This study aimed to develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with prostate cancer.
Clinical data of patients with mPCa between 2010 and 2014 were retrieved retrospectively, and randomized into training (2/3) and validation sets (1/3). Nomograms were built with potential risk factors based on COX regression analysis. Accuracy was validated using the discrimination and calibration curve for the training and validation groups, respectively.
6659 mPCa patients were collected and enrolled, including 4440 in the training set and 2219 in the validation set. Multivariate analysis showed that age, marital status, PSA, biopsy Gleason score, T stage, and bone metastasis were independent risk factors for both OS and CSS. The concordance index (C-index) of OS was 0.735 (95% CI 0.722-0.748) for the internal validation and 0.735 (95% CI 0.717-0.753) for the external validation. For CSS, it was 0.734 (95% CI 0.721-0.747) and 0.742 (95% CI 0.723-0.761), respectively. The nomograms for predicting OS and CSS displayed better discrimination power in both training and validation sets. Moreover, a favorable consistency between the predicted and actual survival probabilities was demonstrated using calibration curves.
The nomograms showed good performances for predicting OS and CSS in patients with prostate cancer. It might be a convenient individualized predictive tool for prognosis in clinical practice.
International urology and nephrology. 2019 Jul 09 [Epub ahead of print]
Wei-Dong Jiang, Ping-Cheng Yuan
Department of Urology and Hubei Key Laboratory of Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, No.141, Tianjin Road, Huangshi, Hubei, 435000, People’s Republic of China., Department of Urology and Hubei Key Laboratory of Kidney Disease Pathogenesis and Intervention, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, No.141, Tianjin Road, Huangshi, Hubei, 435000, People’s Republic of China. .