Literature DB >> 9254897

On the prediction of the histologic composition of benign prostatic hyperplasia based on clinical and MRI parameters.

R E Weijers1, J V Zambon, A G Kessels, A P de Bruïne.   

Abstract

BACKGROUND: The histologic composition of prostate adenoma seems related to the development of clinical benign prostatic hyperplasia (BPH). Therefore, a new noninvasive prediction model as an alternative for biopsies was investigated.
METHODS: In 19 patients, the data of a routine preoperative workup for transurethral resection (TURP) and of an additional MRI-examination were related to the results of morphometry on TURP-tissue.
RESULTS: Statistical analysis identified age of the patient and MRI-volumetrics of the prostate adenoma as best predictors of the epithelial fraction, with a 95% confidence interval of at least 5% (range, 9-14%) (R2 = 50%).
CONCLUSIONS: This prediction model is sufficiently accurate to categorize a population of patients into histologic subgroups. It seems very likely that this method will be of use as an investigative tool in medical trials to provide insight into the pathogenesis of clinical BPH and into treatment strategies for the individual patient.

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Mesh:

Year:  1997        PMID: 9254897     DOI: 10.1002/(sici)1097-0045(19970801)32:3<179::aid-pros4>3.0.co;2-g

Source DB:  PubMed          Journal:  Prostate        ISSN: 0270-4137            Impact factor:   4.104


  3 in total

Review 1.  Targeting phenotypic heterogeneity in benign prostatic hyperplasia.

Authors:  Douglas W Strand; Daniel N Costa; Franto Francis; William A Ricke; Claus G Roehrborn
Journal:  Differentiation       Date:  2017-08-04       Impact factor: 3.880

2.  Use of MRI for Lobar Classification of Benign Prostatic Hyperplasia: Potential Phenotypic Biomarkers for Research on Treatment Strategies.

Authors:  Neil F Wasserman; Benjamin Spilseth; Jafar Golzarian; Gregory J Metzger
Journal:  AJR Am J Roentgenol       Date:  2015-09       Impact factor: 3.959

3.  MRI Features Associated with Histology of Benign Prostatic Hyperplasia Nodules: Generation of a Predictive Model.

Authors:  Jessica C Dai; Tara N Morgan; Ramy Goueli; Daniel Parrott; Alexander Kenigsberg; Ryan J Mauck; Claus G Roehrborn; Douglas W Strand; Daniel N Costa; Jeffrey C Gahan
Journal:  J Endourol       Date:  2022-02-28       Impact factor: 2.619

  3 in total

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