Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1257
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dc.contributor.authorLo, Kevin-
dc.contributor.authorCorcoran, Niall-
dc.contributor.authorKerger, Michael-
dc.contributor.authorKurganovs, Natalie-
dc.contributor.authorPeters, Justin-
dc.contributor.authorMoon, Daniel-
dc.contributor.authorCostello, Anthony-
dc.contributor.authorHong, Matthew-
dc.contributor.otherHovens, Marcus-
dc.contributor.otherPedersen, John-
dc.contributor.otherNottle, Timothy-
dc.contributor.otherRyan, Andrew-
dc.date.accessioned2017-11-15T00:18:46Z-
dc.date.available2017-11-15T00:18:46Z-
dc.date.issued2017-09-
dc.identifier.citationPathol Res Pract. 2017 Sep 27. pii: S0344-0338(17)30817-8.en_US
dc.identifier.issn0344-0338en_US
dc.identifier.urihttp://hdl.handle.net/11434/1257-
dc.description.abstractProstate cancer displays a wide spectrum of clinical behaviour from biological indolence to rapidly lethal disease, but we remain unable to accurately predict an individual tumor's future clinical course at an early curable stage. Beyond basic dimensions and volume calculations, tumor morphometry is an area that has received little attention, as it requires the analysis of the prostate gland and tumor foci in three-dimensions. Previous efforts to generate three-dimensional prostate models have required specialised graphics units and focused on the spatial distribution of tumors for optimisation of biopsy strategies rather than to generate novel morphometric variables such as tumor surface area. Here, we aimed to develop a method of creating three-dimensional models of a prostate's pathological state post radical prostatectomy that allowed the derivation of surface areas and volumes of both prostate and tumors, to assess the method's accuracy to known clinical data, and to perform initial investigation into the utility of morphometric variables in prostate cancer prognostication. Serial histology slides from 21 prostatectomy specimens covering a range of tumor sizes and pathologies were digitised. Computer generated three-dimensional models of tumor and prostate space filling models were reconstructed from these scanned images using Rhinoceros 4.0 spatial reconstruction software. Analysis of three-dimensional modelled prostate volume correlated only moderately with weak concordance to that from the clinical data (r=0.552, θ=0.405), but tumor volume correlated well with strong concordance (r=0.949, θ=0.876). We divided the cohort of 21 patients into those with features of aggressive tumor versus those without and found that larger tumor surface area (32.7 vs 3.4cc, p=0.008) and a lower tumor surface area to volume ratio (4.7 vs 15.4, p=0.008) were associated with aggressive tumor biology.en_US
dc.publisherElsevieren_US
dc.subjectProstate Canceren_US
dc.subjectProstate Neoplasmsen_US
dc.subjectHistopathologyen_US
dc.subjectRadical Prostatectomyen_US
dc.subjectTumor Spatial Reconstructionen_US
dc.subject3 Dimensional Modellingen_US
dc.subjectTumor Morphometryen_US
dc.subjectRhinoceros 4.0 Spatial Reconstruction Softwareen_US
dc.subjectEpworth Prostate Centre, Epworth HealthCare, Victoria, Australiaen_US
dc.subjectCancer Services Clinical Institute, Epworth HealthCare, Victoria, Australiaen_US
dc.title3D modelling of radical prostatectomy specimens: Developing a method to quantify tumor morphometry for prostate cancer risk prediction.en_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1016/j.prp.2017.09.022en_US
dc.identifier.journaltitlePathology, Research and Practiceen_US
dc.description.pubmedurihttps://www.ncbi.nlm.nih.gov/pubmed/29033190en_US
dc.description.affiliatesSchool of Medicine, University of Queensland, St Lucia, QLD, Australia.en_US
dc.description.affiliatesFaculty of Medicine, Monash University, Clayton, VIC, Australia.en_US
dc.description.affiliatesDivision of Urology, Department of Surgery, Royal Melbourne Hospital, University of Melbourne, Parkville.en_US
dc.type.studyortrialCohort Studyen_US
dc.type.contenttypeTexten_US
Appears in Collections:Cancer Services
Epworth Prostate Centre

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