A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients.
- Author:
Jin-You WANG
1
,
2
;
Yao ZHU
;
Chao-Fu WANG
;
Shi-Lin ZHANG
;
Bo DAI
;
Ding-Wei YE
Author Information
- Publication Type:Journal Article
- MeSH: Aged; Biopsy; Cohort Studies; Humans; Logistic Models; Male; Neoplasm Grading; Neoplasm Staging; Nomograms; Prostate-Specific Antigen; Prostatectomy; Prostatic Neoplasms
- From:Chinese Journal of Cancer 2014;33(5):241-248
- CountryChina
- Language:English
- Abstract: Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate-specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinically diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and were subsequently treated with radical prostatectomy. Of all included patients, 220 (81.8%) were referred with clinical symptoms. The prostate-specific antigen level, primary and secondary biopsy Gleason scores, and clinical T category were used in a multivariate logistic regression model to predict the probability of Gleason sum upgrading. The developed nomogram was validated internally. Gleason sum upgrading was observed in 90 (33.5%) patients. Our nomogram showed a bootstrap-corrected concordance index of 0.789 and good calibration using 4 readily available variables. The nomogram also demonstrated satisfactory statistical performance for predicting significant upgrading. External validation of the nomogram published by Chun et al. in our cohort showed a marked discordance between the observed and predicted probabilities of Gleason sum upgrading. In summary, a new nomogram to predict Gleason sum upgrading in clinically diagnosed prostate cancer was developed, and it demonstrated good statistical performance upon internal validation.
