title: Geometric systematic prostate biopsy.
abstract: OBJECTIVE
  The common sextant prostate biopsy schema lacks a three-dimensional (3D) geometric definition. The study objective was to determine the influence of the geometric distribution of the cores on the detection probability of prostate cancer (PCa).
  
  
  METHODS
  The detection probability of significant (>0.5 cm3) and insignificant (<0.2 cm3) tumors was quantified based on a novel 3D capsule model of the biopsy sample. The geometric distribution of the cores was optimized to maximize the probability of detecting significant cancer for various prostate sizes (20-100cm3), number of biopsy cores (6-40 cores) and biopsy core lengths (14-40 mm) for transrectal and transperineal biopsies.
  
  
  RESULTS
  The detection of significant cancer can be improved by geometric optimization. With the current sextant biopsy, up to 20% of tumors may be missed at biopsy in a 20 cm3prostate due to the schema. Higher number and longer biopsy cores are required to sample with an equal detection probability in larger prostates. Higher number of cores increases both significant and insignificant tumor detection probability, but predominantly increases the detection of insignificant tumors.
  
  
  CONCLUSIONS
  The study demonstrates mathematically that the geometric biopsy schema plays an important clinical role, and that increasing the number of biopsy cores is not necessarily helpful.
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Doyoung
authors.initials: D
authors.lastname: Chang
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Xue
authors.initials: X
authors.lastname: Chong
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Chunwoo
authors.initials: C
authors.lastname: Kim
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Changhan
authors.initials: C
authors.lastname: Jun
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Doru
authors.initials: D
authors.lastname: Petrisor
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Misop
authors.initials: M
authors.lastname: Han
authors.affiliation: a Robotics Laboratory, Urology Department, School of Medicine , Johns Hopkins University , Baltimore , MD , USA.
authors.forename: Dan
authors.initials: D
authors.lastname: Stoianovici
mesh_terms: D001707:Biopsy, Needle / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D011336:Probability; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology
keywords: Prostate cancer; biopsy optimization; insignificant tumor; probability of detection; significant; systematic biopsy