Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis

A comprehensive analysis of minimally differentially methylated regions common to pediatric and adult solid tumors

Cancer is the second most common cause of death in children aged 1-14 years in the United States, with 11,000 new cases and 1200 deaths annually. Pediatric cancers typically have lower mutational burden compared to adult-onset cancers, however, the epigenomes in pediatric cancer are highly altered, with widespread DNA methylation changes. The rarity of pediatric cancers poses a significant challenge to developing cancer-type specific biomarkers for diagnosis, prognosis, or treatment monitoring. In the current study, we explored the potential of a DNA methylation profile common across various pediatric cancers. To do this, we conducted whole genome bisulfite sequencing (WGBS) on 31 recurrent pediatric tumor tissues, 13 normal tissues, and 20 plasma cell-free (cf)DNA samples, representing 11 different pediatric cancer types. We defined minimal focal regions that were differentially methylated across samples in the multiple cancer types which we termed minimally differentially methylated regions (mDMRs). These methylation changes were also observed in 506 pediatric and 5691 adult cancer samples accessed from publicly available databases, and in 44 pediatric cancer samples we analyzed using a targeted hybridization probe capture assay. Finally, we found that these methylation changes were detectable in cfDNA and could serve as potential cfDNA methylation biomarkers for early detection or minimal residual disease.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification

RATIONALE AND OBJECTIVES

Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.

MATERIAL AND METHODS

An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology.

RESULTS

A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth.

CONCLUSION

Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

Histomic and transcriptomic features of MRI-visible and invisible clinically significant prostate cancers are associated with prognosis

Magnetic resonance imaging (MRI) is increasingly used to triage patients for prostate biopsy. However, 9% to 24% of clinically significant (cs) prostate cancers (PCas) are not visible in MRI. We aimed to identify histomic and transcriptomic determinants of MRI visibility and their association to metastasis, and PCa-specific death (PCSD). We studied 45 radical prostatectomy-treated patients with csPCa (grade group [GG]2-3), including 30 with MRI-visible and 15 with MRI-invisible lesions, and 18 men without PCa. First, histological composition was quantified. Next, transcriptomic profiling was performed using NanoString technology. MRI visibility-associated differentially expressed genes (DEGs) and Reactome pathways were identified. MRI visibility was classified using publicly available genes in MSK-IMPACT and Decipher, Oncotype DX, and Prolaris. Finally, DEGs and clinical parameters were used to classify metastasis and PCSD in an external cohort, which included 76 patients with metastatic GG2-4 PCa, and 84 baseline-matched controls without progression. Luminal area was lower in MRI-visible than invisible lesions and low luminal area was associated with short metastasis-free and PCa-specific survival. We identified 67 DEGs, eight of which were associated with survival. Cell division, inflammation and transcriptional regulation pathways were upregulated in MRI-visible csPCas. Genes in Decipher, Oncotype DX and MSK-IMPACT performed well in classifying MRI visibility (AUC = 0.86-0.94). DEGs improved classification of metastasis (AUC = 0.69) and PCSD (AUC = 0.68) over clinical parameters. Our data reveals that MRI-visible csPCas harbor more aggressive histomic and transcriptomic features than MRI-invisible csPCas. Thus, targeted biopsy of visible lesions may be sufficient for risk stratification in patients with a positive MRI.

The Added Value of Side-specific Systematic Biopsy in Patients Diagnosed by Magnetic Resonance Imaging-targeted Prostate Biopsy

BACKGROUND

Systematic biopsy (SB) combined with magnetic resonance imaging (MRI)-targeted biopsy is still recommended considering the risk of missing clinically significant prostate cancer (csPCa).

OBJECTIVE

To evaluate the added value in csPCa detection on side-specific SB relative to MRI lesion and to externally validate the Noujeim risk stratification model that predicts the risk of csPCa on distant SB cores relative to the index MRI lesion.

