3D modelling of radical prostatectomy specimens: Developing a method to quantify tumor morphometry for prostate cancer risk prediction.

Prostate 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.

Microscopic assessment of fresh prostate tumour specimens yields significantly increased rates of correctly annotated samples for downstream analysis.

AIMS: To assess if performing frozen sections of tissue biopsies from fresh radical prostatectomy specimens, prior to tissue banking, could improve the identification of the banked samples compared to standard fresh tumour banking procedures. METHODS: Tissue biopsies banked from 332 fresh prostatectomy specimens were assessed for accuracy of diagnosis, comparing two separate methods of tumour identification: one in which tumour was identified in the gross specimen by visual inspection (n = 155) and one in which rapid frozen sectioning was applied (n = 177). The associations with correct tumour annotation and clinicopathological variables, including age, pre-operative prostate specific antigen (PSA) levels, pathological Gleason score, pathological T stage, tumour volume and surgical margins, were examined using univariable and multivariable binary logistic regression models. RESULTS: For the gross visual inspection cohort the rate of correctly identifying and banking specimens containing prostate cancer was 69%. For the cohort assessed with rapid frozen sections, 94% of banked specimens actually had cancer. On multivariable analysis, we found that only frozen sectioning and tumour volume variables were independent predictors of correctly banked tumour specimens whilst all other routinely reported pathological variables had no influence on the success rates of fresh prostate tumour banking. CONCLUSION: The success rate for correctly banking fresh prostate tumour specimens is directly related to the tumour volume. Frozen section scrutiny of prostate samples is recommended to prevent misclassification of the banked material.