How should radiologists incorporate non-imaging prostate cancer biomarkers into daily practice?

Enhancing prostate MRI expertise: educational strategies for radiologists

The adoption of multiparametric MRI (mpMRI) and the Prostate Imaging Reporting and Data System has significantly changed prostate cancer diagnosis and management. These advancements, alongside novel biomarkers and updated International Society of Uropathology grade groups, have improved cancer detection and prognostication. Despite this progress, varying levels of expertise in mpMRI among radiologists have resulted in inconsistent assessments, potentially leading to unnecessary procedures and diminished confidence in the modality. This review assesses the educational landscape for prostate MRI, highlighting available resources for radiologists at all professional stages. It emphasizes the need for targeted educational strategies to bridge knowledge gaps and improve patient care outcomes in prostate cancer management.

Environmental Impact of Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Guided Prostate Biopsy

BACKGROUND

Reducing low-value clinical care is an important strategy to mitigate environmental pollution caused by health care.

OBJECTIVE

To estimate the environmental impacts associated with prostate magnetic resonance imaging (MRI) and prostate biopsy.

DESIGN, SETTING, AND PARTICIPANTS

We performed a cradle-to-grave life cycle assessment of prostate biopsy. Data included materials and energy inventory, patient and staff travel contributed by prostate MRI, transrectal ultrasound guided prostate biopsy, and pathology analysis. We compared environmental emissions across five clinical scenarios: multiparametric MRI (mpMRI) of the prostate with targeted and systematic biopsies (baseline), mpMRI with targeted biopsy cores only, systematic biopsy without MRI, mpMRI with systematic biopsy, and biparametric MRI (bpMRI) with targeted and systematic biopsies. We estimated the environmental impacts associated with reducing the overall number and varying the approach of a prostate biopsy by using MRI as a triage strategy or by omitting MRI. The study involved academic medical centers in the USA, outpatient urology clinics, health care facilities, medical staff, and patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Greenhouse gas emissions (CO₂ equivalents, CO₂e), and equivalents of coal and gasoline burned were measured.

RESULTS AND LIMITATIONS

In the USA, a single transrectal prostate biopsy procedure including prostate MRI, and targeted and systematic biopsies emits an estimated 80.7 kg CO₂e. An approach of MRI targeted cores alone without a systematic biopsy generated 76.2 kg CO₂e, a systematic 12-core biopsy without mpMRI generated 36.2 kg CO₂e, and bpMRI with targeted and systematic biopsies generated 70.5 kg CO₂e; mpMRI alone contributed 42.7 kg CO₂e (54.3% of baseline scenario). Energy was the largest contributor, with an estimated 38.1 kg CO₂e, followed by staff travel (20.7 kg CO₂e) and supply production (11.4 kg CO₂e). Performing 100 000 fewer unnecessary biopsies would avoid 8.1 million kg CO₂e, the equivalent of 4.1 million liters of gasoline consumed. Per 100 000 patients, the use of prostate MRI to triage prostate biopsy and guide targeted biopsy cores would save the equivalent of 1.4 million kg of CO₂ emissions, the equivalent of 700 000 l of gasoline consumed. This analysis was limited to prostate MRI and biopsy, and does not account for downstream clinical management.

CONCLUSIONS

A prostate biopsy contributes a calculable environmental footprint. Modifying or reducing the number of biopsies performed through existing evidence-based approaches would decrease health care pollution from the procedure.

PATIENT SUMMARY

We estimated that prostate magnetic resonance imaging (MRI) with a prostate biopsy procedure emits the equivalent of 80.7 kg of carbon dioxide. Performing fewer unnecessary prostate biopsies or using prostate MRI as a tool to decide which patients should have a prostate biopsy would reduce procedural greenhouse gas emissions and health care pollution.

Tumour microenvironment and focal therapy for prostate cancer

PURPOSE OF REVIEW

Although most studies focus on the tumour component of prostate cancer (PCa), increasing attention is being paid to the prostatic tumour microenvironment (TME) and its role in diagnosis, prognosis, and therapy development. Herein, we review the prognostic capability of tumour and nontumour derived biomarkers, the immunomodulatory effects of focal therapy (FT) on TME, and its potential as part of a multidisciplinary approach to PCa treatment.

RECENT FINDINGS

Tumour cells have always been the natural candidates to explore new biomarkers, but recent evidence highlights the prognostic contribution of TME cell markers. TME plays a critical role in PCa progression and tumours may escape from the immune system by establishing a microenvironment that suppresses effective antitumour immunity. It has been demonstrated that FT has an immunomodulatory effect and may elicit an immune response that can either favour or inhibit tumorigenesis. TME shows to be an additional target to enhance oncological control.