DESIGN, SETTING, AND PARTICIPANTS

Overall, 4841 consecutive patients diagnosed by MRI-targeted biopsy and SB for Prostate Imaging Reporting and Data System score ≥3 lesions were identified from a prospectively maintained database between January 2016 and April 2023 at 15 European referral centers. A total of 2387 patients met the inclusion criteria and were included in the analysis.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

McNemar's test was used to compare the csPCa detection rate between several biopsy strategies including MRI-targeted biopsy, side-specific SB, and a combination of both. Model performance was evaluated in terms of discrimination using area under the receiver operation characteristic curve (AUC), calibration plots, and decision curve analysis. Clinically significant prostate cancer was defined as International Society of Urological Pathology grade group ≥2.

RESULTS AND LIMITATIONS

Overall, the csPCa detection rate was 49%. Considering MRI-targeted biopsy as reference, the added values in terms of csPCa detection were 5.8% (relative increase of 13%), 4.2% (relative increase of 9.8%), and 2.8% (relative increase of 6.1%) for SB, ipsilateral SB, and contralateral SB, respectively. Only 35 patients (1.5%) exclusively had csPCa on contralateral SB (p < 0.001). Considering patients with csPCa on MRI-targeted biopsy and ipsilateral SB, the upgrading rate was 2% (20/961) using contralateral SB (p < 0.001). The Noujeim model exhibited modest performance (AUC of 0.63) when tested using our validation set.

CONCLUSIONS

The added value of contralateral SB was negligible in terms of cancer detection and upgrading rates. The Noujeim model could be included in the decision-making process regarding the appropriate prostate biopsy strategy.

PATIENT SUMMARY

In the present study, we collected a set of patients who underwent magnetic resonance imaging (MRI)-targeted and systematic biopsies for the detection of prostate cancer. We found that biopsies taken at the opposite side of the MRI suspicious lesion have a negligible impact on cancer detection. We also validate a risk stratification model that predicts the risk of cancer on biopsies beyond 10 mm from the initial lesion, which could be used in daily practice to improve the personalization of the prostate biopsy.

68Ga-prostate specific membrane antigen-11 PET/CT versus multiparametric MRI in the detection of primary prostate cancer: A systematic review and head-to-head comparative meta-analysis

PURPOSE

The goal of this study was to evaluate the effectiveness of two diagnostic methods, 68Ga-PSMA-11 PET/CT and mpMRI, in detecting primary prostate cancer without limitations on the Gleason score.

METHODS

We conducted a comprehensive literature review, searching databases such as PubMed, Embase, and Web of Science until June 2023. Our objective was to identify studies that compared the efficacy of 68Ga-PSMA-11 PET/CT and mpMRI in detecting primary prostate cancer. To determine heterogeneity, the I2 statistic was used. Meta-regression analysis and leave-one-out sensitivity analysis were conducted to identify potential sources of heterogeneity.

RESULTS

Initially, 1286 publications were found, but after careful evaluation, only 16 studies involving 1227 patients were analyzed thoroughly. The results showed that the 68Ga-PSMA-11 PET/CT method had a pooled sensitivity and specificity of 0.87 (95 % CI: 0.80-0.92) and 0.80 (95 % CI: 0.69-0.89), respectively, for diagnosing prostatic cancer. Similarly, the values for mpMRI were determined as 0.84 (95 % CI: 0.75-0.92) and 0.74 (95 % CI: 0.61-0.86), respectively. There were no significant differences in diagnostic effectiveness observed when comparing two primary prostate cancer methodologies (pooled sensitivity P = 0.62, pooled specificity P = 0.50). Despite this, the funnel plots showed symmetry and the Egger test results (P values > 0.05) suggested there was no publication bias.

CONCLUSIONS

After an extensive meta-analysis, it was found that both 68Ga-PSMA-11 PET/CT and mpMRI demonstrate similar diagnostic effectiveness in detecting primary prostate cancer. Future larger prospective studies are warranted to investigate this issue further.

