AUTHOR=Xu Jiaqin , Huang Chen , Wu Zhenyu , Xu Huilin , Li Jiong , Chen Yuntao , Wang Ce , Zhu Jingjing , Qin Guoyou , Zheng Xueying , Yu Yongfu 

TITLE=Risk Prediction of Second Primary Malignancies in Primary Early-Stage Ovarian Cancer Survivors: A SEER-Based National Population-Based Cohort Study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.875489

DOI=10.3389/fonc.2022.875489

ISSN=2234-943X

ABSTRACT=Purpose: This study aimed to characterize the clinical features of early stage ovarian cancer (OC) survivors with second primary malignancies (SPMs) and provided a prediction tool for individualized risk of developing SPMs.
Methods: Data were obtained from the Surveillance, Epidemiology and End Results (SEER) database during 1998–2013. Considering non-SPMs death as a competing event, the Fine & Gray model and corresponding nomogram were used to identify the risk factors for SPMs and predict the SPMs probabilities after initial OC diagnosis. The decision curve analysis (DCA) was performed to evaluate the clinical utility of our proposed model.
Results: A total of 14,314 qualified patients were enrolled. The diagnosis rate of and the cumulative incidence of SPMs were 7.9% and 13.6% (95% confidence interval (CI) = 13.5 to 13.6%), respectively, at the follow-up of 20.9 years. The multivariable competing risk analysis suggested that older age at initial cancer diagnosis, race of white, epithelial histologic subtype of OC (serous, endometrioid, mucinous, and brenner tumor), number of lymph nodes examined (<12), and radiotherapy were significantly associated with an elevated SPMs risk. The DCA revealed that the net benefit obtained by our proposed model was higher than the scenarios of all-screening or no-screening within a wide range of risk thresholds (1% to 23%).
Conclusion: The competing risk nomogram can be potentially useful for assisting physicians in identifying patients with different risks of SPMs and scheduling risk-adapted clinical management. More comprehensive data on treatment regimens and patient characteristics may help improve the predictability of the risk model for SPMs.