Active Surveillance of Prostate Cancer Using Multiparametric Magnetic Resonance Imaging: A Review of the Current Role and Future Perspectives

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

Purpose

To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.

Methods

Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.

Results

The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).

Conclusion

A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

Clinical and histological predictive factors of reclassification of prostate cancer patients on active surveillance

INTRODUCTION AND OBJECTIVE

Active surveillance (AS) has been established as a therapeutic strategy in patients with low-risk prostate cancer. Demographic and anatomopathological factors that increase the probability of reclassifying patients have been identified.

MATERIALS AND METHODS

Laboratory and histopathological data were collected from 116 patients included on AS since 2014. Univariate analysis was performed with Chi-square, t-student and Kendall's Tau, multivariate analysis according to logistic regression and Kaplan-Meier curves were calculated.

RESULTS

Of the 116 patients in AS, the median age at diagnosis was 66 years and the median follow-up was 13 months (2-72). Of these, 61 (52.6%) are still on surveillance, while 55 (47.4%) have left the program, mostly due to histological progression (52 patients (45.2%)); radical prostatectomy was performed in 27 (49.1%). Prostate volume (PV)≤60cc and the number of positive cylinders >1 in diagnostic biopsy (P=.05) were associated with higher reclassification rate in univariate analysis (P<.05). Multivariate analysis showed that these two variables significantly correlated with higher reclassification rate (PV 60 cc: OR 4.39, P=.04; >1 positive cylinder at diagnostic biopsy: OR 2.48, P=.03).

CONCLUSIONS

It has been shown that initial ultrasound volume and the number of positive cylinders in the diagnostic biopsy are independent risk factors for reclassification. Initial PSA, laterality of the affected cylinders and PSA density were not predictive factors of progression in our series.

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.

Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk

The tumor microenvironment plays a crucial role in both the development and progression of prostate cancer. Furthermore, identifying protein and gene expression differences between different regions is valuable for treatment development. We applied Digital Spatial Profiling multiplex analysis to formalin-fixed paraffin embedded prostatectomy tissue blocks to investigate protein and transcriptome differences between tumor, tumor-adjacent stroma (TAS), CD45+ tumor, and CD45+ TAS tissue. Differential expression of an immunology/oncology protein panel (n = 58) was measured. OX40L and CTLA4 were expressed at higher levels while 22 other proteins, including CD11c, were expressed at lower levels (FDR < 0.2 and p-value < 0.05) in TAS as compared to tumor epithelia. A tissue microarray analysis of 97 patients with 1547 cores found positive correlations between high expression of CD11c and increased time to recurrence in tumor and TAS, and inverse relationships for CTLA4 and OX40L, where higher expression in tumor correlated with lower time to recurrence, but higher time to recurrence in TAS. Spatial transcriptomic analysis using a Cancer Transcriptome Atlas panel (n = 1825 genes) identified 162 genes downregulated and 69 upregulated in TAS versus tumor, 26 downregulated and 6 upregulated in CD45+ TAS versus CD45+ tumor. We utilized CIBERSORTx to estimate the relative immune cell fractions using CD45+ gene expression and found higher average fractions for memory B, naïve B, and T cells in TAS. In summary, the combination of protein expression differences, immune cell fractions, and correlations of protein expression with time to recurrence suggest that closely examining the tumor microenvironment provides valuable data that can improve prognostication and treatment techniques.

AI-assisted biparametric MRI surveillance of prostate cancer: feasibility study

OBJECTIVES

To evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa).

METHODS

This retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS ≥ 2 lesions across prior and current studies. The heatmaps for each patient's prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores.

RESULTS

The model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81).

CONCLUSIONS

Our proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy.

KEY POINTS

• Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach. • The diagnostic accuracy of our csPCa detection AI model improved by including clinical parameters.

The role of MRI in prostate cancer: current and future directions

There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.

Transrectal Ultrasound in Prostate Cancer: Current Utilization, Integration with mpMRI, HIFU and Other Emerging Applications

Transrectal ultrasound (TRUS) has been an invaluable tool in the assessment of prostate size, anatomy and aiding in prostate cancer (PCa) diagnosis for decades. Emerging techniques warrant an investigation into the efficacy of TRUS, how it compares to new techniques, and options to increase the accuracy of prostate cancer diagnosis. Currently, TRUS is used to guide both transrectal and transperineal biopsy approaches with similar cancer detection rates, but lower rates of infection have been reported with the transperineal approach, while lower rates of urinary retention are often reported with the transrectal approach. Multiparametric MRI has substantial benefits for prostate cancer diagnosis and triage such as lesion location, grading, and can be combined with TRUS to perform fusion biopsies targeting specific lesions. Micro-ultrasound generates higher resolution images that traditional ultrasound and has been shown effective at diagnosing PCa, giving it the potential to become a future standard of care. Finally, high-intensity focused ultrasound focal therapy administered via TRUS has been shown to offer safe and effective short-term oncological control for localized disease with low morbidity, and the precise nature makes it a viable option for salvage and repeat therapy.

