Dataset Information

The highest percentage of Gleason Pattern 4 is a predictor in intermediate-risk prostate cancer.

ABSTRACT:

Objectives

This study aims to clarify the clinicopathological significance of several novel pathological markers, including the percentage of Gleason pattern 4 and small/non-small cribriform pattern, in intermediate-risk Gleason score 3 + 4 = 7 prostate cancer.

Subjects and methods

Two-hundred and twenty-eight patients with Gleason score 3 + 4 = 7 intermediate-risk prostate cancer who underwent radical prostatectomy between 2009 and 2019 at our institute were selected. Preoperative clinicopathological characteristics, including serum prostate-specific antigen level, clinical T stage, percentage of cancer-positive cores at biopsy, small/non-small cribriform pattern, the highest percentage of Gleason pattern 4, the total length of Gleason pattern 4 and percentage of Gleason score 7 cores were examined in univariate/multivariate logistic regression analysis to determine their predictive value for postoperative adverse pathological findings, defined as an upgrade to Gleason score 4 + 3 = 7 or higher, pN1 or pT3b disease.

Results

Fifty-four cases (23.7%) showed adverse pathological findings. Although a non-small cribriform pattern, highest Gleason pattern 4 percentage and total length of Gleason pattern 4 were predictive of adverse pathological findings in univariate analysis, only the highest Gleason pattern 4 percentage was an independent predictive factor in multivariate analysis (odds ratio: 1.610; 95% confidence interval: 1.260-2.070; P = 0.0002).

Conclusion

The highest Gleason pattern 4 percentage was a potent predictive parameter for Gleason score 3 + 4 = 7 intermediate-risk prostate cancer and should be considered in the risk classification scheme for prostate cancer.

SUBMITTER: Sato S

PROVIDER: S-EPMC9931537 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

The highest percentage of Gleason Pattern 4 is a predictor in intermediate-risk prostate cancer.

Sato Shun S Kimura Takahiro T Onuma Hajime H Egawa Shin S Shimoda Masayuki M Takahashi Hiroyuki H

BJUI compass 20221017 2

<h4>Objectives</h4>This study aims to clarify the clinicopathological significance of several novel pathological markers, including the percentage of Gleason pattern 4 and small/non-small cribriform pattern, in intermediate-risk Gleason score 3 + 4 = 7 prostate cancer.<h4>Subjects and methods</h4>Two-hundred and twenty-eight patients with Gleason score 3 + 4 = 7 intermediate-risk prostate cancer who underwent radical prostatectomy between 2009 and 2019 at our institute were selected. Preoperativ ...[more]

PMID: 36816145

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Project description:PURPOSE:Very high-risk prostate cancer (PC) is associated with poor response to local and systemic treatments; however, few cases have been molecularly profiled. We studied clinical outcomes and molecular profiles of patients with clinically localized primary Gleason pattern 5 PC. PATIENTS AND METHODS:Clinicopathologic features, targeted somatic and germline sequencing, and PTEN, TP53, and ERG status by immunohistochemistry were assessed in patients undergoing surgery from 2005 to 2015; 60 consecutive patients were identified with Gleason score 5 + 4 = 9 or 5 + 5 = 10 PC after radical prostatectomy with available tissue and clinical follow-up. Clinicopathologic and genomic parameters were correlated with biochemical relapse, metastasis-free survival, time to castration resistance, and overall survival using Cox proportional hazards models. RESULTS:Of patients with somatic sequencing data and clinical follow-up, 34% had DNA repair gene mutations, including 22% (11 of 49) with homologous recombination and 12% (six of 49) with mismatch repair gene alterations. Homologous recombination mutations were germline in 82% (nine of 11) of patients. In addition, 33% (16 of 49) had TP53 mutation, and 51% (29 of 57) had PTEN loss. Overall, 43% developed metastasis, with a time to castration resistance of 12 months. On multivariable analysis of clinicopathologic variables, only ductal/intraductal histology (hazard ratio, 4.43; 95% CI, 1.76 to 11.15; P = .002) and seminal vesicle invasion (hazard ratio, 5.14; 95% CI, 1.83 to 14.47; P = .002) were associated with metastasis. Among genomic alterations, only TP53 mutation and PTEN loss were associated with metastasis on univariable analysis, and neither remained significant in multivariable analyses. These data are retrospective and hypothesis generating. CONCLUSION:Potentially actionable homologous recombination and mismatch repair alterations are observed in a significant proportion of patients with very high-risk PC at the time of radical prostatectomy. These findings could inform the design of prospective trials in this patient population.

Project description:ObjectiveThe purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists.MethodsA retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists.ResultsThe best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82).ConclusionsMachine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists.Key points• Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Project description:BACKGROUND:While the new Gleason grade grouping (GGG), which started in 2016, has been widely validated in prostate cancer, it does not incorporate the concept of tertiary Gleason pattern 5. Furthermore, no study has "quantified" the individual risk of each Gleason pattern, including tertiary Gleason pattern 5, after radical prostatectomy. METHODS:We reviewed 1022 men with adjuvant-treatment-naïve prostate cancer who underwent radical prostatectomy between 2005 and 2017. The primary endpoint was biochemical recurrence-free survival, defined as two consecutive prostate-specific antigen measurements ?0.2?ng/ml after surgery. The individual quantitative risk score (IQRS) of each amount (primary/secondary/tertiary) of each Gleason pattern (3/4/5) was calculated using the Cox regression model. On the basis of the IQRS, the modified Gleason grade grouping (mGGG) model was developed. As a robustness analysis of the mGGG model, salvage treatment-free survival was also assessed. RESULTS:During a median follow-up of 45?months, 229 of 1022 (22.4%) patients developed biochemical recurrence. The IQRS of each Gleason pattern was as follows: primary 5, 1.81 points (hazard ratio [HR] 6.13); secondary 5, 1.37 points (HR 3.92); tertiary 5, 0.87 points (HR 2.39); primary 4, 1.07 points (HR 2.91); secondary 4, 0.79 points (HR 2.21); and any Gleason pattern 3, 0 points (HR 1). Based on the IQRS, the mGGG model was developed, which classified patients into the following five groups: I (3?+?3 or less); II (3?+?4); III (4?+?3); IV (3?+?4?+?t5, 4?+?3?+?t5, 3?+?5, 5?+?3, and 4?+?4); V (4?+?4?+?t5, 4?+?5, 5?+?4, and 5?+?5). The c-index for biochemical recurrence-free survival was significantly improved from 0.655 of the original GGG model to 0.672 of the mGGG model (P?<?0.05). In the robustness analysis, the c-index for salvage treatment-free survival was also significantly improved from 0.619 of the original GGG model to 0.638 of the mGGG model (P?<?0.05). CONCLUSIONS:The quantitative risk of tertiary (<?5%) Gleason pattern 5 is slightly higher than that of secondary (5-50%) Gleason pattern 4. Our newly developed mGGG model more accurately predicts outcomes after radical prostatectomy than the original GGG model.

Dataset Information

The highest percentage of Gleason Pattern 4 is a predictor in intermediate-risk prostate cancer.

Objectives

Subjects and methods

Results

Conclusion

Publications

The highest percentage of Gleason Pattern 4 is a predictor in intermediate-risk prostate cancer.

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