ReIGNITE Radiation Therapy Boost: A Prospective, International Study of Radiation Oncologists' Accuracy in Contouring Prostate Tumors for Focal Radiation Therapy Boost on Conventional Magnetic Resonance Imaging Alone or With Assistance of Restriction Spectrum Imaging

PURPOSE

In a phase III randomized trial, adding a radiation boost to tumor(s) visible on MRI improved prostate cancer (PCa) disease-free and metastasis-free survival without additional toxicity. Radiation oncologists' ability to identify prostate tumors is critical to widely adopting intraprostatic tumor radiotherapy boost for patients. A diffusion MRI biomarker, called the Restriction Spectrum Imaging restriction score (RSIrs), has been shown to improve radiologists' identification of clinically significant PCa. We hypothesized that (1) radiation oncologists would find accurately delineating PCa tumors on conventional MRI challenging and (2) using RSIrs maps would improve radiation oncologists' accuracy for PCa tumor delineation.

METHODS AND MATERIALS

In this multi-institutional, international, prospective study, 44 radiation oncologists (participants) and 2 expert radiologists (experts) contoured prostate tumors on 39 total patient cases using conventional MRI with or without RSIrs maps. Participant volumes were compared to the consensus expert volumes. Contouring accuracy metrics included percent overlap with expert volume, Dice coefficient, conformal number, and maximum distance beyond expert volume.

RESULTS

1604 participant volumes were produced. 40 of 44 participants (91%) completely missed ≥1 expert-defined target lesion without RSIrs, compared to 13 of 44 (30%) with RSIrs maps. On conventional MRI alone, 134 of 762 contour attempts (18%) completely missed the target, compared to 18 of 842 (2%) with RSIrs maps. Use of RSIrs maps improved all contour accuracy metrics by approximately 50% or more. Mixed effects modeling confirmed that RSIrs maps were the main variable driving improvement in all metrics. System Usability Scores indicated RSIrs maps significantly improved the contouring experience (72 vs. 58, p < 0.001).

CONCLUSIONS

Radiation oncologists struggle with accurately delineating visible PCa tumors on conventional MRI. RSIrs maps improve radiation oncologists' ability to target MRI-visible tumors for prostate tumor boost.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

Clinical implication of genetic composition and molecular mechanism on treatment strategies of HER2-positive breast cancers

The current clinical management model of HER2-positive breast cancers is commonly based on guidelines, which in turn are based on the design and outcome of clinical trials. While this model is useful to most practicing clinicians, the treatment outcome of individual patient is not certain at the start of treatment. As the understanding of the translational research of carcinogenesis and the related changes in cancer genetics and tumor microenvironment during treatment is critical in the selection of right choice of treatment to maximize the successful clinical outcome for the patient, this review article intends to discuss the latest developments in the genetic and molecular mechanisms of cancer progression and treatment resistance, and how they influence the planning of the treatment strategies of HER2-positive breast cancers.

The Role of [68Ga]PSMA PET/CT for Clinical Suspicion of Prostate Cancer in Patients with or without Previous Negative Biopsy: A Systematic Review

Simple Summary

In this paper we systematically evaluate the evidence regarding the role of [⁶⁸Ga]PSMA PET/CT for clinical suspicions of prostate cancer in patients with or without previous negative biopsy. A critical review of PubMed and Web of Science according to the PRISMA statement was conducted. Eighteen publications were selected for inclusion in the analysis. In 8 articles, there was a direct comparison with mpMRI. [⁶⁸Ga]PSMA PET/CT resulted more accurate in identifying primary prostate cancer with PSA values between 4 and 20 ng/mL than mpMRI. Moreover, its use combined with MRI improved sensitivity for csPCa detection, thus potentially avoiding unnecessary biopsies. Overall, [⁶⁸Ga]PSMA PET/CT resulted a promising technique in patients with clinical suspicion of PCa and precedent negative biopsy or contraindications to MRI.