Evaluation of multiparametric prostate magnetic resonance imaging findings in patients with a Gleason score of 6 in transrectal ultrasonography-guided biopsy

PURPOSE

We aimed to evaluate prostate multiparametric magnetic resonance imaging (mpMRI) findings of patients with a Gleason score (GS) of 6 and effectiveness of MRI based on the final pathology result in patients undergoing radical prostatectomy (RP).

MATERIAL AND METHODS

mpMRI findings of 80 patients who had a GS of 3 + 3 and who underwent mpMRI were evaluated retrospectively. The mpMRI were scored according to the PIRADS v2.1 guidelines. The patients were divided into those with a high probability of clinically significant cancer (CSC) (PI-RADS 4-5) and those with a low probability of CSC (PI-RADS 2-3).

RESULTS

Of the 80 patients, 33.8% had PI-RADS 2-3, and 66.2% had PI-RADS 4-5 lesions. There was a significant difference between the groups in prostate specific antigen (PSA) value, PSA density, patient age, and tumour percentage on biopsy. When the pathology results were taken as the gold standard in the group that underwent RP, sensitivity, specificity, and accuracy of mpMRI were calculated as 94.74%, 100%, and 96.3%, respectively, an increase in the final GS was found in 9 (33.3%) of the 27 patients, and 70.35% of patients were identified as having CSC.

CONCLUSIONS

PI-RADS 4-5 scores have high sensitivity and negative predictive value in the diagnosis of CSC. mpMRI is a reliable and non-invasive diagnostic method that can complement biopsy results in decision-making in patients who are initially evaluated as low risk.

The Impact of Visible Tumor (PI-RADS ≥ 3) on Upgrading and Adverse Pathology at Radical Prostatectomy in Low Risk Prostate Cancer Patients: A Biopsy Core Based Analysis

BACKGROUND

The objective of this study was to investigate the impact of the characteristics of a single visible tumor (Prostate Imaging-Reporting and Data System [PI-RADS]≥3) on upgrading and adverse pathology at radical prostatectomy (RP) in biopsy naïve low risk prostate cancer (PCa) patients.

MATERIALS AND METHODS

We retrospectively reviewed 64 biopsy naïve patients from 3 different referral centers between 2018 and 2020 with a PSA<10, cT1c disease, a single PI-RADS≥ 3 index lesion in multiparametric-MRI (mp-MRI), all bearing a GG 1 tumor sampled software fusion biopsy, who underwent RP. Preoperative clinical variables including the localization, number and tumor burden of positive cores for each PI-RADS category were related to upgrading and adverse pathology (GG>2 and/or pT3 and/or lymph node positive disease) at RP.

RESULTS

Overall 37 patients (57.8%) were upgraded with a significant difference of upgrading in PI-RADS3 (30.0%) versus PI-RADS 4 (67.6%) (P = .007) and PI-RADS 4-5 (70.5%) lesions (P = .002). Thirty-three of 37 GG1 tumors were upgraded to GG2, while 6 of these 33 (18.2%) had adverse pathology as well. Overall 9 patients (14.1%) had adverse pathology at RP all harboring PI-RADS4-5 lesions. The number of positive cores differed significantly between the upgraded and nonupgraded patients. Adverse pathology group had significantly higher tumor volume at RP.

CONCLUSION

PI-RADS4-5 lesions are the independent predictors of upgrading and adverse pathology in low risk PCa with visible tumors. Upgrading and adverse pathology were closely related to the number of positive combined cores reflecting the role of tumor volume. This should be kept in mind in shared decision making of an individual patient with low risk disease and a visible tumor.

Atypical small acinar proliferation and its significance in pathological reports in modern urological times

Atypical small acinar proliferation is a histopathological diagnosis of unspecified importance in prostate needle-biopsy reports, suggestive but not definitive for cancer. The terminology corresponds to some uncertainty in the biopsy report, as the finding might represent an underlying non-cancerous pathology mimicking cancer or an under-sampled prostate cancer site. Therefore, traditional practice favors an immediate repeat biopsy. However, in modern urological times, the need of urgent repeat biopsy is being challenged by some authors as in the majority of cases, the grade of cancer found in subsequent biopsy is reported to be low or the disease to be non-significant. On the other hand, high risk disease cannot be excluded, whereas no clinical or pathological factors can predict the final outcome. In this review, we discuss the significance of the diagnosis of atypical small acinar proliferation in the biopsy report, commenting on its importance in modern urological practice.

