Prostate cancer grading: the effect of stratification of needle biopsy Gleason Score 4 + 3 as high or intermediate grade.
ABSTRACT To assess the discrepancy between needle biopsy (NB) and radical prostatectomy (RP) Gleason score (GS) in Irish men, specifically the influence of the stratification of GS 4 + 3 on overall levels of agreement, levels of discrepancy and kappa coefficients, as the GS assigned to prostate cancer NBs affects clinical decision-making and influences future therapeutic strategies.
We reviewed retrospectively a database of the discrepancies between NB and RP Gleason grades (GG) from 2003 to 2008. All patients had clinically localized prostate cancer, and none had had neoadjuvant therapy. Grading of 206 NB specimens was compared with their corresponding RP specimens. The discrepancy rate between NB and RP GS was assessed for each combination of GG. Intermediate- (GS 7, defined as GS 3 + 4 alone vs GS 7) and high-grade (GS 4 + 3 and GS 8-10 vs GS 8-10) classifications were compared. The level of agreement and the kappa coefficient for each system was assessed.
In NB, GS 6 was most frequently diagnosed (53%); after RP, GS 3 + 4 was most frequent (36%). In 42% of cases the exact GG remained unchanged after RP, increasing to 48% for GS 6 and GS 3 + 4. Overall 42% of cases showed an increase in their GG. In GS 6 NBs, the rate of increase in the primary GG or increase in the GS was 52%. Biopsy GS 6 and 3 + 4 showed the highest levels of agreement between NB and RP. Low-grade prostate cancer on NB was upgraded in 52% of cases; high-grade prostatic adenocarcinoma was downgraded in 27-77% of cases depending on the grading system used.
Classification of high-grade prostate cancer as GS 4 + 3 and GS 8-10 results in higher levels of agreement between NB and RP GS. Reliable identification of well differentiated prostatic adenocarcinoma in NB specimens represents an ongoing diagnostic challenge, necessitating careful preoperative consideration of the definitive grade of a patient's disease.
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ABSTRACT: Abstract Objective. The aim of this study was to analyse whether primary Gleason pattern in biopsy Gleason score (GS) 7 predicted adverse histopathological features and had an impact on the risk of biochemical failure in a consecutive series of patients undergoing radical prostatectomy (RP). Material and methods. Between 2006 and 2010, 441 patients with biopsy GS 7 underwent RP at Rigshospitalet, Copenhagen, Denmark. Favourable histopathological features were defined as pT2 margin-negative cancer, RP specimen GS ≤ 3+4 and no lymph-node metastasis. Adverse histopathological features were defined as advanced pT3/4 cancer or pT2 margin-positive cancer and/or RP specimen GS ≥ 4+3 and/or positive lymph nodes. Biochemical failure was defined as the first prostate-specific antigen (PSA) ≥ 0.2 ng/ml. Results. A total of 344 patients (78.0%) had GS 3+4 in biopsies, while 97 patients (22.0%) had GS 4+3. No difference in age, PSA, percentage of biopsies with cancer, clinical tumour stage or volume on transrectal ultrasonography was found. Primary Gleason pattern 4 was associated with worse pathological stage (p = 0.049). On multivariate analysis, primary Gleason pattern 4 (p < 0.0001), cT stage (p = 0.024), PSA (p < 0.0001) and age (p = 0.009) predicted adverse histopathological features. In univariate analysis, Gleason score 3+4 had a significantly lower biochemical failure rate compared with Gleason score 4+3 (p = 0.0035). PSA (p < 0.0001), primary Gleason pattern 4 (p = 0.001) and percentage of biopsies with cancer (p = 0.02) were independently associated with risk of biochemical failure. Conclusions. In biopsies with GS 7, a primary Gleason pattern 4 was associated with significantly elevated risk of adverse histopathological features and biochemical failure compared to pattern 3 in patients undergoing RP. This study underlines the heterogeneity of biopsy GS 7.Scandinavian journal of urology. 07/2013;
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ABSTRACT: Efforts to improve the diagnosis, prognosis and surveillance of prostate cancer (PCa) are relevant. Gleason score (GSc) overestimation may subject individuals to unnecessary aggressive treatment. We aimed to use stereology in PCa evaluations and investigate whether mean nuclear volume (MNV) correlates with the Gleason primary pattern (Gpp) and to improve the subjective GSc to obtain an objective and reliable method without inter-observer dissension.PLoS ONE 01/2014; 9(7):e102156. · 3.53 Impact Factor
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ABSTRACT: There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease. A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study. 30 % of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage. This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician's face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.BMC Medical Informatics and Decision Making 11/2013; 13(1):126. · 1.60 Impact Factor