Background: Utilizing the traditional Cox regression model to identify the factors affecting the risk of mortality due to microinvasive cutaneous squamous cell carcinoma (micSCC) may produce skewed results. Since cause-specific mortality can guide clinical decision-making, this study employed the Fine-Gray model based on the Surveillance, Epidemiology, and End Results (SEER) database to identify significant predictive variables for the risk of micSCC-related mortality. Methods: This study used the information of patients with micSCC who were listed in the SEER database during 2000–2015. Cox regression and Fine-Gray models were utilized for the multivariable analysis, and Gray’s test and the cumulative incidence function were used for the univariable analyses. Results: There were 100 patients who died from other reasons and 38 who died from micSCC among the 1259 qualified patients with micSCC. Most were female, white, married, had localized metastasis, etc. According to the univariable Gray’s test (P < 0.05), the cumulative incidence rate for events of interest was strongly associated with age, sex, marital status, American Joint Committee on Cancer staging, radiation status, summary stage, chemotherapy status, surgery status, and tumor size. Multivariable Cox regression analysis and multivariable competing-risks analysis indicated that age, tumor size, and income were independent risk variables for the prognosis of patients with micSCC. In both age and tumor size variables, the competing-risks model showed a slight decrease in the hazard ratio and a slight narrowing of the 95% confidence interval compared with the Cox regression model. However, this pattern is not evident in the income variable. Conclusions: This study established a Fine-Gray model for identifying the independent risk factors that influence the risk of mortality among patients with micSCC. This study uncovers that, in the context of competing risks, age, tumor size, and income serve as independent risk factors influencing the risk of mortality due to micSCC among patients. Our findings have the potential to provide more accurate risk assessments for patient outcomes and contribute to the development of individualized treatment plans.

Background. The objective of this study is to determine the prognostic factors of keratinizing squamous cell carcinoma of the tongue (KTSCC) and to establish a prognostic nomogram of KTSCC to assist clinical diagnosis and treatment. Methods. This study identified 3874 patients with KTSCC from the Surveillance, Epidemiology, and End Results (SEER) database, and these patients were randomly divided into the training (70, (n=2711) and validation (30, n=1163) cohorts. Cox regression was then used to filter variables. Nomograms were then constructed based on meaningful variables. Finally, the concordance index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration charts, and decision-curve analysis (DCA), were used to evaluate the discrimination, accuracy and effectiveness of the model. Results. A nomogram model was established for predicting the 3-, 5-, and 8-year overall survival (OS) probabilities of patients with KTSCC. The model indicated that age, radiotherapy sequence, SEER stage, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, radiotherapy status, race, lymph node dissection status, and sex were factors influencing the OS of patients with KTSCC. Verified by C-index, NRI, IDI, calibration curve, and DCA curve, our model has better discrimination, calibration, accuracy and net benefit compared to the AJCC system. Conclusions. This study identified the factors that affect the survival of KTSCC patients and established a prognostic nomogram that can help clinicians predict the 3-, 5-, and 8-year survival rates of KTSCC patients.

Objective: The aim of this study was to compare the competing risk model with the Cox model to evaluate prognostic markers in females with keratinized squamous cell carcinoma of the reproductive system and to develop predictive models. Methods: Patients with keratinizing squamous cell carcinoma of the female reproductive system were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Using the cumulative incidence function (CIF) and Gray's test for univariate analysis, the competing risk and Cox models were used for multivariate analysis. A nomogram was developed based on the results of the competing risk model, and the C-index, net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the model's discrimination ability. The clinical validity of the model was assessed using calibration plots and decision curve analysis (DCA). Results: In this investigation, competing risk model analysis revealed that age, marital status, tumor size, AJCC stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, surgery and radiotherapy, and income were significant factors affecting the prognosis of patients with keratinizing squamous cell carcinoma of the female reproductive system. Based on these results, a nomogram for predicting the 3-year, 5-year, and 8-year survival rates was established. The nomogram demonstrated better clinical utility than the AJCC staging system. Conclusion: For the first time, the competing risk model was used in this study to assess the prognostic risk factors of keratinizing squamous cell carcinoma of the female reproductive system. The results may help clinicians make better clinical judgments. Additionally, we developed a nomogram to predict the likelihood of cancer-specific death (CSD) in patients at 3, 5, and 8 years. Physicians may use our nomogram to more accurately forecast the likelihood of CSD compared to the AJCC staging system.