title: Is visual registration equivalent to semiautomated registration in prostate biopsy?
abstract: In magnetic resonance iimaging- (MRI-) ultrasound (US) guided biopsy, suspicious lesions are identified on MRI, registered on US, and targeted during biopsy. The registration can be performed either by a human operator (visual registration) or by fusion software. Previous studies showed that software registration is fairly accurate in locating suspicious lesions and helps to improve the cancer detection rate. Here, the performance of visual registration was examined for ability to locate suspicious lesions defined on MRI. This study consists of 45 patients. Two operators with differing levels of experience (<1 and 18 years) performed visual registration. The overall spatial difference by the two operators in 72 measurements was 10.6 ± 6.0 mm. Each operator showed a spatial difference of 9.4 ± 5.1 mm (experienced; 39 lesions) and 12.1 ± 6.6 mm (inexperienced; 33 lesions), respectively. In a head-to-head comparison of the same 16 lesions from 12 patients, the spatial differences were 9.7 mm ± 4.9 mm (experienced) and 13.4 mm ± 7.4 mm (inexperienced). There were significant differences between the two operators (unpaired, P value = 0.042; paired, P value = 0.044). The substantial differences by the two operators suggest that visual registration could improperly and inaccurately target many tumors, thereby potentially leading to missed diagnosis or false characterization on pathology.
authors.affiliation: Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors.forename: Jin Tae
authors.initials: JT
authors.lastname: Kwak
authors.affiliation: Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors.forename: Cheng William
authors.initials: CW
authors.lastname: Hong
authors.affiliation: National Cancer Institute, National Institutes of Health, Urologic Oncology Branch, Bethesda, MD 20892, USA.
authors.forename: Peter A
authors.initials: PA
authors.lastname: Pinto
authors.affiliation: Walter Reed National Military Medical Center, Bethesda, MD 20814, USA.
authors.forename: Molly
authors.initials: M
authors.lastname: Williams
authors.affiliation: Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors.forename: Sheng
authors.initials: S
authors.lastname: Xu
authors.affiliation: Philips Research North America, Briarcliff Manor, NY 10510, USA.
authors.forename: Jochen
authors.initials: J
authors.lastname: Kruecker
authors.affiliation: Philips Research North America, Briarcliff Manor, NY 10510, USA.
authors.forename: Pingkun
authors.initials: P
authors.lastname: Yan
authors.affiliation: Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Baris
authors.initials: B
authors.lastname: Turkbey
authors.affiliation: Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Peter L
authors.initials: PL
authors.lastname: Choyke
authors.affiliation: Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors.forename: Bradford J
authors.initials: BJ
authors.lastname: Wood
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D061705:Image-Guided Biopsy / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008853:Microscopy / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods
references.citation: J Magn Reson Imaging. 2012 Jun;35(6):1414-21
references.pmid: 22246980
references.citation: Radiology. 2010 Apr;255(1):89-99
references.pmid: 20308447
references.citation: J Urol. 2001 May;165(5):1554-9
references.pmid: 11342916
references.citation: J Urol. 2003 Jan;169(1):125-9
references.pmid: 12478119
references.citation: Eur Urol. 2004 Apr;45(4):444-8; discussion 448-9
references.pmid: 15041107
references.citation: J Urol. 1997 Jan;157(1):199-202; discussion 202-3
references.pmid: 8976250
references.citation: Radiology. 2005 Feb;234(2):576-81
references.pmid: 15616117
references.citation: Radiology. 2007 Jul;244(1):184-95
references.pmid: 17495178
references.citation: Comput Aided Surg. 2008 Sep;13(5):255-64
references.pmid: 18821344
references.citation: BJU Int. 2010 May;105(9):1231-6
references.pmid: 19817743
references.citation: Radiology. 2011 Feb;258(2):488-95
references.pmid: 21177390
references.citation: J Urol. 2011 Oct;186(4):1281-5
references.pmid: 21849184
references.citation: BJU Int. 2011 Oct;108(8 Pt 2):E171-8
references.pmid: 21426475
references.citation: Urology. 2011 Dec;78(6):1356-62
references.pmid: 21840577
references.citation: Cardiovasc Intervent Radiol. 2012 Oct;35(5):986-98
references.pmid: 22851166
references.citation: CA Cancer J Clin. 2013 Jan;63(1):11-30
references.pmid: 23335087
references.citation: Radiology. 2013 Aug;268(2):461-9
references.pmid: 23579051
references.citation: Eur Urol. 2014 Aug;66(2):343-51
references.pmid: 24262102
references.citation: J Urol. 2000 Jan;163(1):152-7
references.pmid: 10604335