Abstract

The purpose of the study is to systematically evaluate the evidence regarding the role of [⁶⁸Ga]PSMA PET/CT for clinical suspicions of prostate cancer in patients with or without previous negative biopsy. We performed a critical review of PubMed and Web of Science according to the PRISMA statement. Eighteen publications were selected for inclusion in this analysis. QUADAS-2 evaluation was adopted for quality analyses. [⁶⁸Ga]PSMA-11 was the radiotracer of choice in 15 studies, while [⁶⁸Ga]PSMA-617 was used in another 3. In 8 articles, there was a direct comparison with mpMRI. The total number of patients included was 1379, ranging from 15 to 291, with a median age of 64 years (range: 42–90). The median baseline PSA value was 12.9 ng/mL, ranging from 0.85 to 4156 ng/mL. Some studies evaluated the PSMA uptake comparing the SUVmax of suspicious lesions with the SUVmax of the normal biodistribution to find out optimal cut-off points. In addition, some studies suggested a significant association between PSA levels, PSA density, and [⁶⁸Ga]PSMA PET/CT finding. [⁶⁸Ga]PSMA PET/CT seems to be more accurate in identifying primary prostate cancer with PSA values between 4 and 20 ng/mL than mpMRI. Moreover, in some trials, the combination of PSMA PET/CT and MRI improved the NPV in the detection of clinically significant prostate cancer (csPCa) than MRI alone. Our findings are limited by the small numbers of studies and patient heterogeneity. [⁶⁸Ga]PSMA PET/CT is a promising technique in patients with clinical suspicion of PCa and precedent negative biopsy or contraindications to MRI. Furthermore, its use combined with MRI improves sensitivity for csPCa detection and can avoid unnecessary biopsies.

MRI radiomics predicts progression-free survival in prostate cancer

Objective

To assess the predictive value of magnetic resonance imaging (MRI) radiomics for progression-free survival (PFS) in patients with prostate cancer (PCa).

Methods

191 patients with prostate cancer confirmed by puncture biopsy or surgical pathology were included in this retrospective study, including 133 in the training group and 58 in the validation group. All patients underwent T2WI and DWI serial scans. Three radiomics models were constructed using univariate logistic regression and Gradient Boosting Decision Tree(GBDT) for feature screening, followed by Cox risk regression to construct a mixed model combining radiomics features and clinicopathological risk factors and to draw a nomogram. The performance of the models was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis. The Kaplan-Meier method was applied for survival analysis.

Results

Compared with the radiomics model, the hybrid model consisting of a combination of radiomics features and clinical data performed the best in predicting PFS in PCa patients, with AUCs of 0.926 and 0.917 in the training and validation groups, respectively. Decision curve analysis showed that the radiomics nomogram had good clinical application and the calibration curve proved to have good stability. Survival curves showed that PFS was shorter in the high-risk group than in the low-risk group.

Conclusion

The hybrid model constructed from radiomics and clinical data showed excellent performance in predicting PFS in prostate cancer patients. The nomogram provides a non-invasive diagnostic tool for risk stratification of clinical patients.

Comparison of Multiparametric Magnetic Resonance Imaging with Prostate-Specific Membrane Antigen Positron-Emission Tomography Imaging in Primary Prostate Cancer Diagnosis: A Systematic Review and Meta-Analysis

Multiparametric magnetic-resonance imaging (mpMRI) has proven utility in diagnosing primary prostate cancer. However, the diagnostic potential of prostate-specific membrane antigen positron-emission tomography (PSMA PET) has yet to be established. This study aims to systematically review the current literature comparing the diagnostic performance of mpMRI and PSMA PET imaging to diagnose primary prostate cancer. A systematic literature search was performed up to December 2021. Quality analyses were conducted using the QUADAS-2 tool. The reference standard was whole-mount prostatectomy or prostate biopsy. Statistical analysis involved the pooling of the reported diagnostic performances of each modality, and differences in per-patient and per-lesion analysis were compared using a Fisher’s exact test. Ten articles were included in the meta-analysis. At a per-patient level, the pooled values of sensitivity, specificity, and area under the curve (AUC) for mpMRI and PSMA PET/CT were 0.87 (95% CI: 0.83−0.91) vs. 0.93 (95% CI: 0.90−0.96, p < 0.01); 0.47 (95% CI: 0.23−0.71) vs. 0.54 (95% CI: 0.23−0.84, p > 0.05); and 0.84 vs. 0.91, respectively. At a per-lesion level, the pooled sensitivity, specificity, and AUC value for mpMRI and PSMA PET/CT were lower, at 0.63 (95% CI: 0.52−0.74) vs. 0.79 (95% CI: 0.62−0.92, p < 0.001); 0.88 (95% CI: 0.81−0.95) vs. 0.71 (95% CI: 0.47−0.90, p < 0.05); and 0.83 vs. 0.84, respectively. High heterogeneity was observed between studies. PSMA PET/CT may better confirm the presence of prostate cancer than mpMRI. However, both modalities appear comparable in determining the localisation of the lesions.

