.346), hypertension (OR=3.202, 95%CI=1.128-9.088), and hemoglobin≤120 g/L (OR=3.868, 95%CI=1.095-13.671) were independent predictors for distinguishing the occurrence of SMCRC from single CRC (P<0.05).The area under the ROC curve was 0.743 (95%CI=0.630-0.856). The sensitivity was 75% and the specificity was 68%. The calibration curve showed that the three curves fit well, indicating that the model had good consistency and accuracy. Conclusion: In this study, the identification factors related to SMCRC and single CRC has been analyzed and a nomogram prediction model constructed to accurately assess the risk of SMCRC, which provides a basis for early clinical identification of SMCRC.
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Received: 05 February 2023
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