The future direction of imaging in prostate cancer: MRI with or without contrast injection

What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning

BACKGROUND

Radical prostatectomy (RP) is recommended in case of localized or locally advanced prostate cancer (PCa), but it can lead to side effects, including urinary incontinence (UI) and erectile dysfunction (ED). Magnetic resonance imaging (MRI) is recommended for PCa diagnosis and staging, but it can also improve preoperative risk-stratification.

PURPOSE

This nonsystematic review aims to provide an overview on factors involved in RP side effects, highlighting anatomical and pathological aspects that could be included in a structured report.

EVIDENCE SYNTHESIS

Considering UI evaluation, MR can investigate membranous urethra length (MUL), prostate volume, the urethral sphincter complex, and the presence of prostate median lobe. Longer MUL measurement based on MRI is linked to a higher likelihood of achieving continence restoration. For ED assessment, MRI and diffusion tensor imaging identify the neurovascular bundle and they can aid in surgery planning. Finally, MRI can precisely describe extra-prostatic extension, prostate apex characteristics and lymph-node involvement, providing valuable preoperative information for PCa treatment.

CONCLUSIONS

Anatomical principals structures involved in RP side effects can be assessed with MR. A standardized MR report detailing these structures could assist urologists in planning optimal and tailored surgical techniques, reducing complications, and improving patients' care.

Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL: a systematic review and meta-analysis

OBJECTIVE

To investigate the diagnostic performance of MRI in detecting clinically significant prostate cancer (csPCa) and prostate cancer (PCa) in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL.

METHODS

A computerized search of PubMed, Embase, Cochrane Library, Medline, and Web of Science was conducted from inception until October 31, 2023. We included articles on the use of MRI to detect csPCa or PCa at 4-10 ng/mL PSA. The primary and secondary outcomes were MRI performance in csPCa and PCa detection, respectively; the estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were pooled in a bivariate random-effects model.

RESULTS

Among the 19 studies (3879 patients), there were 10 (2205 patients) and 13 studies (2965 patients) that reported MRI for detecting csPCa or PCa, respectively. The pooled sensitivity and specificity for csPCa detection were 0.84 (95% confidence interval [CI], 0.79-0.88) and 0.76 (95%CI, 0.65-0.84), respectively, for PCa detection were 0.82 (95%CI, 0.75-0.87) and 0.74 (95%CI, 0.65-0.82), respectively. The pooled NPV for csPCa detection was 0.91 (0.87-0.93). Biparametric magnetic resonance imaging also showed a significantly higher sensitivity and specificity relative to multiparametric magnetic resonance imaging (both p < 0.01).

CONCLUSION

Prostate MRI enables the detection of csPCa and PCa with satisfactory performance in the PSA gray zone. The excellent NPV for csPCa detection indicates the possibility of biopsy decision-making in patients in the PSA gray zone, but substantial heterogeneity among the included studies should be taken into account.

CLINICAL RELEVANCE STATEMENT

Prostate MRI can be considered a reliable and satisfactory tool for detecting csPCa and PCa in patients with PSA in the "gray zone", allowing for reducing unnecessary biopsy and optimizing the overall examination process.

KEY POINTS

Prostate-specific antigen (PSA) is a common screening tool for prostate cancer but risks overdiagnosis. MRI demonstrated excellent negative predictive value for prostate cancer in the PSA gray zone. MRI can influence decision-making for these patients, and biparametric MRI should be further evaluated.

Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents

Gadolinium-based contrast agents (GBCA) are essential for diagnostic MRI examinations. GBCA are only used in small quantities on a per-patient basis; however, the acquisition of contrast-enhanced MRI examinations worldwide results in the use of many thousands of litres of GBCA per year. Data shows that these GBCA are present in sewage water, surface water, and drinking water in many regions of the world. Therefore, there is growing concern regarding the environmental impact of GBCA because of their ubiquitous presence in the aquatic environment. To address the problem of GBCA in the water system as a whole, collaboration is necessary between all stakeholders, including the producers of GBCA, medical professionals and importantly, the consumers of drinking water, i.e. the patients. This paper aims to make healthcare professionals aware of the opportunity to take the lead in making informed decisions about the use of GBCA and provides an overview of the different options for action.In this paper, we first provide a summary on the metabolism and clinical use of GBCA, then the environmental fate and observations of GBCA, followed by measures to reduce the use of GBCA. The environmental impact of GBCA can be reduced by (1) measures focusing on the application of GBCA by means of weight-based contrast volume reduction, GBCA with higher relaxivity per mmol of Gd, contrast-enhancing sequences, and post-processing; and (2) measures that reduce the waste of GBCA, including the use of bulk packaging and collecting residues of GBCA at the point of application.Critical relevance statement This review aims to make healthcare professionals aware of the environmental impact of GBCA and the opportunity for them to take the lead in making informed decisions about GBCA use and the different options to reduce its environmental burden.Key points• Gadolinium-based contrast agents are found in sources of drinking water and constitute an environmental risk.• Radiologists have a wide spectrum of options to reduce GBCA use without compromising diagnostic quality.• Radiology can become more sustainable by adopting such measures in clinical practice.

Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions

The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.

Design of a magnetic resonance imaging-based screening program for early diagnosis of prostate cancer: preliminary results of a randomized controlled trial-Prostate Cancer Secondary Screening in Sapienza (PROSA)

OBJECTIVES

The main objective is to propose an MRI-based screening protocol, investigating the role of MRI without the injection of contrast media (bi-parametric MRI, bpMRI) as a secondary prevention test for prostate cancer (PCa) early diagnosis, comparing MRI with the prostate specific antigen (PSA) test. For this reason, preliminary results of Prostate Cancer Secondary Screening in Sapienza (PROSA) are presented, to investigate the efficiency of an MRI-based screening protocol. PROSA is a prospective, randomized, single-center study. To date, 351 men have been enrolled and blindly randomized into two different arms: (A) Men underwent a bpMRI regardless of their PSA values (175); (B) Men followed as per clinical practice: those with increased PSA (61) were referred to bpMRI, while those with normal PSA (112) were not. Men who screened positive on MRI were directed to MR-directed targeted biopsy. On arm A, 4 clinically significant PCa have been detected, while none was found on arm B (p = 0.046). To evaluate the efficiency of the screening protocol, we calculated the experimental event rate (EER, 3.6%), control event rate (CER, 1.2%.), absolute risk reduction (ARR, 2.5%), and number needed to treat (NNT, 40.3). PROSA represents an interesting experience in the field of imaging-based PCa screening. The preliminary data from this trial highlight the promising role of non-contrast MRI as a screening tool for early detection of PCa. Further data will finally validate the most appropriate screening program.

CLINICAL RELEVANCE STATEMENT

PROSA depicts an interesting experience in the field of research focused on imaging-based prostate cancer screening. Its preliminary data highlight the promising role of non-contrast MRI as a screening tool for early detection of PCa.

KEY POINTS

• Promotion of an MRI-based screening protocol, investigating the role of non-contrast MRI as a secondary prevention test for prostate cancer early diagnosis, comparing MRI with PSA test. • Prostate Cancer Secondary Screening in Sapienza (PROSA) represents an interesting experience in the field of research focused on imaging-based prostate cancer screening; its preliminary results indicate that it is possible to use non-contrast MRI as a screening tool for early detection of PCa. • This new approach to PCa screening could facilitate the early diagnosis of clinically significant prostate cancer while reducing the number of unnecessary prostate biopsies and the detection of clinically insignificant prostate cancer.

Digital droplet immunoassay based on a microfluidic chip with magnetic beads for the detection of prostate-specific antigen

Sensitive biomarker detection techniques are beneficial for both disease diagnosis and postoperative examinations. In this study, we report an integrated microfluidic chip designed for the immunodetection of prostate-specific antigens (PSAs). The microfluidic chip is based on the three-dimensional structure of quartz capillaries. The outlet channel extends to 1.8 cm, effectively facilitating the generation of uniform droplets ranging in size from 3 to 50 μm. Furthermore, we successfully immobilized the captured antibodies onto the surface of magnetic beads using an activator, and we constructed an immunosandwich complex by employing biotinylated antibodies. A key feature of this microfluidic chip is its integration of microfluidic droplet technology advantages, such as high-throughput parallelism, enzymatic signal amplification, and small droplet size. This integration results in an exceptionally sensitive PSA detection capability, with the detection limit reduced to 7.00 ± 0.62 pg/mL.

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors

PURPOSE

The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade ≥ 2).

METHODS

This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients.

RESULTS

705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naïve (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG ≥ 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS ≥ 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively.

CONCLUSIONS

Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions.

TRIAL REGISTRATION

The present study was registered at ClinicalTrials.gov number: NCT05078359.

Prostate Region-Wise Imaging Biomarker Profiles for Risk Stratification and Biochemical Recurrence Prediction

BACKGROUND

Identifying prostate cancer (PCa) patients with a worse prognosis and a higher risk of biochemical recurrence (BCR) is essential to guide treatment choices. Here, we aimed to identify possible imaging biomarker (perfusion/diffusion + radiomic features) profiles extracted from MRIs that were able to discriminate patients according to their risk or the occurrence of BCR 10 years after diagnosis, as well as to evaluate their predictive value with or without clinical data.

METHODS

Patients with localized PCa receiving neoadjuvant androgen deprivation therapy and radiotherapy were retrospectively evaluated. Imaging features were extracted from MRIs for each prostate region or for the whole gland. Univariate and multivariate analyses were conducted.

