The presence of prostate cancer on saturation biopsy can be accurately predicted.

Link:
Autor/in:
Erscheinungsjahr:
2010
Medientyp:
Text
Schlagworte:
  • Adult
  • Humans
  • Male
  • Aged
  • Middle Aged
  • Aged, 80 and over
  • Epidemiologic Methods
  • Biopsy, Needle
  • Nomograms
  • Prostate pathology
  • Prostatic Intraepithelial Neoplasia pathology
  • Prostatic Neoplasms pathology
  • Adult
  • Humans
  • Male
  • Aged
  • Middle Aged
  • Aged, 80 and over
  • Epidemiologic Methods
  • Biopsy, Needle
  • Nomograms
  • Prostate pathology
  • Prostatic Intraepithelial Neoplasia pathology
  • Prostatic Neoplasms pathology
Beschreibung:
  • OBJECTIVE: To improve the ability of our previously reported saturation biopsy nomogram quantifying the risk of prostate cancer, as the use of office-based saturation biopsy has increased. PATIENTS AND METHODS: Saturation biopsies of 540 men with one or more previously negative 6-12 core biopsies were used to develop a multivariable logistic regression model-based nomogram, predicting the probability of prostate cancer. Candidate predictors were used in their original or stratified format, and consisted of age, total prostate-specific antigen (PSA) level, percentage free PSA (%fPSA), gland volume, findings on a digital rectal examination, cumulative number of previous biopsy sessions, presence of high-grade prostatic intraepithelial neoplasia on any previous biopsy, and presence of atypical small acinar proliferation (ASAP) on any previous biopsy. Two hundred bootstraps re-samples were used to adjust for overfit bias. RESULTS: Prostate cancer was diagnosed in 39.4% of saturation biopsies. Age, total PSA, %fPSA, gland volume, number of previous biopsies, and presence of ASAP at any previous biopsy were independent predictors for prostate cancer (all P <0.05). The nomogram was 77.2% accurate and had a virtually perfect correlation between predicted and observed rates of prostate cancer. CONCLUSIONS: We improved the accuracy of the saturation biopsy nomogram from 72% to 77%; it relies on three previously included variables, i.e. age, %fPSA and prostate volume, and on three previously excluded variables, i.e. PSA, the number of previous biopsy sessions, and evidence of ASAP on previous biopsy. Our study represents the largest series of saturation biopsies to date.
  • OBJECTIVE: To improve the ability of our previously reported saturation biopsy nomogram quantifying the risk of prostate cancer, as the use of office-based saturation biopsy has increased. PATIENTS AND METHODS: Saturation biopsies of 540 men with one or more previously negative 6-12 core biopsies were used to develop a multivariable logistic regression model-based nomogram, predicting the probability of prostate cancer. Candidate predictors were used in their original or stratified format, and consisted of age, total prostate-specific antigen (PSA) level, percentage free PSA (%fPSA), gland volume, findings on a digital rectal examination, cumulative number of previous biopsy sessions, presence of high-grade prostatic intraepithelial neoplasia on any previous biopsy, and presence of atypical small acinar proliferation (ASAP) on any previous biopsy. Two hundred bootstraps re-samples were used to adjust for overfit bias. RESULTS: Prostate cancer was diagnosed in 39.4% of saturation biopsies. Age, total PSA, %fPSA, gland volume, number of previous biopsies, and presence of ASAP at any previous biopsy were independent predictors for prostate cancer (all P <0.05). The nomogram was 77.2% accurate and had a virtually perfect correlation between predicted and observed rates of prostate cancer. CONCLUSIONS: We improved the accuracy of the saturation biopsy nomogram from 72% to 77%; it relies on three previously included variables, i.e. age, %fPSA and prostate volume, and on three previously excluded variables, i.e. PSA, the number of previous biopsy sessions, and evidence of ASAP on previous biopsy. Our study represents the largest series of saturation biopsies to date.
Lizenz:
  • info:eu-repo/semantics/restrictedAccess
Quellsystem:
Forschungsinformationssystem des UKE

Interne Metadaten
Quelldatensatz
oai:pure.atira.dk:publications/2667e7b4-64b6-47e5-be57-dd4ac467da9d