A novel model to predict cancer‐specific survival in patients with early‐stage uterine papillary serous carcinoma (UPSC). (17th December 2019)

• Record Type: Journal Article
• Authors: Chen, Lihua; Liu, Xiaona; Li, Mengjiao; Wang, Shuoer; Zhou, Hongyu; Liu, Lei; Cheng, Xi
• Is Part Of: Cancer medicine. Volume 9:Number 3(2020)
• Page Start: 988
• Page End: 998
• Publication Date: 2019-12-17
• Subjects: cancer‐specific death (CSD) -- competing risk model -- nomogram -- prediction model -- uterine papillary serous carcinoma (UPSC); 616.994005
• DOI: 10.1002/cam4.2648
• Languages: English
• ISSNs: 2045-7634

Abstract

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 < .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 : Our prediction model of CSD based on proportional subdistribution hazards regression showed good performance in predicting the cancer‐specific survival of early‐stage UPSC patients and contributes to selection of adjuvant therapy for stage I‐II UPSC patients in clinical treatment strategy.