RESULTS

128 patients (mean [range] age, 71 [50-83] years) were included. Prostate region-wise imaging biomarker profiles mainly composed of radiomic features allowed discriminating risk groups and patients experiencing BCR. Heterogeneity-related radiomic features were increased in patients with worse prognosis and with BCR. Overall, imaging biomarkers profiles retained good predictive ability (AUC values superior to 0.725 in most cases), which generally improved when clinical data were included (particularly evident for the prediction of the BCR, with AUC values ranging from 0.841 to 0.877 for combined models and sensitivity values above 0.960) and when models were built per prostate region vs. the whole gland.

CONCLUSIONS

Prostate region-aware imaging profiles enable identification of patients with worse prognosis and with a higher risk of BCR, retaining higher predictive values when combined with clinical variables.

Low cancer yield in PI-RADS 3 upgraded to 4 by dynamic contrast-enhanced MRI: is it time to reconsider scoring categorization?

OBJECTIVES

To evaluate MRI diagnostic performance in detecting clinically significant prostate cancer (csPCa) in peripheral-zone PI-RADS 4 lesions, comparing those with clearly restricted diffusion (DWI-score 4), and those with equivocal diffusion pattern (DWI-score 3) and positive dynamic contrast-enhanced (DCE) MRI.

METHODS

This observational prospective study enrolled 389 men referred to MRI and, if positive (PI-RADS 3 with PSA-density [PSAD] ≥ 0.15 ng/mL/mL, 4 and 5), to MRI-directed biopsy. Lesions with DWI-score 3 and positive DCE were classified as "PI-RADS 3up," instead of PI-RADS 4. Univariable and multivariable analyses were implemented to determine features correlated to csPCa detection.

RESULTS

Prevalence of csPCa was 14.5% and 53.3% in PI-RADS categories 3up and 4, respectively (p < 0.001). MRI showed a sensitivity of 100.0%, specificity 40.9%, PPV 46.5%, NPV 100.0%, and accuracy 60.9% for csPCa detection. Modifying the threshold to consider MRI positive and to indicate biopsy (same as previously described, but PI-RADS 3up only when associated with elevated PSAD), the sensitivity changed to 93.9%, specificity 57.2%, PPV 53.0%, NPV 94.8%, and accuracy 69.7%. Age (p < 0.001), PSAD (p < 0.001), positive DWI (p < 0.001), and PI-RADS score (p = 0.04) resulted in independent predictors of csPCa.

CONCLUSIONS

Most cases of PI-RADS 3up were false-positives, suggesting that upgrading peripheral lesions with DWI-score 3 to PI-RADS 4 because of positive DCE has a detrimental effect on MRI accuracy, decreasing the true prevalence of csPCa in the PI-RADS 4 category. PI-RADS 3up should not be upgraded and directed to biopsy only if associated with increased PSAD.

KEY POINTS

• As per PI-RADS v2.1 recommendations, in case of a peripheral zone lesion with equivocal diffusion-weighted imaging (DWI score 3), but positive dynamic contrast-enhanced (DCE) MRI, the overall PI-RADS score should be upgraded to 4. • The current PI-RADS recommendation of upgrading PI-RADS 3 lesions of the peripheral zone to PI-RADS 4 because of positive DCE decreased clinically significant prostate cancer detection rate in our series. • According to our results, the most accurate threshold for setting indication to prostate biopsy is PI-RADS 3 or PI-RADS 3 with positive DCE both associated with increased PSA density.

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Could Biparametric MRI Replace Multiparametric MRI in the Management of Prostate Cancer?

Prostate cancer (PCa) is a worldwide epidemiological problem, since it is one of the most prevalent types of neoplasia among men, and the third-leading cause of cancer-related deaths, after lung and colorectal tumors. Unfortunately, the early stages of PCa have a wide range of unspecific symptoms. For these reasons, early diagnosis and accurate evaluation of suspicious lesions are crucial. Multiparametric MRI (mpMRI) is currently the imaging modality of choice for diagnostic screening and local staging of PCa, but also has a leading role in guiding biopsies and in treatment biparametric MRI (bpMRI) could partially replace mpMRI due to its lack of adverse reactions caused by contrast agents, relatively lower costs, and shorter acquisition time. Further, 31 relevant articles regarding the advantages and disadvantages of the aforementioned imaging techniques were scanned. As a result, while bpMRI has comparable accuracy in detecting PCa, its roles in the other steps of PCa management are limited.

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models

OBJECTIVES

Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models.

RESULTS

Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%.

DISCUSSION

The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.

MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI

Purpose

To compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI).

Methods

A total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis.

Results

The radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated.

Conclusions

Our study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.