Risk of second primary malignancies in prostate cancer patients: A Surveillance, Epidemiology, and End Results population-based study.
[OBJECTIVE] Second primary malignancy (SPM) is becoming a challenge in clinical practice.
APA
Shou J, Wo Q (2026). Risk of second primary malignancies in prostate cancer patients: A Surveillance, Epidemiology, and End Results population-based study.. Asian journal of urology, 13(1), 45-51. https://doi.org/10.1016/j.ajur.2024.10.007
MLA
Shou J, et al.. "Risk of second primary malignancies in prostate cancer patients: A Surveillance, Epidemiology, and End Results population-based study.." Asian journal of urology, vol. 13, no. 1, 2026, pp. 45-51.
PMID
41815370
Abstract
[OBJECTIVE] Second primary malignancy (SPM) is becoming a challenge in clinical practice. However, limited studies have focused on this issue in patients with prostate cancer (PCa). We sought to explore the potential risk of SPMs in patients with a prior diagnosis of PCa and construct a model to predict the risk of developing SPMs.
[METHODS] We retrospectively extracted data on PCa patients from the Surveillance, Epidemiology, and End Results database between 2000 and 2018. A competing-risk model was established to explore the risk factors for developing SPMs and to predict the probability of developing SPMs for PCa patients after the initial diagnosis. The calibration curve, concordance index, and decision curve analysis were used to assess the validity of the model.
[RESULTS] A total of 284 738 eligible PCa patients were included, 14 845 (5.2%) of whom developed SPMs after the initial diagnosis. The results showed that age at the initial diagnosis, race, histological grade, pathological tumor stage, radiotherapy, and surgery were independent risk factors for developing SPMs. The concordance index of the model was 0.685 (95% confidence interval 0.684-0.686), and the calibration plots showed an excellent agreement between the nomogram prediction and the actual observation. Furthermore, the decision curve analysis indicated a positive benefit with the threshold risk range of 2%-12%.
[CONCLUSION] The nomogram is sufficiently accurate to predict the risk of SPMs for PCa survivors and to help surgeons identify patients who are at a high risk of developing SPMs and contribute to further management of SPMs.
[METHODS] We retrospectively extracted data on PCa patients from the Surveillance, Epidemiology, and End Results database between 2000 and 2018. A competing-risk model was established to explore the risk factors for developing SPMs and to predict the probability of developing SPMs for PCa patients after the initial diagnosis. The calibration curve, concordance index, and decision curve analysis were used to assess the validity of the model.
[RESULTS] A total of 284 738 eligible PCa patients were included, 14 845 (5.2%) of whom developed SPMs after the initial diagnosis. The results showed that age at the initial diagnosis, race, histological grade, pathological tumor stage, radiotherapy, and surgery were independent risk factors for developing SPMs. The concordance index of the model was 0.685 (95% confidence interval 0.684-0.686), and the calibration plots showed an excellent agreement between the nomogram prediction and the actual observation. Furthermore, the decision curve analysis indicated a positive benefit with the threshold risk range of 2%-12%.
[CONCLUSION] The nomogram is sufficiently accurate to predict the risk of SPMs for PCa survivors and to help surgeons identify patients who are at a high risk of developing SPMs and contribute to further management of SPMs.