gp-4 and Prostatic-Neoplasms

Quantitative Gleason grading appears to be a reliable prognostic parameter and provides broader risk stratification then the traditional Gleason grading in patients with prostate cancer (PCa) treated with radical prostatectomy (RP).. To determine if quantification of Gleason pattern (GP) 4 for targeted and systematic biopsy (TBx + SBx) cores together with further clinical variables can identify the lowest quantitative GP 4 fraction on RP.. A total of 548 patients underwent TBx + SBx of the prostate and then RP, with pathology revealing Gleason score 3 + 4, 4 + 3, or 4 + 4 disease.. TBx + SBx of the prostate followed by RP.. GP 4 fraction thresholds of ≤5%, ≤10%, ≤15%, ≤20%, and ≤25% were compared between the TBx + SBx and RP specimens. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy for predicting the GP 4 fraction in the RP specimen were determined. Logistic regression models were used to establish a probabilistic relationship between various combinations of clinical and biopsy variables and the GP 4 fraction in the RP specimen.. GP 4 fractions of ≤5%, ≤10%, ≤15%, ≤20%, and ≤25% was observed in 33%, 49%, 58%, 65%, and 70% of patients on TBx, and 18%, 41%, 53%, 63%, and 70% of patients on RP, respectively. The sensitivity, specificity, NPV, PPV, and accuracy were 75%, 67%, 91%, 39%, and 74% for a TBx GP 4 fraction of ≤5%, and 65%, 85%, 65%, 85%, and 79% for a TBx GP 4 fraction of ≤25%, respectively. A model combining quantified TBx + SBx GP 4 with clinical parameters demonstrated the highest diagnostic accuracy. Limitations include the retrospective study design.. Our results demonstrate that the combination of MRI-TBx + SBx and GP 4 quantification allowed precise detection of a low fraction of GP 4 when using RP specimens as the reference standard. Moreover, we found that clinical variables including Prostate Imaging-Reporting and Data System score without biopsy are limited in detection of low GP 4 fractions.. Combination of targeted biopsy alone as well as combined with systematic biopsy and quantitative Gleason grading of biopsy specimen showed high agreement with pathology findings after surgical removal of the prostate. This could help in identifying patients who are suitable for active surveillance.

Topics: Humans; Image-Guided Biopsy; Male; Multiparametric Magnetic Resonance Imaging; Neoplasm Grading; Prostate; Prostatectomy; Prostatic Neoplasms; Retrospective Studies; Watchful Waiting

To determine the prognostic significance of the maximum allowable percentage of Gleason pattern 4 (GP4) at prostate biopsy compared with adverse pathology observed at radical prostatectomy (RP) to expand active surveillance eligibility among a cohort with intermediate risk of prostate cancer.. A retrospective study of patients with grade group (GG) 1 or 2 prostate cancer on prostate biopsy with subsequent RP was performed at our institution. A Fisher exact test was used to understand the relationship among GP4 subgroups (0%, ≤5%, 6%-10%, and 11%-49%) assigned at biopsy and adverse pathologic findings at RP. Additional analyses comparing the GP4 ≤5% cohort's prebiopsy prostate-specific antigen (PSA) level and GP4 length with adverse pathology at RP were also performed.. No statistically significant difference in adverse pathology at RP was observed between the active surveillance-eligible control (GP4 0%) and the GP4 ≤5% subgroup. In total, 68.9% of the GP4 ≤5% cohort showed favorable pathologic outcomes. A separate analysis of the GP4 ≤5% subgroup revealed that neither prebiopsy serum PSA levels nor GP4 length showed statistical correlation with adverse pathology at RP.. Active surveillance may be a reasonable option for management of patients in the GP4 ≤5% group until long-term follow-up data become available.

Topics: Biopsy; Humans; Male; Neoplasm Grading; Prostate; Prostate-Specific Antigen; Prostatectomy; Prostatic Neoplasms; Retrospective Studies; Watchful Waiting

Differential classification of prostate cancer grade group (GG) 2 and 3 tumors remains challenging, likely because of the subjective quantification of the percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-predicted %GP4 is associated with biochemical recurrence (BCR) risk in intermediate-risk GG 2 and 3 tumors. The study was conducted in a radical prostatectomy cohort (1999-2012) of African American men from the Henry Ford Health System (Detroit, Michigan). A CNN model that could discriminate 4 tissue types (stroma, benign glands, GP3 glands, and GP4 glands) was developed using histopathologic images containing GG 1 (n = 45) and 4 (n = 20) tumor foci. The CNN model was applied to GG 2 (n = 153) and 3 (n = 62) tumors for %GP4 estimation, and Cox proportional hazard modeling was used to assess the association of %GP4 and BCR, accounting for other clinicopathologic features including GG. The CNN model achieved an overall accuracy of 86% in distinguishing the 4 tissue types. Furthermore, CNN-predicted %GP4 was significantly higher in GG 3 than in GG 2 tumors (P = 7.2 × 10

Topics: Artificial Intelligence; Humans; Male; Neoplasm Grading; Neoplasm Recurrence, Local; Neural Networks, Computer; Prostatectomy; Prostatic Neoplasms; Reproducibility of Results