The Prognostic Value of PI-RADS Score in CyberKnife Ultra-Hypofractionated Radiotherapy for Localized Prostate Cancer

Prostate Imaging-Reporting and Data System (PI-RADS) has been widely implemented as a diagnostic tool for significant prostate cancer (PCa); less is known about its prognostic value, especially in the setting of primary radiotherapy. We aimed to analyze the association between PI-RADS v. 2.1 classification and risk of metastases, based on a group of 152 patients treated with ultra-hypofractionated stereotactic CyberKnife radiotherapy for localized low or intermediate risk-group prostate cancer. We found that all distant failures (n = 5) occurred in patients diagnosed with a PI-RADS score of 5, and axial measurements of the target lesion were associated with the risk of developing metastases (p < 0.001). The best risk stratification model (based on a combination of greatest dimension, the product of multiplication of PI-RADS target lesion axial measurements, and age) achieved a c-index of 0.903 (bootstrap-validated bias-corrected 95% CI: 0.848−0.901). This creates a hypothesis that PI-RADS 5 and the size of the target lesion are important prognostic factors in early-stage PCa patients and should be considered as an adverse prognostic measure for patients undergoing early treatment such as radiation or focal therapy.

Unified model involving genomics, magnetic resonance imaging and prostate‐specific antigen density outperforms individual co‐variables at predicting biopsy upgrading in patients on active surveillance for low risk prostate cancer

Background

Active surveillance (AS) is the reference standard treatment for the management of low risk prostate cancer (PCa). Accurate assessment of tumor aggressiveness guides recruitment to AS programs to avoid conservative treatment of intermediate and higher risk patients. Nevertheless, underestimating the disease risk may occur in some patients recruited, with biopsy upgrading and the concomitant potential for delayed treatment.

Aim

To evaluate the accuracy of mpMRI and GPS for the prediction of biopsy upgrading during active surveillance (AS) management of prostate cancer (PCa).

Method

A retrospective analysis was performed on 144 patients recruited to AS from October 2013 to December 2020. Median follow was 4.8 (IQR 3.6, 6.3) years. Upgrading was defined as upgrading to biopsy grade group ≥2 on follow up biopsies. Cox proportional hazard regression was used to investigate the effect of PSA density (PSAD), baseline Prostate Imaging‐Reporting and Data System (PI‐RADS) v2.1 score and GPS on upgrading. Time‐to‐event outcome, defined as upgrading, was estimated using the Kaplan–Meier method with log‐rank test.

Results

Overall rate of upgrading was 31.9% (n = 46). PSAD was higher in the patients who were upgraded (0.12 vs. 0.08 ng/ml², p = .005), while no significant difference was present for median GPS in the overall cohort (overall median GPS 21; 22 upgrading vs. 20 no upgrading, p = .2044). On univariable cox proportional hazard regression analysis, the factors associated with increased risk of biopsy upgrading were PSA (HR = 1.30, CI 1.16–1.47, p = <.0001), PSAD (HR = 1.08, CI 1.05–1.12, p = <.0001) and higher PI‐RADS score (HR = 3.51, CI 1.56–7.91, p = .0024). On multivariable cox proportional hazard regression analysis, only PSAD (HR = 1.10, CI 1.06–1.14, p = <.001) and high PI‐RADS score (HR = 4.11, CI 1.79–9.44, p = .0009) were associated with upgrading. A cox regression model combining these three clinical features (PSAD ≥0.15 ng/ml² at baseline, PI‐RADS Score and GPS) yielded a concordance index of 0.71 for the prediction of upgrading.

Conclusion

In this study PSAD has higher accuracy over baseline PI‐RADS score and GPS score for the prediction of PCa upgrading during AS. However, combined use of PSAD, GPS and PI‐RADS Score yielded the highest predictive ability with a concordance index of 0.71.

