A prospective study of the prostate health index density and multiparametric magnetic resonance imaging in diagnosing clinically significant prostate cancer

Chen, Yuanchong; Xu, Dong; Ruan, Mingjian; Li, H.Z.; Lin, Guiting; Gang, Song Woon

doi:10.4111/icu.20230060

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…With the development of medical technology, especially the application of multi-parametric magnetic resonance imaging (Multi-parametric Magnetic Resonance Imaging, mpMRI), a new perspective has been provided for the early screening and accurate diagnosis of prostate cancer [3]. mpMRI, by combining different imaging sequences, can provide more comprehensive tumor information, thereby signi cantly improving diagnostic accuracy [4].…”

Section: Introductionmentioning

confidence: 99%

The application of the nomogram model based on ADC histogram features in predicting clinically significant prostate cancer

Gao,

Zhang

2024

Preprint

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Objectives This study aimed to develop a nomogram model using ADC histogram features to predict clinically significant prostate cancer (CSPCa).Methods A retrospective analysis was conducted on 283 patients with suspected prostate cancer admitted to the Urology Department of Jiangnan University Affiliated Central Hospital from January 2019 to June 2024. Patients were randomly divided into a training set (70%, 198 cases) and an internal validation set (30%, 85 cases). Key features were selected through univariate analysis and LASSO regression, and a predictive model was further constructed using univariate and multivariate Logistic regression analysis. The validity of the model was assessed through ROC curves, calibration curves, and decision curve analysis.Results The study found that ADC_CoeffOfVar (odds ratio OR = 1.01, P = 0.034) and ADC_entropy (OR = 1.00, P < 0.001) are independent predictors for CSPCa. The nomogram model constructed based on these factors showed good predictive performance in both the training set (AUC = 0.844) and the internal validation set (AUC = 0.765). Calibration curve analysis showed that the model's predictions were highly consistent with actual observations, and decision curve analysis (DCA) further confirmed the net clinical benefit of the model in clinical decision-making.Conclusion The nomogram model constructed based on ADC histogram features not only provides a non-invasive tool for preoperative risk assessment but also has potential for practical clinical application.

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Section: Introductionmentioning

confidence: 99%

The application of the nomogram model based on ADC histogram features in predicting clinically significant prostate cancer

Gao,

Zhang

2024

Preprint

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Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer

Li,

Zha,

Wang

et al. 2024

Front. Surg.

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Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

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Methylated DNA Markers in Voided Urine for the Identification of Clinically Significant Prostate Cancer

Shah,

Taylor,

Negaard

et al. 2024

Life

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Introduction: Non-invasive assays are needed to better discriminate patients with prostate cancer (PCa) to avoid over-treatment of indolent disease. We analyzed 14 methylated DNA markers (MDMs) from urine samples of patients with biopsy-proven PCa relative to healthy controls and further studied discrimination of clinically significant PCa (csPCa) from healthy controls and Gleason 6 cancers. Methods: To evaluate the panel, urine from 24 healthy male volunteers with no clinical suspicion for PCa and 24 men with biopsy-confirmed disease across all Gleason scores was collected. Blinded to clinical status, DNA from the supernatant was analyzed for methylation signal within specific DNA sequences across 14 genes (HES5, ZNF655, ITPRIPL1, MAX.chr3.6187, SLCO3A1, CHST11, SERPINB9, WNT3A, KCNB2, GAS6, AKR1B1, MAX.chr3.8028, GRASP, ST6GALNAC2) by target enrichment long-probe quantitative-amplified signal assays. Results: Utilizing an overall specificity cut-off of 100% for discriminating normal controls from PCa cases across the MDM panel resulted in 71% sensitivity (95% CI: 49–87%) for PCa detection (4/7 Gleason 6, 8/12 Gleason 7, 5/5 Gleason 8+) and 76% (50–92%) for csPCa (Gleason ≥ 7). At 100% specificity for controls and Gleason 6 patients combined, MDM panel sensitivity was 59% (33–81%) for csPCa (5/12 Gleason 7, 5/5 Gleason 8+). Conclusions: MDMs assayed in urine offer high sensitivity and specificity for detection of clinically significant prostate cancer. Prospective evaluation is necessary to estimate discrimination of patients as first-line screening and as an adjunct to prostate-specific antigen (PSA) testing.

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A prospective study of the prostate health index density and multiparametric magnetic resonance imaging in diagnosing clinically significant prostate cancer

Cited by 3 publications

References 28 publications

The application of the nomogram model based on ADC histogram features in predicting clinically significant prostate cancer

The application of the nomogram model based on ADC histogram features in predicting clinically significant prostate cancer

Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer

Methylated DNA Markers in Voided Urine for the Identification of Clinically Significant Prostate Cancer

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