Systematic versus Targeted Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy among Men with Visible Lesions

Patel, Hiten D.; Koehne, Elizabeth L.; Shea, Steven M.; Fang, Andrew; Gorbonos, Alex; Quek, Marcus L.; Flanigan, Robert C.; Goldberg, Allen I.; Rais‐Bahrami, Soroush; Gupta, Gopal N.

doi:10.1097/ju.0000000000002120

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…While the study did not directly compare the effects of PHI combined with PI-RADS, it indirectly illustrated the importance of combining PHI and mpMRI, which indicated that the two indicators can also be beneficial to white and Caucasian populations. A study from Loyola and UAB populations showed that Asian American men have a lower risk of PCa (OR = 0.15, 95% CI: 0.06-0.42) and CSPCa (OR = 0.34, 95% CI: 0.12-1.02) than other races (33). For men with African ancestry, Patel et al conducted a study evaluating implications of race (African American men versus non-African American men) and tumor location for PCa detection (34).…”

Section: Discussionmentioning

confidence: 99%

Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer

Zhou

Cui

et al. 2022

Front. Oncol.

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BackgroundWith the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.MethodsWe retrospectively collected the clinical indicators of patients with TPSA at 4–20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.ResultsAmong the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4–20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4–20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4–20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.ConclusionsThe models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.

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Section: Discussionmentioning

confidence: 99%

Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer

Zhou

Cui

et al. 2022

Front. Oncol.

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“…Although guidelines on PCa management stratify low risk and veryelow risk groups by volume, oncologic implications of volume have blurred in the MRI era with improved tissue sampling from combined targeted and systematic biopsy, and changes in pathological grading. 3 Future guidelines may place a greater emphasis on imaging, genomics, and emerging tools rather than disease volume for clinically localized PCa.…”

Section: Editorial Commentsmentioning

confidence: 99%

Editorial Comment

Kovac

2023

Journal of Urology

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“…Several recent prospective studies support the use of multiparametric (mp)MRI and fusion biopsies to improve detection of clinically significant prostate cancer (csPCa) [ 1 , 2 , 3 ]. Targeted biopsies have been shown to increase detection of csPCa while reducing detection of grade group (GG)1 prostate cancer (PCa) compared to systematic biopsies alone [ 4 , 5 ]. Although it loses the benefit of reduced GG1 PCa detection, the combined biopsy approach of both targeted and systematic biopsies optimizes identification of csPCa and is often employed in clinical practice, as recommended by guidelines [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators

et al. 2022

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Objectives To develop and validate a prostate cancer (PCa) risk calculator (RC) incorporating multiparametric magnetic resonance imaging (mpMRI) and to compare its performance with that of the Prostate Biopsy Collaborative Group (PBCG) RC. Patients and Methods Men without a PCa diagnosis receiving mpMRI before biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015–2020) were included. Data from a separate institution were used for external validation. The primary outcome was diagnosis of no cancer, grade group (GG)1 PCa, and clinically significant (cs)PCa (≥GG2). Binary logistic regression was used to explore standard clinical and mpMRI variables (prostate volume, Prostate Imaging‐Reporting Data System [PI‐RADS] version 2.0 lesions) with the final PLUM RC, based on a multinomial logistic regression model. Receiver‐operating characteristic curve, calibration curves, and decision‐curve analysis were evaluated in the training and validation cohorts. Results A total of 1010 patients were included for development (N = 674 training [47.8% PCa, 30.9% csPCa], N = 336 internal validation) and 371 for external validation. The PLUM RC outperformed the PBCG RC in the training (area under the curve [AUC] 85.9% vs 66.0%; P < 0.001), internal validation (AUC 88.2% vs 67.8%; P < 0.001) and external validation (AUC 83.9% vs 69.4%; P < 0.001) cohorts for csPCa detection. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a threshold probability of 15%, the PLUM RC vs the PBCG RC could avoid 13.8 vs 2.7 biopsies per 100 patients without missing any csPCa. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for the PBCG RC. The PLUM RC compared favourably with the Stanford Prostate Cancer Calculator (SPCC; AUC 84.1% vs 81.1%; P = 0.002) and the MRI‐European Randomized Study of Screening for Prostate Cancer (ERSPC) RC (AUC 84.5% vs 82.6%; P = 0.05). Conclusions The mpMRI‐based PLUM RC significantly outperformed the PBCG RC and compared favourably with other mpMRI‐based RCs. A large proportion of biopsies could be avoided using the PLUM RC in shared decision making while maintaining optimal detection of csPCa.

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Systematic versus Targeted Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy among Men with Visible Lesions

Cited by 17 publications

References 18 publications

Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer

Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer

Editorial Comment

A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators

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