Nuclear morphometry predicts disease-free interval for clinically localized adenocarcinoma of the prostate treated with definitive radiation therapy

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Abstract

Men treated for prostate cancer often have unexpected outcomes despite predictive models based on stage, grade and prostate-specific antigen (PSA). Previous results have indicated that nuclear morphometry can predict patient outcome in urologic malignancies. Application of this analytical method in prostate cancer treated with radiation therapy is limited. We have evaluated the predictive ability of nuclear morphometry in such patients. Histologic sections from 23 men with clinically localized adenocarcinoma of the prostate treated with radiation therapy were studied. Nuclear morphometric parameters were assessed using a previously described and validated system. Univariate and multivariate logistic regression analyses and a Cox proportional hazards model were used to assess the ability of nuclear morphometric parameters to predict recurrence and disease-free interval. Ten patients had no recurrence with median follow-up of 47.5 months, while 13 had recurrence. Gleason grade was not predictive of treatment outcome. Pre-treatment PSA data, available for only 11 patients, were predictive of treatment outcome. Several nuclear morphometric parameters predicted recurrence, including upper quartile of suboptimal circle fit and upper quartile of feret-diameter ratio. A prognostic factor score incorporating these 2 parameters was derived, which predicted disease-free interval (p = 0.0014).

Original languageEnglish (US)
Pages (from-to)594-597
Number of pages4
JournalInternational Journal of Cancer
Volume84
Issue number6
DOIs
StatePublished - 1999

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Prostate
Adenocarcinoma
Radiotherapy
Recurrence
Prostate-Specific Antigen
Prostatic Neoplasms
Proportional Hazards Models
Logistic Models
Regression Analysis
Neoplasms
Therapeutics

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

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title = "Nuclear morphometry predicts disease-free interval for clinically localized adenocarcinoma of the prostate treated with definitive radiation therapy",
abstract = "Men treated for prostate cancer often have unexpected outcomes despite predictive models based on stage, grade and prostate-specific antigen (PSA). Previous results have indicated that nuclear morphometry can predict patient outcome in urologic malignancies. Application of this analytical method in prostate cancer treated with radiation therapy is limited. We have evaluated the predictive ability of nuclear morphometry in such patients. Histologic sections from 23 men with clinically localized adenocarcinoma of the prostate treated with radiation therapy were studied. Nuclear morphometric parameters were assessed using a previously described and validated system. Univariate and multivariate logistic regression analyses and a Cox proportional hazards model were used to assess the ability of nuclear morphometric parameters to predict recurrence and disease-free interval. Ten patients had no recurrence with median follow-up of 47.5 months, while 13 had recurrence. Gleason grade was not predictive of treatment outcome. Pre-treatment PSA data, available for only 11 patients, were predictive of treatment outcome. Several nuclear morphometric parameters predicted recurrence, including upper quartile of suboptimal circle fit and upper quartile of feret-diameter ratio. A prognostic factor score incorporating these 2 parameters was derived, which predicted disease-free interval (p = 0.0014).",
author = "Hurwitz, {Mark D.} and Theodore DeWeese and Zinreich, {Eva S.} and Epstein, {Jonathan Ira} and Partin, {Alan Wayne}",
year = "1999",
doi = "10.1002/(SICI)1097-0215(19991222)84:6<594::AID-IJC9>3.0.CO;2-D",
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AU - Hurwitz, Mark D.

AU - DeWeese, Theodore

AU - Zinreich, Eva S.

AU - Epstein, Jonathan Ira

AU - Partin, Alan Wayne

PY - 1999

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N2 - Men treated for prostate cancer often have unexpected outcomes despite predictive models based on stage, grade and prostate-specific antigen (PSA). Previous results have indicated that nuclear morphometry can predict patient outcome in urologic malignancies. Application of this analytical method in prostate cancer treated with radiation therapy is limited. We have evaluated the predictive ability of nuclear morphometry in such patients. Histologic sections from 23 men with clinically localized adenocarcinoma of the prostate treated with radiation therapy were studied. Nuclear morphometric parameters were assessed using a previously described and validated system. Univariate and multivariate logistic regression analyses and a Cox proportional hazards model were used to assess the ability of nuclear morphometric parameters to predict recurrence and disease-free interval. Ten patients had no recurrence with median follow-up of 47.5 months, while 13 had recurrence. Gleason grade was not predictive of treatment outcome. Pre-treatment PSA data, available for only 11 patients, were predictive of treatment outcome. Several nuclear morphometric parameters predicted recurrence, including upper quartile of suboptimal circle fit and upper quartile of feret-diameter ratio. A prognostic factor score incorporating these 2 parameters was derived, which predicted disease-free interval (p = 0.0014).

AB - Men treated for prostate cancer often have unexpected outcomes despite predictive models based on stage, grade and prostate-specific antigen (PSA). Previous results have indicated that nuclear morphometry can predict patient outcome in urologic malignancies. Application of this analytical method in prostate cancer treated with radiation therapy is limited. We have evaluated the predictive ability of nuclear morphometry in such patients. Histologic sections from 23 men with clinically localized adenocarcinoma of the prostate treated with radiation therapy were studied. Nuclear morphometric parameters were assessed using a previously described and validated system. Univariate and multivariate logistic regression analyses and a Cox proportional hazards model were used to assess the ability of nuclear morphometric parameters to predict recurrence and disease-free interval. Ten patients had no recurrence with median follow-up of 47.5 months, while 13 had recurrence. Gleason grade was not predictive of treatment outcome. Pre-treatment PSA data, available for only 11 patients, were predictive of treatment outcome. Several nuclear morphometric parameters predicted recurrence, including upper quartile of suboptimal circle fit and upper quartile of feret-diameter ratio. A prognostic factor score incorporating these 2 parameters was derived, which predicted disease-free interval (p = 0.0014).

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