Abstract
Skin cancer is a common malignant tumor in human beings. At present, the construction of clinical prediction models mainly focuses on malignant melanoma and no researchers have constructed clinical prediction models for all kind of skin cancer to predict the prognosis of skin cancer. We used patient data collected from the surveillance, epidemiology, and end results program database to construct and validate our model for clinical prediction of skin cancer, hoping to provide a reference for clinical treatment of skin cancer.R software was used for univariate and multivariate Cox regression analysis of variables to screen out factors that have an impact on the survival of skin cancer patients. Then the prognostic model of skin cancer patients was constructed and the nomogram was drawn. Concordance Index (C-index), receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the clinical prediction model.A total of 3180 skin cancer patients were included in this study. We constructed nomogram, a 3-year and 5-year clinical prediction model for skin cancer patients. We used C-index to evaluate the accuracy of nomogram model, and the result of C-index was 0.728, 95%CI (0.703–0.753). The nomogram model was evaluated by ROC curve. The area under the curve values of the ROC curve for 3-year survival rate and 5-year survival rate were 0.732 and 0.768 respectively. The model calibration diagram of the modeling group also shows that the model exhibits high accuracy.The nomogram model of postoperative survival of patients with skin cancer, based on the surveillance, epidemiology, and end results program database of patients with skin cancer, has shown good stability and accuracy in multi-method validation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.