Histological Grading of Prostatic Carcinoma in Prostatectomy Specimens: Comparison of Prognostic Accuracy of Five Grading Systems
- 1 April 1990
- journal article
- research article
- Published by Wiley in British Journal of Urology
- Vol. 65 (4), 368-375
- https://doi.org/10.1111/j.1464-410x.1990.tb14758.x
Abstract
The prognostic accuracy of 5 histological grading systems (Broders, Anderson, Mostofi, Gleason and Mostofi-Schroeder) was compared. Grading was performed on 50 prostatectomy specimens by 5 pathologists. The results were averaged so as to reduce the impact of inter-observer variation. The Cox proportional hazards model was used to estimate the relationship between average grading scores and both time-to-recurrence and time-to-death by prostatic carcinoma. Age at surgery was considered to be a possible confounding factor and adjusted accordingly. The prognostic impact of the 5 grading systems (related to both recurrence and death caused by prostatic carcinoma) was judged by the likelihood ratio (LR) test score (chi 2 distributed with 1 df); for time-to-recurrence for the Mostofi-Schroeder score the LR was 6.54 and for the Gleason system it was 1.79. A stepwise procedure demonstrated that the best prognostic performance was reached with the Mostofi-Schroeder and Broders systems used together (with Mostofi-Schroeder weighted 1.5 times larger than Broders). For time-to-recurrence the median grading result was also used, giving results similar to the mean grading result. For time-to-death from prostatic carcinoma the LR test scores for all grading systems were relatively low. In this analysis the outcome of the Gleason system showed a minimum of prognostic ability, whereas the Broders and Mostofi-Schroeder systems had a reasonable predictive ability. Since the inter-observer variation of the Mostofi-Schroeder system was large, the Broders system is preferable. The restrictions and implications of this study are discussed and a brief review of the prognostic importance of grading of prostatic carcinoma is presented.Keywords
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