BackgroundThe aim of this study was to establish and verify a predictive nomogram for patients with cutaneous verrucous carcinoma (CVC) who will eventually survive and to determine the accuracy of the nomogram relative to the conventional American Joint Committee on Cancer (AJCC) staging system.MethodsAssessments were performed on 1125 patients with CVC between 2004 and 2015, and the results of those examinations were recorded in the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided at a ratio of 7:3 into the training (n = 787) and validation (n = 338) cohorts. Predictors were identified using stepwise regression analysis in the COX regression model for create a nomogram to predict overall survival of CVC patients at 3-, 5-, and 8-years post-diagnosis. We compared the performance of our model with that of the AJCC prognosis model using several evaluation metrics, including C-index, NRI, IDI, AUC, calibration plots, and DCAs.ResultsMultivariate risk factors including sex, age at diagnosis, marital status, AJCC stage, radiation status, and surgery status were employed to determine the overall survival (OS) rate (P<0.05). The C-index nomogram performed better than the AJCC staging system variable for both the training (0.737 versus 0.582) and validation cohorts (0.735 versus 0.573), which AUC (> 0.7) revealed that the nomogram exhibited significant discriminative ability. The statistically significant NRI and IDI values at 3-, 5-, and 8-year predictions for overall survival (OS) in the validation cohort (55.72%, 63.71%, and 78.23%, respectively and 13.65%, 20.52%, and 23.73%, respectively) demonstrate that the established nomogram outperforms the AJCC staging system (P < 0.01) in predicting OS for patients with cutaneous verrucous carcinoma (CVC). The calibration plots indicate good performance of the nomogram, while decision curve analyses (DCAs) show that the predictive model could have a favorable clinical impact.ConclusionThis study constructed and validated a nomogram for predicting the prognosis of patients with CVC in the SEER database and assessed it using several variables. This nomogram model can assist clinical staff in making more-accurate predictions than the AJCC staging method about the 3-, 5-, and 8-year OS probabilities of patients with CVC.

Background: Applying a competing-risks analysis to data from the Surveillance, Epidemiology, and End Results (SEER) database, we aimed to identify significant prognostic factors and evaluate the cumulative incidence of cause-specific (CS) death for skin verrucous carcinoma (SVC). The Cox proportional-hazards model, extensively employed in assessing survival trends and identifying prognostic indicators, has the potential to generate erroneous predictions. However, in the realm of clinical practice, there is still a lack of specific prognostic factors for cutaneous verrucous carcinoma, leading to disproportionate treatment. The insights derived from this analysis can serve as valuable guidance for clinical interventions Methods: The SEER database provided relevant data of patients with SVC. The reliability, precision, and logicality of estimations for cumulative incidence function (CIF) related to CS mortality and death from other causes at each time point were enhanced through the utilization of competing-risks analysis. In the univariate analyses, Gray's test and the CIF were used, while the multivariate analysis employed the Cox proportional-hazards model, CS, and the Fine-Gray model. Results: The study involved 656 eligible patients with SVC, with 332 deaths recorded: 115 attributed to SVC and 217 resulting from other causes. Univariate analyses revealed that variables such as differentiation grade, marital status, metastasis, American Joint Committee on Cancer (AJCC) stage, age, surgery, the status of radiotherapy, and chemotherapy significantly influenced the cumulative incidence rates for events of interest (P<0.05). Marital status, AJCC stage, race, age, and surgery status, emerged as independent risk factors in the multivariate Cox regression. Based on the multivariate Fine-Gray and CS model analysis, age, AJCC stage, differentiation grade, and surgery independently served as key determinants affecting the risk of specific outcomes in SVC patients (P<0.05). Conclusions: The novel competing-risks model increased the accuracy of predictions by examining the cumulative incidence rate of cancer-specific mortality. Moreover, this approach is high useful in research for obtaining data such as the prognostic variables for SVC.

Background: The objective of this investigation was to ascertain precise prognostic determinants for cervical cancer through the utilization of a competing-risks model that relied on data procured from the Surveillance, Epidemiology, and End Results (SEER) database. Methods: This study abstracted data related to cervical cancer patients from 2000 to 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. The univariate analysis used a cumulative incidence function with the Gray test and a Fine-Gray specific cause (CS) and Cox proportional risk model. Results: Among the 11424 eligible cervical cancer patients, 2603 patients were found to have died of cervical cancer, while 1153 patients were found to have died from other causes. Meanwhile, a univariate Gray test established age, race, marital status, pathological type, primary site, degree of differentiation, American Joint Committee on Cancer (AJCC) staging, T stage, lymph node involvement, metastasis, tumor size, regional lymph nodes examined, regional lymph nodes positive, surgical status, regional lymphadenectomy, radiation status, and chemotherapy status all significantly influenced the amassed incidence of events of interest (P<0.05). Multifactorial competing risks analysis demonstrated that age, race, marital status, pathology type, Grade, AJCC stage, T stage, lymph node involvement, metastasis, surgery, regional lymphadenectomy, and chemotherapy status were independent risk factors affecting postoperative prognosis in patients with cervical cancer (P<0.05). Multifactorial Cox regression results differed: lymph node involvement was not an independent risk factor. Conclusions: It was found that prognostic factors for cervical cancer were identified more accurately using competing risk models than traditional methods.