SUMMARY

A better understanding of TME has the potential to reliably elucidate PCa heterogeneity and assign a prognostic profile in accordance with prostate tumour foci. The joint contribution of biomarkers derived from both tumour and TME compartments may improve patient selection for FT by accurately stratifying disease aggressivity according to the characteristics of tumour foci. Preclinical studies have suggested that FT may act as a TME modulator, highlighting its promising role in multimodal therapeutic management.

Evaluation of the Predictive Role of Blood-Based Biomarkers in the Context of Suspicious Prostate MRI in Patients Undergoing Prostate Biopsy

The aim of this study was to assess the predictive value of pre-biopsy blood-based markers in patients undergoing a fusion biopsy for suspicious prostate magnetic resonance imaging (MRI). We identified 365 consecutive patients who underwent MRI-targeted and systematic prostate biopsy for an MRI scored Prostate Imaging–Reporting and Data System Version (PI-RADS) ≥ 3. We evaluated the neutrophil/lymphocyte ratio (NLR), derived neutrophil/lymphocyte ratio (dNLR), platelet/lymphocyte ratio (PLR), systemic immune inflammation index (SII), lymphocyte/monocyte ratio (LMR,) de Ritis ratio, modified Glasgow Prognostic Score (mGPS), and prognostic nutrition index (PNI). Uni- and multivariable logistic models were used to analyze the association of the biomarkers with biopsy findings. The clinical benefits of biomarkers implemented in clinical decision-making were assessed using decision curve analysis (DCA). In total, 69% and 58% of patients were diagnosed with any prostate cancer and Gleason Grade (GG) ≥ 2, respectively. On multivariable analysis, only high dNLR (odds ratio (OR) 2.61, 95% confidence interval (CI) 1.23–5.56, p = 0.02) and low PNI (OR 0.48, 95% CI 0.26–0.88, p = 0.02) remained independent predictors for GG ≥ 2. The logistic regression models with biomarkers reached AUCs of 0.824–0.849 for GG ≥ 2. The addition of dNLR and PNI did not enhance the net benefit of a standard clinical model. Finally, we created the nomogram that may help guide biopsy avoidance in patients with suspicious MRI. In patients with PI-RADS ≥ 3 lesions undergoing MRI-targeted and systematic biopsy, a high dNLR and low PNI were associated with unfavorable biopsy outcomes. Pre-biopsy blood-based biomarkers did not, however, significantly improve the discriminatory power and failed to add a clinical benefit beyond standard clinical factors.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

Reliability of Serial Prostate Magnetic Resonance Imaging to Detect Prostate Cancer Progression During Active Surveillance: A Systematic Review and Meta-analysis

CONTEXT

Although magnetic resonance imaging (MRI) is broadly implemented into active surveillance (AS) protocols, data on the reliability of serial MRI in order to help guide follow-up biopsy are inconclusive.

OBJECTIVE

To assess the diagnostic estimates of serial prostate MRI for prostate cancer (PCa) progression during AS.

EVIDENCE ACQUISITION

We systematically searched PubMed, Scopus, and Web of Science databases to select studies analyzing the association between changes on serial prostate MRI and PCa progression during AS. We included studies that provided data for MRI progression, which allowed us to calculate diagnostic estimates. We compared Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) accuracy with institution-specific definitions.

EVIDENCE SYNTHESIS

We included 15 studies with 2240 patients. Six used PRECISE criteria and nine institution-specific definitions of MRI progression. The pooled PCa progression rate, which included histological progression to Gleason grade ≥2, was 27%. The pooled sensitivity and specificity were 0.59 (95% confidence interval [CI] 0.44-0.73) and 0.75 (95% CI 0.66-0.84) respectively. There was significant heterogeneity between included studies. Depending on PCa progression prevalence, the pooled negative predictive value for serial prostate MRI ranged from 0.81 (95% CI 0.73-0.88) to 0.88 (95% CI 0.83-0.93) and the pooled positive predictive value ranged from 0.37 (95% CI 0.24-0.54) to 0.50 (95% CI 0.36-0.66). There were no significant differences in the pooled sensitivity (p = 0.37) and specificity (p = 0.74) of PRECISE and institution-specific schemes.

CONCLUSIONS

Serial MRI still should not be considered a sole factor for excluding PCa progression during AS, and changes on MRI are not accurate enough to indicate PCa progression. There was a nonsignificant trend toward improved diagnostic estimates of PRECISE recommendations. These findings highlight the need to further define the optimal triggers and timing of biopsy during AS, as well as the need for optimizing the quality, interpretation, and reporting of serial prostate MRI.

PATIENT SUMMARY

Our study suggests that serial prostate magnetic resonance imaging (MRI) alone in patients on active surveillance is not accurate enough to reliably rule out or rule in prostate cancer progression. Other clinical factors and biomarkers along with serial MRI are required to safely tailor the intensity of follow-up biopsies.