Defining clinically significant prostate cancer on the basis of pathological findings

Matoso, Andrés; Epstein, Jonathan I.

doi:10.1111/his.13712

Cited by 132 publications

(95 citation statements)

References 92 publications

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“…(4 + 3) and higher (≥grade Group 3). 11,12 We aimed to assess the association between PSA-D and pathologically significant PCa and to assess the impact of prostate volume on the relationship between PSA-D and pathologically significant PCa.…”

Section: Methodsmentioning

confidence: 99%

Association between PSA density and pathologically significant prostate cancer: The impact of prostate volume

et al. 2020

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Background: The early diagnosis of prostate cancer (PCa) is mainly based on prostate-specific antigen (PSA) blood levels and digital rectal examination. However, this approach may result in a high rate of negative biopsies and increased detection of clinically insignificant PCa (CS-PCa). An important prognostic biomarker, PSA density (PSA-D) demonstrated improved performance in PCa detection compared to PSA. The relationship between prostate volume and the prognostic accuracy of PSA-D remains mostly unclear. The aim of our study is to investigate the PSA-D predictive value of CS-PCa detection at different prostate volumes. Methods: Using our local radical prostatectomy registry, patients were divided into three prostate size subgroups based on preoperative sonographic prostate volume assessment: less than 50, 50-75, and more than 75 cc. Patients' and PCa characteristics were recorded, including age, body mass index, PSA at diagnosis, prostate volume, PSA-D, D'Amico risk classification, Gleason grade group, and pathological staging following surgery. Results: The study cohort included 364 patients who underwent Robotic Radical prostatectomy for biopsy-proven clinically localized PCa. 221 (61%) and 143 (39%) patients had PSA-D less than 0.15 and PSA-D more than 0.15, respectively. ISUP GG 1-2 PCa (CS-PCa) was observed in 220 patients (60%), while 144 (40%) had ISUP GG 3-5 PCa at final pathology. PSA-D correlated with CS-PCa only in small and medium-size prostates, but not in large glands (p = .03, p = .01, and p = .36, respectively). The highest sensitivity (72.7%) was observed in small prostates, compared to 3.2% in large prostates. The highest specificity (89.4%) was noted in large prostates. Positive predictive value in small and medium-size prostates was similar (~50%), compared to 20% in large glands. The negative predictive value was slightly better for small and medium-size prostates compared with large glands (68.9%, 73.7%, and 53.1%, respectively). An association between PSA-D and harboring CS-PCa was detected only in small and medium-size glands (72.7% and 43%, respectively). Conclusion: PSA-D is associated with CS-PCa detection in radical prostatectomy specimens in small and medium-size prostates. The level of PSA-D is directly associated with the ISUP PCa grade group. Therefore, PSA-D is a beneficial, available,

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Section: Methodsmentioning

confidence: 99%

Association between PSA density and pathologically significant prostate cancer: The impact of prostate volume

et al. 2020

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“…As stand-alone, both the mpMRI-and CUDI-TBx strategies showed reduced detection of insignificant (GG = 1) PCa, but detection and accuracy of negative imaging for GG ≥2 PCa was also insufficient. Although at present it is common to define GG ≥2 PCa on biopsy as 'csPCa', this definition is probably too stringent [36]. Particularly, GG = 2 PCa shows considerable heterogeneity in clinical outcome, as recent studies suggest that survival rates for GG = 2 PCa in the absence of CR/IDC is similar to those for GG = 1 PCa [37,38].…”

Section: Allmentioning

confidence: 99%

Detection of clinically significant prostate cancer in biopsy‐naïve men: direct comparison of systematic biopsy, multiparametric MRI‐ and contrast‐ultrasound‐dispersion imaging‐targeted biopsy

et al. 2020

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To compare and evaluate a multiparametric magnetic resonance imaging (mpMRI)-targeted biopsy (TBx) strategy, contrast-ultrasound-dispersion imaging (CUDI)-TBx strategy and systematic biopsy (SBx) strategy for the detection of clinically significant prostate cancer (csPCa) in biopsy-na€ ıve men. Patients and Methods A prospective, single-centre paired diagnostic study included 150 biopsy-na€ ıve men, from November 2015 to November 2018. All men underwent pre-biopsy mpMRI and CUDI followed by a 12-core SBx taken by an operator blinded from the imaging results. Men with suspicious lesions on mpMRI and/or CUDI also underwent MRI-TRUS fusion-TBx and/ or cognitive CUDI-TBx after SBx by a second operator. A non-inferiority analysis of the mpMRI-and CUDI-TBx strategies in comparison with SBx for International Society of Urological Pathology Grade Group [GG] ≥2 PCa in any core with a non-inferiority margin of 1 percentage point was performed. Additional analyses for GG ≥2 PCa with cribriform growth pattern and/or intraductal carcinoma (CR/IDC), and GG ≥3 PCa were performed. Differences in detection rates were tested using McNemar's test with adjusted Wald confidence intervals. Results After enrolment of 150 men, an interim analysis was performed. Both the mpMRI-and CUDI-TBx strategies were inferior to SBx for GG ≥2 PCa detection and the study was stopped. SBx found significantly more GG ≥2 PCa: 39% (56/142), as compared with 29% (41/142) and 28% (40/142) for mpMRI-TBx and CUDI-TBx, respectively (P < 0.05). SBx found significantly more GG = 1 PCa: 14% (20/142) compared to 1% (two of 142) and 3% (four of 142) with mpMRI-TBx and CUDI-TBx, respectively (P < 0.05). Detection of GG ≥2 PCa with CR/IDC and GG ≥3 PCa did not differ significantly between the strategies. The mpMRI-and CUDI-TBx strategies were comparable in detection but the mpMRI-TBx strategy had less false-positive findings (18% vs 53%). Conclusions In our study in biopsy-na€ ıve men, the mpMRI-and CUDI-TBx strategies had comparable PCa detection rates, but the mpMRI-TBX strategy had the least false-positive findings. Both strategies were inferior to SBx for the detection of GG ≥2 PCa, despite reduced detection of insignificant GG = 1 PCa. Both strategies did not significantly differ from SBx for the detection of GG ≥2 PCa with CR/IDC and GG ≥3 PCa.

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“…The sampling bias is problematic as the risk group influences the therapeutic approach [1,4,8,10]. Definition of clinically significant PCa is a challenging dynamic process with ongoing debates [10][11][12][13]. Patients with Gleason Grade Group (GrG) ≤ 2 have a much better prognosis than those with GrG ≥ 3 [11,12].…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

et al. 2020

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Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

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Defining clinically significant prostate cancer on the basis of pathological findings

Cited by 132 publications

References 92 publications

Association between PSA density and pathologically significant prostate cancer: The impact of prostate volume

Association between PSA density and pathologically significant prostate cancer: The impact of prostate volume

Detection of clinically significant prostate cancer in biopsy‐naïve men: direct comparison of systematic biopsy, multiparametric MRI‐ and contrast‐ultrasound‐dispersion imaging‐targeted biopsy

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

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