Multiparametric MRI of the prostate with three functional techniques in patients with PSA elevation before initial TRUS-guided biopsy

Clinical Validation of Multiparametric Ultrasound for Detecting Clinically Significant Prostate Cancer Using Computer-Aided Diagnosis: A Direct Comparison with the Magnetic Resonance Imaging Pathway

We present the protocol for a study testing the hypothesis that a computer-aided diagnosis (CAD) system for three-dimensional multiparametric ultrasound (3D mpUS) is noninferior to magnetic resonance imaging (MRI) in guiding prostate biopsies for detection of clinically significant prostate cancer (csPCa). The prospective study has a fully paired design for assessment of diagnostic accuracy and is registered on ClinicalTrials.gov as NCT06281769. A total of 438 biopsy-naïve men scheduled for prostate MRI evaluation because of an abnormal digital rectal examination and/or elevated serum prostate-specific antigen will be included. All patients will undergo both MRI (multiparametric or biparametric) and 3D mpUS with CAD (PCaVision). Suspicious lesions will be independently identified using each imaging technique. MRI targeted biopsy (TBx) and/or PCaVision TBx will be performed if suspicious lesions are identified on imaging. When both PCaVision and MRI identify lesions in an individual patient, the TBx order for this patient will be randomized. Three TBx samples per lesion will be taken for a maximum of two lesions per modality. The primary objective is the detection rate for csPCa (International Society of Urological Pathology grade group [GG] ≥2) with the PCaVision versus the MRI TBx pathway. The noninferiority margin for the absolute difference in detection rates is set at a difference of 5%. Secondary outcomes are the proportion of men in whom TBx could have been safely omitted in each pathway. Additional diagnostic accuracy analyses will be performed for different definitions of PCa (GG ≥3; GG ≥2 with cribriform growth and/or intraductal carcinoma; and GG 1). The frequency of insufficient image quality for the two pathways will also be assessed. Lastly, we will determine the diagnostic performance for csPCa detection at various 3D mpUS image quality thresholds for PCaVision.

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.

Detection of prostate cancer using prostate imaging reporting and data system score and prostate-specific antigen density in biopsy-naive and prior biopsy-negative patients

Background

Few studies report on indications for prostate biopsy using Prostate Imaging–Reporting and Data System (PI-RADS) score and prostate-specific antigen density (PSAD). No study to date has included biopsy-naïve and prior biopsy-negative patients. Therefore, we evaluated the predictive values of the PI-RADS, version 2 (v2) score combined with PSAD to decrease unnecessary biopsies in biopsy-naïve and prior biopsy-negative patients.

Materials and methods

A total of 1,098 patients who underwent multiparametric magnetic resonance imaging at our hospital before a prostate biopsy and who underwent their second prostate biopsy with an initial benign negative prostatic biopsy were included. We found factors associated with clinically significant prostate cancer (csPca). We assessed negative predictive values by stratifying biopsy outcomes by prior biopsy history and PI-RADS score combined with PSAD.

Results

The median age was 65 years (interquartile range: 59-70), and the median PSA was 5.1 ng/mL (interquartile range: 3.8-7.1). Multivariate logistic regression analysis revealed that age, prostate volume, PSAD, and PI-RADS score were independent predictors of csPca. In a biopsy-naïve group, 4% with PI-RADS score 1 or 2 had csPca; in a prior biopsy-negative group, 3% with PI-RADS score 1 or 2 had csPca. The csPca detection rate was 2.0% for PSA density <0.15 ng/mL/mL and 4.0% for PSA density 0.15-0.3 ng/mL/mL among patients with PI-RADS score 3 in a biopsy-naïve group. The csPca detection rate was 1.8% for PSA density <0.15 ng/mL/mL and 0.15-0.3 ng/mL/mL among patients with PI-RADS score 3 in a prior biopsy-negative group.

Conclusion

Patients with PI-RADS v2 score ≤2, regardless of PSA density, may avoid unnecessary biopsy. Patients with PI-RADS score 3 may avoid unnecessary biopsy through PSA density results.

Accuracy of multiparametric magnetic resonance imaging for diagnosing prostate Cancer: a systematic review and meta-analysis

BACKGROUND

The application of multiparametric magnetic resonance imaging (mpMRI) for diagnosis of prostate cancer has been recommended by the European Association of Urology (EAU), National Comprehensive Cancer Network (NCCN), and European Society of Urogenital Radiology (ESUR) guidelines. The purpose of this study is to systematically review the literature on assessing the accuracy of mpMRI in patients with suspicion of prostate cancer.

METHOD

We searched Embase, Pubmed and Cochrane online databases from January 12,000 to October 272,018 to extract articles exploring the possibilities that the pre-biopsy mpMRI can enhance the diagnosis accuracy of prostate cancer. The numbers of true- and false-negative results and true- and false-positive ones were extracted to calculate the corresponding sensitivity and specificity of mpMRI. Study quality was assessed using QUADAS-2 tool. Random effects meta-analysis and a hierarchical summary receiver operating characteristic (HSROC) plot were performed for further study.

RESULTS

After searching, we acquired 3741 articles for reference, of which 29 studies with 8503 participants were eligible for inclusion. MpMRI maintained impressive diagnostic value, the area under the HSROC curve was 0.87 (95%CI,0.84-0.90). The sensitivity and specificity for mpMRI were 0.87 [95%CI, 0.81-0.91] and 0.68 [95%CI,0.56-0.79] respectively. The positive likelihood ratio was 2.73 [95%CI 1.90-3.90]; negative likelihood ratio was 0.19 [95% CI 0.14,-0.27]. The risk of publication bias was negligible with P = 0.96.