Network Analysis Integrating microRNA Expression Profiling with MRI Biomarkers and Clinical Data for Prostate Cancer Early Detection: A Proof of Concept Study

The MRI of the prostate is the gold standard for the detection of clinically significant prostate cancer (csPCa). Nonetheless, MRI still misses around 11% of clinically significant disease. The aim was to comprehensively integrate tissue and circulating microRNA profiling, MRI biomarkers and clinical data to implement PCa early detection. In this prospective cohort study, 76 biopsy naïve patients underwent MRI and MRI directed biopsy. A sentinel sample of 15 patients was selected for a pilot molecular analysis. Weighted gene coexpression network analysis was applied to identify the microRNAs drivers of csPCa. MicroRNA-target gene interaction maps were constructed, and enrichment analysis performed. The ANOVA on ranks test and ROC analysis were performed for statistics. Disease status was associated with the underexpression of the miRNA profiled; a correlation was found with ADC (r = -0.51, p = 0.02) and normalized ADC values (r = -0.64, p = 0.002). The overexpression of miRNAs from plasma was associated with csPCa (r = 0.72; p = 0.02), and with PI-RADS assessment score (r = 0.73; p = 0.02); a linear correlation was found with biomarkers of diffusion and perfusion. Among the 800 profiled microRNA, eleven were identified as correlating with PCa, among which miR-548a-3p, miR-138-5p and miR-520d-3p were confirmed using the RT-qPCR approach on an additional cohort of ten subjects. ROC analysis showed an accuracy of >90%. Provided an additional validation set of the identified miRNAs on a larger cohort, we propose a diagnostic paradigm shift that sees molecular data and MRI biomarkers as the prebiopsy triage of patients at risk for PCa. This approach will allow for accurate patient allocation to biopsy, and for stratification into risk group categories, reducing overdiagnosis and overtreatment.

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.

Which Prostate Cancers are Undetected by Multiparametric Magnetic Resonance Imaging in Men with Previous Prostate Biopsy? An Analysis from the PICTURE Study

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) has improved risk stratification for suspected prostate cancer in patients following prior biopsy. However, not all significant cancers are detected by mpMRI. The PICTURE study provides the ideal opportunity to investigate cancer undetected by mpMRI owing to the use of 5 mm transperineal template mapping (TTPM) biopsy.

OBJECTIVE

To summarise attributes of cancers systematically undetected by mpMRI in patients with prior biopsy.

DESIGN SETTING AND PARTICIPANTS

PICTURE was a paired-cohort confirmatory study in which men requiring repeat biopsy underwent mpMRI followed by TTPM biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Attributes were compared between cancers detected and undetected by mpMRI at the patient level. Four predefined histopathological thresholds were used as the target condition for TTPM biopsy. Application of prostate-specific antigen density (PSAD) was explored.

RESULTS AND LIMITATIONS

When nonsuspicious mpMRI was defined as Likert score 1-2, 2.9% of patients (3/103; 95% confidence interval [CI] 0.6-8.3%) with definition 1 disease (Gleason ≥ 4 + 3 of any length or maximum cancer core length [MCCL] ≥ 6 mm of any grade) had their cancer not detected by mpMRI. This proportion was 6.5% (11/168; 95% CI 3.3-11%) for definition 2 disease (Gleason ≥ 3 + 4 of any length or MCCL ≥ 4 mm of any grade), 4.8% (7/146; 95% CI 2.0-9.6%) for any amount of Gleason ≥ 3 + 4 cancer, and 9.3% (20/215; 95% CI 5.8-14%) for any cancer. Definition 1 cancers undetected by mpMRI had lower overall Gleason score (p = 0.02) and maximum Gleason score (p = 0.01) compared to cancers detected by mpMRI. Prostate cancers undetected by mpMRI had shorter MCCL than cancers detected by mpMRI for every cancer threshold: definition 1, 6 versus 8 mm (p = 0.02); definition 2, 5 versus 6 mm (p = 0.04); any Gleason ≥ 3 + 4, 5 versus 6 mm (p = 0.03); and any cancer, 3 versus 5 mm (p = 0.0009). A theoretical PSAD threshold of 0.15 ng/ml/ml reduced the proportion of patients with undetected disease on nonsuspicious mpMRI to 0% (0/105; 95% CI 0-3.5%) for definition 1, 0.58% (1/171; 95% CI 0.01-3.2%) for definition 2, and 0% (0/146) for any Gleason ≥ 3 + 4.

