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Combination of clinical characteristics and transrectal ultrasound-guided biopsy to predict lobes without significant cancer: application in patient selection for hemiablative focal therapy.

Authors
  • Jung, Jin-Woo1
  • Lee, Byung Ki1
  • Choi, Won Suk2
  • Park, Yong Hyun3
  • Lee, Sangchul3
  • Jeong, Seong Jin3
  • Lee, Sang Eun3
  • Byun, Seok-Soo3
  • 1 Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea. , (North Korea)
  • 2 Choi Won Suk Urology Clinic, Yongin, Korea. , (North Korea)
  • 3 Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea ; Department of Urology, Seoul National University College of Medicine, Seoul, Korea. , (North Korea)
Type
Published Article
Journal
Prostate international
Publication Date
Mar 01, 2014
Volume
2
Issue
1
Pages
37–42
Identifiers
DOI: 10.12954/PI.13031
PMID: 24693533
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A major limitation of performing hemiablative focal therapy (FT) for prostate cancer (PCa) is the possibility of accompanying significant cancer in the contralateral side of the prostate that is missed on prostate biopsy. We attempted to verify whether clinical and biopsy-related parameters can be used to predict the absence of significant cancer in the prostate lobe. We assumed that hemiablative FT could be performed in patients with low-risk PCa, with unilateral tumors as assessed by transrectal ultrasound-guided biopsy. We evaluated 214 patients who had undergone radical prostatectomy (RP) and fulfilled the eligibility criteria. Seemingly preserved lobes, defined by the absence of cancer on biopsy, were classified as lobes with no cancer (LNC), lobes with insignificant cancer (LIC), and lobes with significant cancer (LSC) according to RP pathology. Cases with an estimated tumor volume of <0.5 mL, a Gleason score of <7, and organ confinement without Gleason pattern 4 were classified as LIC. Univariate and multivariate logistic regression analyses were performed to identify predictors for LSC. Predictive accuracies of the multivariate models were assessed using receiver operating characteristic curve-derived areas under the curve. Of 214 evaluated lobes, 45 (21.0%), 62, (29.0%), and 107 (50.0%) were classified as LNC, LIC, and LSC, respectively. Among the clinical and biopsy-related parameters, prostate-specific antigen density and prostate volume were identified as significant predictors for LSC in univariate regression analysis. However, multivariate analysis did not identify an independent predictor. Predictive accuracies of the multivariate models did not exceed 70.4%. Conventional parameters have limited value in predicting LSC in patients who are candidates for hemiablative FT.

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