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Background

Two diagnostic models of prostate cancer (PCa) and clinically significant prostate cancer (CS-PCa) were established using clinical data of among patients whose prostate-specific antigen (PSA) levels are in the gray area (4.0–10.0 ng/ml).

Material/Methods

Data from 181 patients whose PSA levels were in the gray area were retrospectively analyzed, and the following data were collected: age, digital rectal examination, total PSA, PSA density (PSAD), free/total PSA (f/t PSA), transrectal ultrasound, multiparametric magnetic resonance imaging (mpMRI), and pathological reports. Patients were diagnosed with benign prostatic hyperplasia (BPH) and PCa by pathology reports, and PCa patients were separated into non-clinically significant PCa (NCS-PCa) and CS-PCa by Gleason score. Afterward, predictor models constructed by above parameters were researched to diagnose PCa and CS-PCa, respectively.

Results

According to the analysis of included clinical data, there were 109 patients with BPH, 44 patients with NCS-PCa, and 28 patients with CS-PCa. Regression analysis showed PCa was correlated with f/t PSA, PSAD, and mpMRI (P<0.01), and CS-PCa was correlated with PSAD and mpMRI (P<0.01). The area under the receiver operating characteristic curves of 2 models for PCa (sensitivity=73.64%, specificity=64.23%) and for CS-PCa (sensitivity=71.41%, specificity=81.82%) were 0.79 and 0.87, respectively.

Conclusions

The prediction models had satisfactory diagnostic value for PCa and CS-PCa among patients with PSA in the gray area, and use of these models may help reduce overdiagnosis.

Prostate cancer biomarkers and multiparametric MRI: is there a role for both in prostate cancer management?

Several advancements have been made in recent years with regards to the detection and evaluation of prostate cancer (PCa). The low specificity of prostate specific antigen (PSA) has left much to be desired in a test, but a boom in novel biomarkers has made screening and surveillance more complicated. Several attempts at identifying a niche for these tests has helped somewhat, but much is still undetermined about the benefit that each test provides. In addition to laboratory tests, advancements in multiparametric magnetic resonance imaging (mpMRI) and PIRADSv.2 scoring have provided significant benefit to the evaluation of PCa. With the widespread use of prostate imaging, it is important to re-evaluate the impact of novel biomarkers in the context of furthering PCa screening and management. In this review, we aim to assess the influence mpMRI has on the role of nine different novel biomarkers in the detection and evaluation of PCa. We performed a review of current peer-reviewed literature to assess this question. Much data has been published on the role of these tests, allowing for their placement into one of three best-fit categories: tests for biopsy-naïve men (Prostate Health Index, Mi Prostate Score, 4K Score); tests for men with prior negative biopsies (ConfirmMDx, Progensa PCA3); and men on active surveillance (OncotypeDx, Prolaris, Decipher). Data on the role of these tests with the use of mpMRI have not been comprehensive and excludes several of the markers. More research is needed to determine the combined impact mpMRI and the novel biomarkers on the evaluation and management of PCa.

Superiority of an Asymmetric Perylene Diimide in Terms of Hydrosolubility, G-Quadruplex Binding, Cellular Uptake, and Telomerase Inhibition in Prostate Cancer Cells

Perylene diimide (PDI) derivatives have been studied as G-quadruplex ligands that suppress telomerase activity by facilitating G-quadruplex formation of telomeric DNA and the hTERT promoter. PIPER, the prototypical PDI, reduces telomerase activity in lung and prostate cancer cells, leading to telomere shortening and cellular senescence of these cells. However, PIPER suffers from poor hydrosolubility and the propensity to aggregate at neutral pH. In this report, we synthesized a new asymmetric PDI, aPDI-PHis, which maintains one N-ethyl piperidine side chain of PIPER and has histidine as another side chain. The results show that aPDI-PHis is superior to its symmetric counterparts, PIPER and PDI-His, in terms of hydrosolubility, G-quadruplex binding, cellular uptake, and telomerase inhibition in prostate cancer cells. These results suggest that one N-ethyl piperidine side chain of PDI is sufficient for G-quadruplex binding, while another side chain can be tuned to elicit desirable properties. These findings might lead to better PDIs for use as anticancer drugs.