Clinical Usefulness of Prostate and Tumor Volume Related Parameters following Radical Prostatectomy for Localized Prostate Cancer

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 volum...

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Veröffentlicht in:The Journal of urology Jg. 201; H. 3; S. 535
Hauptverfasser: Ito, Yujiro, Udo, Kazuma, Vertosick, Emily A, Sjoberg, Daniel D, Vickers, Andrew J, Al-Ahmadie, Hikmat A, Chen, Ying-Bei, Gopalan, Anuradha, Sirintrapun, S Joseph, Tickoo, Satish K, Scardino, Peter T, Eastham, James A, Reuter, Victor E, Fine, Samson W
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Sprache:Englisch
Veröffentlicht: United States 01.03.2019
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ISSN:1527-3792, 1527-3792
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Abstract 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. 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. 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. 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.
AbstractList 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.PURPOSEWe 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.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.MATERIALS AND METHODSA 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.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.RESULTSOn 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.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.CONCLUSIONSTumor 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.
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. 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. 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. 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.
Author Vertosick, Emily A
Eastham, James A
Reuter, Victor E
Udo, Kazuma
Vickers, Andrew J
Sirintrapun, S Joseph
Scardino, Peter T
Ito, Yujiro
Gopalan, Anuradha
Tickoo, Satish K
Sjoberg, Daniel D
Fine, Samson W
Al-Ahmadie, Hikmat A
Chen, Ying-Bei
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  organization: Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
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  organization: Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
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  givenname: Samson W
  surname: Fine
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  organization: Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
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Snippet We evaluated whether the prediction of biochemical recurrence after radical prostatectomy is enhanced by any of 6 parameters, including prostate volume, total...
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StartPage 535
SubjectTerms Aged
Humans
Male
Middle Aged
Neoplasm Recurrence, Local - blood
Neoplasm Recurrence, Local - diagnosis
Predictive Value of Tests
Prostate - pathology
Prostate-Specific Antigen - blood
Prostatectomy - methods
Prostatic Neoplasms - blood
Prostatic Neoplasms - pathology
Prostatic Neoplasms - surgery
Retrospective Studies
Tumor Burden
Title Clinical Usefulness of Prostate and Tumor Volume Related Parameters following Radical Prostatectomy for Localized Prostate Cancer
URI https://www.ncbi.nlm.nih.gov/pubmed/30300632
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