Construction and Validation of a Noninvasive Screening Strategy for Colorectal Cancer Based on an Integrated Model of Inflammation, Metabolism, and Anemia
- 1 Department of Medical Laboratory, Guigang People’s Hospital, Guigang, China
- 2 Department of Endocrinology, Guigang People’s Hospital, Guigang, China
- 3 Department of Clinical Laboratory, Laibin People’s Hospital, Laibin, China
- 4 Department of Medical Laboratory, Guigang People’s Hospital, Guigang, China
- 5 Department of Medical Laboratory, Guigang People’s Hospital, Guigang, China
- 6 Department of Oncology, Guigang People’s Hospital, Guigang, China
Abstract
Objective : To construct and validate a noninvasive screening strategy for colorectal cancer based on an integrated model of inflammation, metabolism, and anemia. Methods : The clinical data of 671 patients with colorectal cancer (colorectal cancer group) and 420 healthy physical examination subjects (healthy control group) in Guigang People’s Hospital from 2020 to 2024 were retrospectively analyzed. Data of tumor markers (CEA, CA19-9), blood routine, inflammatory indexes (AISI, SIRI, PLR), liver and kidney functions, etc. of the two groups were collected. A prediction model was constructed through multivariate logistic regression analysis, and the efficacy of the model was evaluated by using the receiver operating characteristic (ROC) curve and calibration curve. Results : There were significant differences between the colorectal cancer group and the healthy control group in gender, age, CEA, CA19-9, blood routine indexes (WBC, NEUT#, LYMPH#, MONO#, RBC, HGB, PLT), inflammatory indexes (AISI, SIRI, PLR, HGB standardized value), and liver and kidney function indexes (ALT, ALP, TP, ALB, GLB, A/G, CRE, UA) (P < 0.05). The integrated model constructed by screening CEA, CA19-9, AISI, PLR, and HGB standardized value as independent predictive factors through multivariate logistic regression analysis had an AUC of 0.971 (95% CI: 0.956 - 0.986), a sensitivity of 92.4%, and a specificity of 94.1% in the training set; and an AUC of 0.948 (95% CI: 0.928 - 0.968), a sensitivity of 89.7%, and a specificity of 91.3% in the validation set. The calibration curve showed that the predicted probability of the model was highly consistent with the actual observed probability (Hosmer-Lemeshow test, P = 0.213). Conclusion : The noninvasive screening strategy based on the integrated model of inflammation, metabolism, and anemia has a high diagnostic value for colorectal cancer and can be used as a preliminary screening tool before colonoscopy.
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