PURPOSE: We evaluated whether the prediction of biochemical recurrence after radical prostatectomy is enhanced by any of 6 parameters, including prostate volume, total tumor volume, high grade total tumor volume, the ratio of high grade total tumor volume to total tumor volume, the ratio of total tumor volume to prostate volume and/or the ratio of high grade total tumor volume to prostate volume. MATERIALS AND METHODS: A total of 1,261 patients who underwent radical prostatectomy during a 3-year period had tumor maps constructed with the Gleason pattern denoted as low-3 or high-4 or 5 and volumetric data generated using commercially available software. Univariate Cox regression models were used to assess whether each volume related parameter was associated with biochemical recurrence after radical prostatectomy. A multivariable Cox regression base model (age, prostate specific antigen, Gleason score/grade group, pathological stage and margin status) was compared with 6 additional models (base model plus each volume related parameter) to evaluate enhancement in predictive accuracy. Decision curve analysis was performed to determine the clinical utility of parameters that enhanced predictive accuracy. RESULTS: On univariate analysis each parameter was significantly associated with biochemical recurrence except prostate volume. Predictive accuracy of the multivariable base model was high (c-index = 0.861). Adding volume related parameters marginally enhanced discrimination. Decision curve analysis failed to show added benefit even for high grade total tumor volume/total tumor volume, which was the parameter with the highest discriminative improvement. CONCLUSIONS: Tumor volume related parameters are significantly associated with radical prostatectomy but do not add important discrimination to standard clinicopathological variables for radical prostatectomy prediction or provide benefit across a range of clinically relevant decision thresholds. Volume related measurement is not warranted in routine pathological evaluation and reporting.
PURPOSE: We evaluated whether the prediction of biochemical recurrence after radical prostatectomy is enhanced by any of 6 parameters, including prostate volume, total tumor volume, high grade total tumor volume, the ratio of high grade total tumor volume to total tumor volume, the ratio of total tumor volume to prostate volume and/or the ratio of high grade total tumor volume to prostate volume. MATERIALS AND METHODS: A total of 1,261 patients who underwent radical prostatectomy during a 3-year period had tumor maps constructed with the Gleason pattern denoted as low-3 or high-4 or 5 and volumetric data generated using commercially available software. Univariate Cox regression models were used to assess whether each volume related parameter was associated with biochemical recurrence after radical prostatectomy. A multivariable Cox regression base model (age, prostate specific antigen, Gleason score/grade group, pathological stage and margin status) was compared with 6 additional models (base model plus each volume related parameter) to evaluate enhancement in predictive accuracy. Decision curve analysis was performed to determine the clinical utility of parameters that enhanced predictive accuracy. RESULTS: On univariate analysis each parameter was significantly associated with biochemical recurrence except prostate volume. Predictive accuracy of the multivariable base model was high (c-index = 0.861). Adding volume related parameters marginally enhanced discrimination. Decision curve analysis failed to show added benefit even for high grade total tumor volume/total tumor volume, which was the parameter with the highest discriminative improvement. CONCLUSIONS:Tumor volume related parameters are significantly associated with radical prostatectomy but do not add important discrimination to standard clinicopathological variables for radical prostatectomy prediction or provide benefit across a range of clinically relevant decision thresholds. Volume related measurement is not warranted in routine pathological evaluation and reporting.
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Authors: Michael Tzeng; Emily Vertosick; Spyridon P Basourakos; James A Eastham; Behfar Ehdaie; Peter T Scardino; Andrew J Vickers; Jim C Hu Journal: Eur Urol Open Sci Date: 2021-06-15