Combination MRI-targeted and systematic prostate biopsy may overestimate gleason grade on final surgical pathology and impact risk stratification

Yu, Alice; Yamany, Tammer; Mojtahed, Amirkasra; Hanna, Nawar; Nicaise, Edouard; Harisinghani, Mukesh G.; Wu, Chin‐Lee; Dahl, Douglas M.; Wszolek, Matthew F.; Blute, Michael L.; Feldman, Adam S.

doi:10.1016/j.urolonc.2021.07.027

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Gleason grade concordance in the literature ranges from 38 to 63% 17 – 20 . Upgraded cases occur at a rate of 25% to 56% 17 – 20 , significantly outweighing downgrading cases in the majority of the studies, which range from 8% to 16% 18 , 19 , 21 . Although the upgrades in our GG1 group (17 to 20) are remarkable, Costa et al reported a 66.7% upgrade in the GG1 group 22 .…”

Section: Discussionmentioning

confidence: 86%

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology

Ozbozduman,

Loc,

Durmaz

et al. 2024

Sci Rep

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This study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64.2% had consistent GG results between in-bore biopsies and RP, whereas 28.4% had upgraded and 7.4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with $$\hbox {GG}>1$$ GG > 1 , larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85.6% accuracy in predicting upgrades, surpassing the 64.2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient ($$\hbox {ADC}_{\text{min}}$$ ADC min ) for $$\hbox {GG}>1$$ GG > 1 patients. Incorporation of MRGB results with tumor size, $$\hbox {ADC}_{\text{min}}$$ ADC min value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.

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Section: Discussionmentioning

confidence: 86%

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology

Ozbozduman,

Loc,

Durmaz

et al. 2024

Sci Rep

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“…However, in our study, we did not analyze the effects of omitting SB and clinical predictors on the underestimation of the Gleason score, and this is also a clinically relevant issue considering the justification for performing SB. Several recent studies indicate that SB may reduce the likelihood of Gleason score underestimation [36,37]. Moreover, taking cores from areas surrounding a suspicious lesion (focal systematic biopsy) can overlap sampling error and may provide a better estimation of the Gleason score [38].…”

Section: Discussionmentioning

confidence: 99%

Prostate Biopsy in the Case of PIRADS 5—Is Systematic Biopsy Mandatory?

Malewski,

Milecki,

Szempliński

et al. 2023

JCM

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Combining systematic biopsy (SB) with targeted biopsy (TB) in the case of a positive result from multiparametric magnetic resonance imaging (mpMRI) is a matter of debate. The Prostate Imaging Reporting and Data System (PIRADS) score of 5 indicates the highest probability of clinically significant prostate cancer (csPC) detection in TB. Potentially, omitting SB in the case of PIRADS 5 may have a marginal impact on the csPC detection rate. The aim of this study was to determine whether SB can be avoided in the case of PIRADS 5 and to identify potential factors allowing for performing TB only. This cohort study involved n = 225 patients with PIRADS 5 on mpMRI (PIRADS 2.0/2.1) who underwent transperineal or transrectal combined biopsy (CB). CsPC was diagnosed in 51.6% (n = 116/225) of cases. TB and SB resulted in the detection of csPC in 48% (n = 108/225) and 20.4% (n = 46/225) of cases, respectively (TB vs. SB, p < 0.001). When the TB was positive, SB detected csPC in n = 38 of the cases (38/108 = 35%). SB added to TB significantly improved csPC detection in 6.9% of cases in absolute terms (n = 8/116) (TB vs. CB, p = 0.008). The multivariate regression model proved that the significant predictors of csPC detection via SB were the densities of the prostate-specific antigen—PSAD > 0.17 ng/mL2 (OR = 4.038, 95%CI: 1.568–10.398); primary biopsy setting (OR = 2.818, 95%CI: 1.334–5.952); and abnormal digital rectal examination (DRE) (OR = 2.746, 95%CI: 1.328–5.678). In a primary biopsy setting (n = 103), SB detected 10% (n = 6/60) of the additional cases of csPC (p = 0.031), while in a repeat biopsy setting (n = 122), SB detected 3.5% (n = 2/56) of the additional cases of csPC (p = 0.5). In the case of PSAD > 0.17 ng/mL2 (n = 151), SB detected 7.4% (n = 7/95) of additional cases of csPC (p = 0.016), while in the case of PSAD < 0.17 ng/mL2 (n = 74), SB detected 4.8% (n = 1/21) of the additional cases of csPC (p = 1.0). The omission of SB had an impact on the csPC diagnosis rate in patients with PIRADS 5 score lesions. Patients who have already undergone prostate biopsy and those with low PSAD are at a lower risk of missing csPC when SB is avoided. However, performing TB only may result in missing other csPC foci located outside the index lesion, which can alter treatment decisions.

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“…However, the imprecise localization of cancer focus by systematic biopsy has caused the overdiagnosis of clinically unimportant diseases and the underdiagnosis of clinically significant cancer [2]. Although invasive multiparametric magnetic resonance imaging-ultrasound (mpMRI-US) fusion biopsy has been demonstrated to improve the detection of clinically significant PCa (csPCa) and is more frequently used in clinical practice [3], the biopsy is associated with numerous complications, including bleeding, infection, and lower urinary tract symptoms [4]. Therefore, the reduction of prostate biopsy is crucial in males with low-grade or no PCa.…”

Section: Introductionmentioning

confidence: 99%

Developing a predictive model for clinically significant prostate cancer by combining age, PSA density, and mpMRI

Ma¹,

Wang

Zhang

et al. 2023

World J Surg Onc

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Purpose The study aimed to construct a predictive model for clinically significant prostate cancer (csPCa) and investigate its clinical efficacy to reduce unnecessary prostate biopsies. Methods A total of 847 patients from institute 1 were included in cohort 1 for model development. Cohort 2 included a total of 208 patients from institute 2 for external validation of the model. The data obtained were used for retrospective analysis. The results of magnetic resonance imaging were obtained using Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). Univariate and multivariate analyses were performed to determine significant predictors of csPCa. The diagnostic performances were compared using the receiver operating characteristic (ROC) curve and decision curve analyses. Results Age, prostate-specific antigen density (PSAD), and PI-RADS v2.1 scores were used as predictors of the model. In the development cohort, the areas under the ROC curve (AUC) for csPCa about age, PSAD, PI-RADS v2.1 scores, and the model were 0.675, 0.823, 0.875, and 0.938, respectively. In the external validation cohort, the AUC values predicted by the four were 0.619, 0.811, 0.863, and 0.914, respectively. Decision curve analysis revealed that the clear net benefit of the model was higher than PI-RADS v2.1 scores and PSAD. The model significantly reduced unnecessary prostate biopsies within the risk threshold of > 10%. Conclusions In both internal and external validation, the model constructed by combining age, PSAD, and PI-RADS v2.1 scores exhibited excellent clinical efficacy and can be utilized to reduce unnecessary prostate biopsies.

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Combination MRI-targeted and systematic prostate biopsy may overestimate gleason grade on final surgical pathology and impact risk stratification

Cited by 12 publications

References 17 publications

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology

Prostate Biopsy in the Case of PIRADS 5—Is Systematic Biopsy Mandatory?

Developing a predictive model for clinically significant prostate cancer by combining age, PSA density, and mpMRI

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