Partial Sampling of Radical Prostatectomy Specimens: Detection of Positive Margins and Extraprostatic Extension.
ABSTRACT Currently there is no global agreement as to how extensively a radical prostatectomy specimen should be sectioned and histologically examined. We analyzed the ability of different methods of partial sampling in detecting positive margin (PM) and extraprostatic extension (EPE)-2 pathologic features of prostate cancer that are most easily missed by partial sampling of the prostate. Radical prostatectomy specimens from 617 patients treated with open radical prostatectomy between 1992 and 2011 were analyzed. Examination of the entirely submitted prostate detected only PM in 370 (60%), only EPE in 100 (16%), and both in 147 (24%) specimens. We determined whether these pathologic features would have been diagnosed had the examination of the specimen been limited only to alternate sections (method 1), alternate sections representing the posterior aspect of the gland in addition to one of the mid-anterior aspects (method 2), and every section representing the posterior aspect of the gland in addition to one of the mid-anterior aspects, supplemented by the remaining ipsilateral anterior sections if a sizeable tumor is seen (method 3). Methods 1 and 2 missed 13% and 21% of PMs and 28% and 47% of EPEs, respectively. Method 3 demonstrated better results missing only 5% of PMs and 7% of EPEs. Partial sampling techniques missed slightly more PMs and EPEs in patients with low-risk to intermediate-risk prostate cancer, although even in high-risk cases none of the methods detected all of the studied aggressive pathologic features.
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ABSTRACT: This study reports a modified point-count method for quantifying the extent of carcinoma in prostatectomy specimens (n = 143), as adapted from Billis et al., Int Braz J Urol. 2003.29:113-120. The prostates were studied as follows: The basal/apical margins were sampled using the cone method. The remainder of the gland was divided into 12 quadrant-shaped regions that were sampled using two slices. Eight equidistant points were marked directly on the coverslip over each fragment. The points inside the tumoral areas were counted and expressed as both the percentage of prostate gland involvement by carcinoma (PGI) and the tumor volume (TV). A significant correlation between the preoperative PSA levels and each of the three quantitative estimations were observed, with improved correlations with the PGI and TV values obtained using the point-count method (viz. number of slices involved (NSI) (r = 0.32), PGI (r = 0.39) and TV (r = 0.44). With the data sets stratified into three categories, all three methods correlated with multiple parameters, including Gleason scores ≥7, primary Gleason scores ≥4, perineural/angiolymphatic invasion, extraprostatic extension, seminal vesicle invasion and positive margins. All three quantitative methods were associated with morphologic features of tumor progression. The results obtained using this modified point-count method correlate more strongly with preoperative PSA levels.Pathology - Research and Practice 05/2014; DOI:10.1016/j.prp.2014.02.002 · 1.56 Impact Factor
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ABSTRACT: This study was conducted to evaluate the capability of detecting prostate cancer (PCa) using auto-fluorescence lifetime spectroscopy (AFLS) and light reflectance spectroscopy (LRS). AFLS used excitation at 447 nm with four emission wavelengths (532, 562, 632, and 684 nm), where their lifetimes and weights were analyzed using a double exponent model. LRS was measured between 500 and 840 nm and analyzed by a quantitative model to determine hemoglobin concentrations and light scattering. Both AFLS and LRS were taken on n = 724 distinct locations from both prostate capsular (nc = 185) and parenchymal (np = 539) tissues, including PCa tissue, benign peripheral zone tissue and benign prostatic hyperplasia (BPH), of fresh ex vivo radical prostatectomy specimens from 37 patients with high volume, intermediate-to-high-grade PCa (Gleason score, GS ≥7). AFLS and LRS parameters from parenchymal tissues were analyzed for statistical testing and classification. A feature selection algorithm based on multinomial logistic regression was implemented to identify critical parameters in order to classify high-grade PCa tissue. The regression model was in turn used to classify PCa tissue at the individual aggressive level of GS = 7,8,9. Receiver operating characteristic curves were generated and used to determine classification accuracy for each tissue type. We show that our dual-modal technique resulted in accuracies of 87.9%, 90.1%, and 85.1% for PCa classification at GS = 7, 8, 9 within parenchymal tissues, and up to 91.1%, 91.9%, and 94.3% if capsular tissues were included for detection. Possible biochemical and physiological mechanisms causing signal differences in AFLS and LRS between PCa and benign tissues were also discussed.Biomedical Optics Express 05/2014; 5(5):1512-29. DOI:10.1364/BOE.5.001512 · 3.50 Impact Factor