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Abstract Objective Stage I‐II uterine papillary serous carcinoma (UPSC) has aggressive biological behavior and leads to poor prognosis. However, clinicopathologic risk factors to predict cancer‐specific survival of patients with stage I‐II UPSC were still unclear. This study was undertaken to develop a prediction model of survival in patients with early‐stage UPSC. Methods Using Surveillance, Epidemiology, and End Results (SEER) database, 964 patients were identified with International Federation of Gynecology and Obstetrics (FIGO) stage I‐II UPSC who underwent at least hysterectomy between 2004 and 2015. By considering competing risk events for survival outcomes, we used proportional subdistribution hazards regression to compare cancer‐specific death (CSD) for all patients. Based on the results of univariate and multivariate analysis, the variables were selected to construct a predictive model; and the prediction results of the model were visualized using a nomogram to predict the cancer‐specific survival and the response to adjuvant chemotherapy and radiotherapy of stage I‐II UPSC patients. Results The median age of the cohort was 67 years. One hundred and sixty five patients (17.1%) died of UPSC (CSD), while 8.6% of the patients died from other causes (non‐CSD). On multivariate analysis, age ≥ 67 (HR = 1.45, P = .021), tumor size ≥ 2 cm (HR = 1.81, P = .014) and >10 regional nodes removed (HR = 0.52, P = .002) were significantly associated with cumulative incidence of CSD. In the age ≥67 cohort, FIGO stage IB‐II was a risk factor for CSD (HR = 1.83, P = .036), and >10 lymph nodes removed was a protective factor (HR = 0.50, P = .01). Both adjuvant chemotherapy combined with radiotherapy and adjuvant chemotherapy alone decreased CSD of patients with stage I‐II UPSC older than 67 years (HR = 0.47, P = .022; HR = 0.52, P = .024, respectively). The prediction model had great risk stratification ability as the high‐risk group had higher cumulative incidence of CSD than the low‐risk group (P

Objective: Stage I‐II uterine papillary serous carcinoma (UPSC) has aggressive biological behavior and leads to poor prognosis. However, clinicopathologic risk factors to predict cancer‐specific survival of patients with stage I‐II UPSC were still unclear. This study was undertaken to develop a prediction model of survival in patients with early‐stage UPSC. Methods: Using Surveillance, Epidemiology, and End Results (SEER) database, 964 patients were identified with International Federation of Gynecology and Obstetrics (FIGO) stage I‐II UPSC who underwent at least hysterectomy between 2004 and 2015. By considering competing risk events for survival outcomes, we used proportional subdistribution hazards regression to compare cancer‐specific death (CSD) for all patients. Based on the results of univariate and multivariate analysis, the variables were selected to construct a predictive model; and the prediction results of the model were visualized using a nomogram to predict the cancer‐specific survival and the response to adjuvant chemotherapy and radiotherapy of stage I‐II UPSC patients. Results: The median age of the cohort was 67 years. One hundred and sixty five patients (17.1%) died of UPSC (CSD), while 8.6% of the patients died from other causes (non‐CSD). On multivariate analysis, age ≥ 67 (HR = 1.45, P =.021), tumor size ≥ 2 cm (HR = 1.81, P =.014) and >10 regional nodes removed (HR = 0.52, P =.002) were significantly associated with cumulative incidence of CSD. In the age ≥67 cohort, FIGO stage IB‐II was a risk factor for CSD (HR = 1.83, P =.036), and >10 lymph nodes removed was a protective factor (HR = 0.50, P =.01). Both adjuvant chemotherapy combined with radiotherapy and adjuvant chemotherapy alone decreased CSD of patients with stage I‐II UPSC older than 67 years (HR = 0.47, P =.022; HR = 0.52, P =.024, respectively). The prediction model had great risk stratification ability as the high‐risk group had higher cumulative incidence of CSD than the low‐risk group (P <.001). In the high‐risk group, patients with post‐operative adjuvant chemoradiotherapy had improved CSD compared with patients who did not receive radiotherapy nor chemotherapy (P =.037). However, there was no such benefit in the low‐risk group. Conclusion: Our prediction model of CSD based on proportional subdistribution hazards regression showed a good performance in predicting the cancer‐specific survival of early‐stage UPSC patients and contributed to guide clinical treatment decision, helping oncologists and patients with early‐stage UPSC to decide whether to choose adjuvant therapy or not. [ABSTRACT FROM AUTHOR]

Background: Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application., Methods: Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram., Results: We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree., Conclusions: This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-6045/coif). The authors have no conflicts of interest to declare., (2022 Annals of Translational Medicine. All rights reserved.)