Development and validation of a nomogram for predicting osteoporosis in prostate cancer patients: A cross-sectional study from China.

BACKGROUND

The specific risk factors contributing to the development of osteoporosis and the appropriate timing of treatment in Chinese prostate cancer (PCa) patients remain unclear. Our objective was to develop and validate a nomogram capable of predicting the occurrence of osteoporosis in PCa patients.

METHODS

We conducted a cross-sectional study with PCa patients attending the Second Hospital of Tianjin Medical University, collecting data from June 2021 to February 2023. The patients were divided into training and validation sets in a 7:3 ratio. The LASSO regression was used to identify the most relevant predictive variables, and the multivariable logistic regression was used to construct the nomogram. The nomogram's performance was validated through receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA) in both the training and validation sets.

RESULTS

We collected data from a total of 596 patients and then constructed the nomogram using age, body mass index, hemoglobin, vitamin D3, testosterone, and androgen deprivation therapy duration. The C-index of the nomogram was 0.923 in the training set and 0.859 in the validation set. The nomogram showed good consistency in both sets. DCA demonstrated the clinical benefit of the nomogram across various prediction thresholds. Furthermore, a separate nomogram was constructed to predict bone loss in patients undergoing ADT, exhibiting equally favorable diagnostic performance and clinical benefit.

CONCLUSION

This study constructed two reliable nomograms to predict osteoporosis and bone loss, integrating personal health information and PCa-specific treatment data. These nomograms offer an easy and individualized approach to predict the occurrence of osteoporosis and bone loss in PCa patients.