Prevalence and Risk Factors of Prostate Cancer in Chinese Men with PSA 4–10 ng/mL Who Underwent TRUS-Guided Prostate Biopsy: The Utilization of PAMD Score

Fang, Dong; Ren, Da; Zhao, Chen; Li, Xuesong; Yu, Wei; Wang, Rui; Xi, Chenguang; He, Qun; Wang, Xiaoying; Xin, Zhongcheng; Zhou, Liqun

doi:10.1155/2015/596797

Cited by 10 publications

(29 citation statements)

References 29 publications

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“…In fact, the PSA in the gray zone, which groups up to 80% of biopsies, was unnecessary [ 5 ]. Therefore, a better risk prediction method specific to these patients is needed [ 21 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Zhang

Tang

et al. 2022

Diagnostics

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Objective: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4–10.0 ng/mL) based on radiomics and other traditional clinical parameters. Methods: In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. Results: The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. Conclusion: The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

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“…In fact, the PSA in the gray zone, which groups up to 80% of biopsies, was unnecessary [ 5 ]. Therefore, a better risk prediction method specific to these patients is needed [ 21 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Zhang

Tang

et al. 2022

Diagnostics

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“…The +EQS model is different from previously proposed prediction models (15)(16)(17)(18)(19). This is to be expected, since such models are sensitive to differences in patient demographics, sample size, and the nature and type of independent and dependent variables included in the study design.…”

Section: Discussionmentioning

confidence: 88%

“…Niu et al proposed an effective prediction model to diagnose high-grade prostate cancer (HGPCa) in grey zone patients based on MP-MRI (15); but the study included only one imaging method (MRI) and the result was not externally validated. Fang et al proposed a 'prostate volume + age + magnetic resonance imaging + digital rectal examination (PAMD)' score based on 345 grey zone cases, and the model showed good predictive accuracy for HGPCa (16). Other prediction models for grey zone patients have been proposed in Japan, USA, and Korea (17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

“…Other prediction models for grey zone patients have been proposed in Japan, USA, and Korea (17)(18)(19). Although MRI-based prediction models in the above studies (15)(16)(17)(18)(19) have shown good PCa predictive ability for PSA grey zone patients, the modalities have some limitations (20), such as lower specificity; benign lesions, such as prostatitis, scarring, high-grade prostatic intraepithelial neoplasia, and hyperplasia, display PCa characteristics on MRI. In addition, there could be inter-observer inconsistencies in MRI (21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a nomogram for predicting prostate cancer in men with prostate-specific antigen grey zone based on retrospective analysis of clinical and multi-parameter magnetic resonance imaging/transrectal ultrasound fusion-derived data

Ding¹,

Wu²,

Song³

et al. 2020

Transl Androl Urol

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Background: Urologists face a dilemma when deciding whether prostate biopsy is required for patients with prostate-specific antigen (PSA) levels in the grey zone (4 to 10 ng/mL). Methods:We retrospectively analyzed data from consecutive patients with PSA levels in grey zone, who underwent targeted multiparametric magnetic resonance imaging (MP-MRI)/transrectal ultrasound (TRUS) fusion biopsy with elastography between November 2017 and December 2019 in our hospital. The patientse data including age, PSA, fPSA (free PSA), fPSA/PSA, PSA density (PSAD), prostate volume, elastography Q-analysis score (EQS), and prostate imaging-reporting and data system (PI-RADS) score were collected. The nomogram was built using logistic regression and the final cohort of patients was randomly divided into a training cohort (70%) and a validation cohort (30%) by R software. The models were evaluated by receiver operating characteristic curve (ROC) analysis and calibration curve analysis. The nomogram was constructed from the best model. Results: The final study cohort consisted of 155 patients (training cohort, 109 patients; validation cohort, 46 patients) with PSA in the grey zone, of which 36 patients were pathologically diagnosed with PCa. The EQS model, −EQS model, +EQS model were built. The +EQS model that consisted of fPSA/PSA, EQS, and PI-RADS score had the best PCa diagnostic accuracy (development and validation, 0.783 and 0.781) and probability score (development and validation, 0.939 vs. 0.622). The new nomogram based on this model was constructed, in which fPSA/PSA ratio had the largest impact, followed by PI-RADS and EQS.Conclusions: Elastography and pre-biopsy MP-MRI has clinical significance for patients with PSA in the grey zone. The new nomogram, which is based on pre biopsy data including serological analysis, PI-RADS score, and EQS, can be helpful for clinical decision-making to avoid unnecessary biopsy.