CONCLUSION

Results of the meta-analysis suggest that mpMRI is a sensitive tool to diagnose prostate cancer. However, because of the high heterogeneity existing among the included studies, further studies are needed to apply the results of this meta-analysis in clinic.

Diagnostic value of semi-quantitative and quantitative analysis of functional parameters in multiparametric MRI of the prostate

OBJECTIVE

To determine the diagnostic value of semi-quantitative and quantitative parameters of three functional techniques in multiparametric (mp)-MRI of the prostate.

METHODS

Mp-MRI was performed in 110 patients with suspicion of prostate cancer (PCA) before transrectal ultrasound (TRUS)-guided core biopsy. Peak-enhancement, initial and post-initial enhancement, initial area under gadolinium curve, Ktrans (forward rate constant), Kep (efflux rate constant), Ve (extracellular volume), ADC (apparent diffusion coefficient) and MR spectroscopy ratio were obtained for malignant and benign lesions. For iAUGC, Ktrans, Kep and Ve we evaluated median, mean and the difference (Diff) between mean and median. For ADC we evaluated mean, median, Diff between mean and median, and min. In addition, we evaluated these parameters in dependence of Gleason score in PCA. Receiver operating characteristic analysis and area under curve (AUC) were determined.

RESULTS

ADC min and Kep Diff were the best predictors of malignancy in all lesions (AUC: 0.765). ADC min was the best predictor of malignancy for lesions in peripheral zone (PZ) (AUC: 0.7506) and Kep Diff was the best predictor of malignancy for lesions in transitional zone (AUC: 0.7514). Peak enhancement was the best parameter in differentiation of low-grade PCA with Gleason score 6 from high-grade PCA with Gleason score ≥ 7 (AUC: 0.7692).

CONCLUSION

ADC min can differentiate PCA from benign prostate lesions in PZ. Kep Diff could possibly improve prostate cancer detection in. Peak enhancement might be able to differentiate low grade from high-grade PCA. Semi-quantitative and quantitative parameters may be useful for the functional techniques in mp-MRI. Advances in knowledge: ADC min can differentiate PCA from benign prostate lesions in PZ. Peak enhancement might be able to differentiate low grade from high-grade PCA.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.

What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel

CONTEXT

It remains unclear whether patients with a suspicion of prostate cancer (PCa) and negative multiparametric magnetic resonance imaging (mpMRI) can safely obviate prostate biopsy.

OBJECTIVE

To systematically review the literature assessing the negative predictive value (NPV) of mpMRI in patients with a suspicion of PCa.

EVIDENCE ACQUISITION

The Embase, Medline, and Cochrane databases were searched up to February 2016. Studies reporting prebiopsy mpMRI results using transrectal or transperineal biopsy as a reference standard were included. We further selected for meta-analysis studies with at least 10-core biopsies as the reference standard, mpMRI comprising at least T2-weighted and diffusion-weighted imaging, positive mpMRI defined as a Prostate Imaging Reporting Data System/Likert score of ≥3/5 or ≥4/5, and results reported at patient level for the detection of overall PCa or clinically significant PCa (csPCa) defined as Gleason ≥7 cancer.

EVIDENCE SYNTHESIS

A total of 48 studies (9613 patients) were eligible for inclusion. At patient level, the median prevalence was 50.4% (interquartile range [IQR], 36.4-57.7%) for overall cancer and 32.9% (IQR, 28.1-37.2%) for csPCa. The median mpMRI NPV was 82.4% (IQR, 69.0-92.4%) for overall cancer and 88.1% (IQR, 85.7-92.3) for csPCa. NPV significantly decreased when cancer prevalence increased, for overall cancer (r=-0.64, p<0.0001) and csPCa (r=-0.75, p=0.032). Eight studies fulfilled the inclusion criteria for meta-analysis. Seven reported results for overall PCa. When the overall PCa prevalence increased from 30% to 60%, the combined NPV estimates decreased from 88% (95% confidence interval [95% CI], 77-99%) to 67% (95% CI, 56-79%) for a cut-off score of 3/5. Only one study selected for meta-analysis reported results for Gleason ≥7 cancers, with a positive biopsy rate of 29.3%. The corresponding NPV for a cut-off score of ≥3/5 was 87.9%.

CONCLUSIONS

The NPV of mpMRI varied greatly depending on study design, cancer prevalence, and definitions of positive mpMRI and csPCa. As cancer prevalence was highly variable among series, risk stratification of patients should be the initial step before considering prebiopsy mpMRI and defining those in whom biopsy may be omitted when the mpMRI is negative.

PATIENT SUMMARY

This systematic review examined if multiparametric magnetic resonance imaging (MRI) scan can be used to reliably predict the absence of prostate cancer in patients suspected of having prostate cancer, thereby avoiding a prostate biopsy. The results suggest that whilst it is a promising tool, it is not accurate enough to replace prostate biopsy in such patients, mainly because its accuracy is variable and influenced by the prostate cancer risk. However, its performance can be enhanced if there were more accurate ways of determining the risk of having prostate cancer. When such tools are available, it should be possible to use an MRI scan to avoid biopsy in patients at a low risk of prostate cancer.

Evaluating the Role of mpMRI in Prostate Cancer Assessment

Prostate cancer is the most common malignancy among American men. The role of multi-parametric MRI has recently gained more importance in detection of prostate cancer, its targeted biopsy, and focal therapy guidance. In this review, uses of multi-parametric MRI in prostate cancer assessment and treatment are discussed.