Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI

Should systematic prostatic biopsies be discontinued?

INTRODUCTION

The use of systematic biopsies in addition to targeted biopsies is based on multiple studies showing that 15-20% of "clinically significant" cancers are missed on targeted biopsies. Concern about these 'missed' cancers drives many interventions. This includes systematic biopsies in men with negative imaging and in men having targeted biopsies, and drives a preference for total gland treatment in men who may be candidates for partial gland ablation. This article summarizes recent genomic and clinical data indicating that, despite "clinically significant" histology, MRI invisible lesions are genomically and clinically favorable. These studies have demonstrated that the genetic aberrations associated with cancer visibility are the same aberrations that drive cancer invasiveness and metastasis. Thus invisible cancers, even if undiagnosed at baseline, are in most cases indolent and pose little threat to the patient. The implications are that patients should be monitored with imaging rather than systematic biopsy, and subject to repeat targeted biopsy for evidence of MR progression. Patients prefer this strategy. It has many advantages in terms of reduced burden of care, cost, psychological benefits, and less diagnosis of insignificant cancer.

CONCLUSION

It is now appropriate to abandon systematic biopsies in most patients.

Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

Radiotranscriptomics identified new mRNAs and miRNA markers for distinguishing prostate cancer from benign prostatic hyperplasia

The present study investigated ultrasound (US) phenotypes reflecting prostate cancer (PCa)-related genetic mutations. Herein, integration of radiotranscriptomic data, US and contrast-enhanced ultrasound (CEUS) radiomic images, and RNA sequencing was performed with the aim of significantly improving the accuracy of PCa prognosis. We performed radiotranscriptomic analysis of clinical, imaging, and two genomic (mRNA and microRNA expression) datasets from 48 and 22 men with PCa and benign prostatic hyperplasia (BPH), respectively. Twenty-three US texture features and four microvascular perfusion features were associated with various patterns of 52 differentially expressed genes related to PCa (p < 0.05); 17 overexpressed genes were associated with two key texture features. Twelve overexpressed genes were identified using microvascular perfusion features. Furthermore, mRNA and miRNA biomarkers could be used to distinguish between PCa and BPH. Compared with RNA sequencing, B-mode and CEUS features reflected genomic alterations associated with hormone receptor status, angiogenesis, and prognosis in patients with PCa. These findings indicate the potential of US to assess biomarker levels in patients with PCa.

Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL

Purpose

To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL.

Methods

A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified via univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram.

Conclusion

The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.