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“…The main challenge is to validate the performance of the biomarkers in a clinical cohort independently and to demonstrate the clinical utility clearly [ 27 ]. Fang et al [ 28 ] developed a ‘PAMD’ score which based on mp-MRI to categorize patients into three risk groups, and the model showed good predictive accuracy for HGPCa (AUC = 0.824). In their study, the prostate volume was determined by TRUS, and the results was not proved by validation cohort.…”

Section: Discussionmentioning

confidence: 99%

Developing a nomogram based on multiparametric magnetic resonance imaging for forecasting high-grade prostate cancer to reduce unnecessary biopsies within the prostate-specific antigen gray zone

Niu

Das

et al. 2017

BMC Med Imaging

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BackgroundSince 1980s the application of Prostate specific antigen (PSA) brought the revolution in prostate cancer diagnosis. However, it is important to underline that PSA is not the ideal screening tool due to its low specificity, which leads to the possible biopsy for the patient without High-grade prostate cancer (HGPCa). Therefore, the aim of this study was to establish a predictive nomogram for HGPCa in patients with PSA 4–10 ng/ml based on Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), MRI-based prostate volume (PV), MRI-based PV-adjusted Prostate Specific Antigen Density (adjusted-PSAD) and other traditional classical parameters.MethodsBetween January 2014 and September 2015, Of 151 men who were eligible for analysis were formed the training cohort. A prediction model for HGPCa was built by using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between October 2015 and October 2016 (n = 74). The relationship between the nomogram-based risk-score as well as other parameters with Gleason score (GS) was evaluated. All patients underwent 12-core systematic biopsy and at least one core targeted biopsy with transrectal ultrasonographic guidance.ResultsThe multivariate analysis revealed that patient age, PI-RADS v2 score and adjusted-PSAD were independent predictors for HGPCa. Logistic regression (LR) model had a larger AUC as compared with other parameters alone. The most discriminative cutoff value for LR model was 0.36, the sensitivity, specificity, positive predictive value and negative predictive value were 87.3, 78.4, 76.3, and 90.4%, respectively and the diagnostic performance measures retained similar values in the validation cohort (AUC 0.82 [95% CI, 0.76–0.89]). For all patients with HGPCa (n = 50), adjusted-PSAD and nomogram-based risk-score were positively correlated with the GS of HGPCa in PSA gray zone (r = 0.455, P = 0.002 and r = 0.509, P = 0.001, respectively).ConclusionThe nomogram based on multiparametric magnetic resonance imaging (mp-MRI) for forecasting HGPCa is effective, which could reduce unnecessary prostate biopsies in patients with PSA 4–10 ng/ml and nomogram-based risk-score could provide a more robust parameter of assessing the aggressiveness of HGPCa in PSA gray zone.

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Prevalence and Risk Factors of Prostate Cancer in Chinese Men with PSA 4–10 ng/mL Who Underwent TRUS-Guided Prostate Biopsy: The Utilization of PAMD Score

Cited by 10 publications

References 29 publications

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Development and validation of a nomogram for predicting prostate cancer in men with prostate-specific antigen grey zone based on retrospective analysis of clinical and multi-parameter magnetic resonance imaging/transrectal ultrasound fusion-derived data

Developing a nomogram based on multiparametric magnetic resonance imaging for forecasting high-grade prostate cancer to reduce unnecessary biopsies within the prostate-specific antigen gray zone

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