Risk of prostate cancer for men with prior negative biopsies undergoing magnetic resonance imaging compared with biopsy‐naive men: A prospective evaluation of the PLUM cohort

Patel, Hiten D.; Koehne, Elizabeth L.; Shea, Steven M.; Bhanji, Yasin; Gerena, Marielia; Gorbonos, Alex; Quek, Marcus L.; Flanigan, Robert C.; Goldberg, Allen I.; Gupta, Gopal N.

doi:10.1002/cncr.33875

Cited by 35 publications

(29 citation statements)

References 16 publications

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“…In this multi‐institutional study of men with a PI‐RADS 3 index lesion who underwent mpMRI‐targeted biopsy, CSPCa was detected in approximately 17% of men. This is consistent with the reported detection rates of approximately 20% 8, 9, 11, 13, 18 . Interestingly, targeted biopsy alone detected the highest GG of CSPCa in only 37.5% of men with a PI‐RADS 3 index lesion, whereas a template biopsy detected the highest GG in 42.1% of men.…”

Section: Discussionsupporting

confidence: 90%

“…The application of their model produced an AUC of 0.84, and biopsies were reduced by 18% without any CSPCas being missed. Patel et al 13 recently created their own risk model after analyzing 900 men who were either biopsy‐naive or had a prior negative biopsy. They too found that age, PSA density, prostate volume, and PI‐RADS score were predictive of CSPCa.…”

Section: Discussionmentioning

confidence: 99%

“…Prediction models have been used to better assess a patient's candidacy for pursuing prostate biopsy, with many models now including the data obtained from mpMRI. 13,15,21,22 Mehralivand et al 15 developed a prebiopsy risk model after analyzing 400 men with mpMRI before MRI-TRUS fusion biopsy and 12-core systematic biopsy. The application of their model produced an AUC of 0.84, and biopsies were reduced by 18% without any CSPCas being missed.…”

Section: Discussionmentioning

confidence: 99%

“…Multivariable regression was applied to assess which variables were associated with CSPCa. These variables were either factors classically associated with CSPCa (age, race, PSA density, prostate volume, and family history) or factors hypothesized to be associated with CSPCa (biopsy history, AS, and anterior lesion location) 11–15 . Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for these variables by single and multiple logistic regression.…”

Section: Methodsmentioning

confidence: 99%

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Multi‐institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI‐RADS 3 lesions

Fang

Shumaker

Martin

et al. 2022

Cancer

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BACKGROUND: Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk factors that predict CSPCa in men with PI-RADS 3 lesions. METHODS: This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model. RESULTS: Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%. CONCLUSIONS: For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multi‐institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI‐RADS 3 lesions

Fang

Shumaker

Martin

et al. 2022

Cancer

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“…Furthermore, the patients of this cohort were biopsy-naïve, and the biopsy setting could influence the performance of models. A prospective study involving 900 patients conducted by Patel et al illustrated that patients with prior negative biopsies had lower PCa detection (27.9% vs 54.4%) in comparison with biopsy-naive men (35), demonstrating the importance of the biopsy setting in cancer detection.…”

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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Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

Patel,

Remmers,

Ellis

et al. 2024

JAMA Netw Open

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ImportanceMagnetic resonance imaging (MRI)–based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms.ObjectiveTo externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator–MRI [RPCRC-MRI]) in cohorts from Europe and North America.Design, Setting, and ParticipantsThis multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy.InterventionsProstate MRI followed by prostate biopsy.Main Outcomes and MeasuresThe primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated.ResultsA total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort.Conclusions and RelevanceIn this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.

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Risk of prostate cancer for men with prior negative biopsies undergoing magnetic resonance imaging compared with biopsy‐naive men: A prospective evaluation of the PLUM cohort

Cited by 35 publications

References 16 publications

Multi‐institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI‐RADS 3 lesions

Multi‐institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI‐RADS 3 lesions

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

Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

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