Could Magnetic Resonance Imaging Help to Identify the Presence of Prostate Cancer Before Initial Biopsy? The Development of Nomogram Predicting the Outcomes of Prostate Biopsy in the Chinese Population

Fang, Dong; Zhao, Chen; Ren, Da; Yu, Wei; Wang, Rui; Li, Xuesong; Yin, Wenshi; Yu, Xiaoming; Yang, Kun‐Lin; Liu, Pei; Gao, Shan; Liu, Shuqing; He, Qun; Wang, Xiaoying; Xin, Zhongcheng; Zhou, Liqun

doi:10.1245/s10434-016-5438-2

Cited by 19 publications

(13 citation statements)

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“…Fang et al incorporated PI-RADS score on pre-biopsy MRI into nomograms showed a good diagnostic performance of the accuracies of detecting prostate cancer (87.5%) and high-grade prostate cancer (87.2%), suggesting that the pre-biopsy MRI could increase predictive accuracy. [ 24 ] In our models that included PPFT and PI-RADS as both parameters performed on mp-MRI, the accuracy of detecting prostate cancer and high-grade prostate cancer was 92.2% and 91.9%, respectively, which were significantly superior to the single PI-RADS score as well as any other variable. The predictive accuracies exhibited good performance compared to previous studies.…”

Section: Discussionmentioning

confidence: 96%

The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer

Cao

Chen

et al. 2017

Oncotarget

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PurposeTo evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).ResultsOverall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.Materials and MethodsThe clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.ConclusionThe PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance.

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Section: Discussionmentioning

confidence: 96%

The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer

Cao

Chen

et al. 2017

Oncotarget

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“…predicted the presence of PCa and HGPCa by applying clinical factors (age, PSA, fPSA, PV, and TRUS) with or without MRI outcomes. The AUC values for the prediction of PCa with or without MRI were 0.875 and 0.841, respectively, while those for the prediction of HGPCa were 0.872 and 0.850, respectively ( 23 ). In a study by Li et al., patients with benign lesions and GS = 6 were grouped into clinically insignificant PCa.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

et al. 2021

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ObjectivesTo evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).MethodsA total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.ResultsThe multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.ConclusionsClinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

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“…In this study cohort, the prevalence for G ≥ 2 PCa was 38% and was a little lower than in the previous studies, which may contribute to the higher net benefit at the same risk threshold of ≥ 10%. Fang et al [23] developed an MRI risk prediction model, based on PSA, age, and (abnormal) transrectal ultrasound, incorporating mpMRI (PI-RADS 1-5). The AUC for G ≥ 3 PCa for the developing cohort (n = 894, with a prevalence 24.4%) was 0.87, in comparison to 0.85 (p = 0.001) risk prediction model without mpMRI ( Table 2).…”

Section: In Biopsy-naïve Settingmentioning

confidence: 99%

“…The accuracy of the systematic biopsy is dependent on the number of cores. Most studies reported a transrectal approach with a median of 12 biopsy cores (Table 1) [20,[22][23][24][25][26][27]30]. However, three studies used a transperineal approach with biopsy cores ranging from 24 to 40 [21,28,29].…”

Section: Considerations and Future Perspectivesmentioning

confidence: 99%

“…Due to the predictive value of mpMRI in PCa diagnosis, recently new multivariate risk prediction tools have been constructed, with the inclusion of the mpMRI suspicion score [20][21][22][23][24][25][26][27][28][29]. The purpose of this review is to explore the performance of these new MRI risk models for indicating a biopsy for prostate cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions

Schoots

Roobol

2019

World J Urol

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Background and purpose Individualized risk-adapted algorithms in prostate cancer (PCa) diagnosis using predictive prebiopsy variables in addition to prostate-specific antigen value may result in a considerable reduction of unnecessary systematic biopsies. Multi-parametric magnetic resonance imaging (mpMRI) has emerged as a secondary prediction tool that can further improve the detection of clinically significant prostate cancer (csPCa). This review explores the performance of new MRI risk models for indicating a biopsy for prostate cancer diagnosis. Results and considerations The area under the receiver-operating characteristic curve for detecting csPCa varies between 0.64 and 0.91 in biopsy-naïve men, and between 0.78 and 0.93 in men with a previous negative biopsy. The utility of multivariate risk prediction tools including MRI suspicion scores as an extra input parameter has the potential to avoid a notable number of biopsies and detection of clinically insignificant PCa at a low price of missing some csPCa. The trade-off depends on the risk threshold that is chosen. In biopsy-naïve men a net benefit was obtained at a risk threshold of above 10% for csPCa in most MRI risk prediction models. All constructed MRI risk models used (referral) patient cohorts with high prevalence of csPCa. Using more representative cohorts from daily clinical screening, net benefit may attenuate at lower risk thresholds. Strengths and limitations of these models are discussed. Future directions To assess their wider applicability, in-depth analysis of mpMRI predictive qualities should be further investigated, in combination with required external validation of these models in a multicenter setting with large prospective datasets.

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Could Magnetic Resonance Imaging Help to Identify the Presence of Prostate Cancer Before Initial Biopsy? The Development of Nomogram Predicting the Outcomes of Prostate Biopsy in the Chinese Population

Abstract: Prostate MRI before biopsy could predict the presence of PCa and HGPCa, especially in younger patients. The incorporation of MRI in nomograms could increase predictive accuracy.

Cited by 19 publications

References 30 publications

The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer

The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions

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