CONCLUSIONS

Few significant cancers are undetected by mpMRI in patients requiring repeat prostate biopsy. Undetected tumours are of lower overall and maximum Gleason grade and shorter cancer length compared to cancers detected by mpMRI.

PATIENT SUMMARY

In patients with a previous prostate biopsy, magnetic resonance imaging (MRI) overlooks few prostate cancers, and these tend to be smaller and less aggressive than cancer that is detected.

Clinical outcomes associated with prostate cancer conspicuity on biparametric and multiparametric MRI: a protocol for a systematic review and meta-analysis of biochemical recurrence following radical prostatectomy

INTRODUCTION

There is an increasing body of evidence to suggest that visibility of prostate cancer on magnetic resonance (MRI) may be related to likelihood of adverse pathological outcomes. Biochemical recurrence (BCR) after radical prostatectomy remains a significant clinical challenge and a means of predicting likelihood of this prior to surgery could inform treatment choice. It appears that MRI could be a potential candidate strategy for BCR prediction, and as such, there is a need to review extant literature on the prognostic capability of MRI. Here, we describe a protocol for a systematic review and meta-analysis of the utility of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) in predicting BCR following radical prostatectomy for prostate cancer treatment.

METHODS AND ANALYSIS

PubMed, MEDLINE, Embase and Cochrane databases will be searched and screening will be guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. In order to meet the inclusion criteria, papers must be English-language articles involving patients who have had bpMRI or mpMRI for suspected prostate cancer and have undergone radical prostatectomy as definitive therapy. Patients must have had prostate-specific antigen monitoring before and after surgery. All relevant papers published from July 1977 to October 2020 will be eligible for inclusion. The Newcastle-Ottawa score will be used to determine the quality and bias of the studies. This protocol is written in-line with the PRISMA protocol 2015 checklist.

ETHICS AND DISSEMINATION

There are no relevant ethical concerns. Dissemination of this protocol will be via peer-reviewed journals as well as national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020206074.

Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer

PURPOSE

Multiparametric MRI (mpMRI) has become an indispensable radiographic tool in diagnosing prostate cancer. However, mpMRI fails to visualize approximately 15% of clinically significant prostate cancer (csPCa). The molecular, cellular, and spatial underpinnings of such radiographic heterogeneity in csPCa are unclear.

EXPERIMENTAL DESIGN

We examined tumor tissues from clinically matched patients with mpMRI-invisible and mpMRI-visible csPCa who underwent radical prostatectomy. Multiplex immunofluorescence single-cell spatial imaging and gene expression profiling were performed. Artificial intelligence-based analytic algorithms were developed to examine the tumor ecosystem and integrate with corresponding transcriptomics.

RESULTS

More complex and compact epithelial tumor architectures were found in mpMRI-visible than in mpMRI-invisible prostate cancer tumors. In contrast, similar stromal patterns were detected between mpMRI-invisible prostate cancer and normal prostate tissues. Furthermore, quantification of immune cell composition and tumor-immune interactions demonstrated a lack of immune cell infiltration in the malignant but not in the adjacent nonmalignant tissue compartments, irrespective of mpMRI visibility. No significant difference in immune profiles was detected between mpMRI-visible and mpMRI-invisible prostate cancer within our patient cohort, whereas expression profiling identified a 24-gene stromal signature enriched in mpMRI-invisible prostate cancer. Prostate cancer with strong stromal signature exhibited a favorable survival outcome within The Cancer Genome Atlas prostate cancer cohort. Notably, five recurrences in the 8 mpMRI-visible patients with csPCa and no recurrence in the 8 clinically matched patients with mpMRI-invisible csPCa occurred during the 5-year follow-up post-prostatectomy.

CONCLUSIONS

Our study identified distinct molecular, cellular, and structural characteristics associated with mpMRI-visible csPCa, whereas mpMRI-invisible tumors were similar to normal prostate tissue, likely contributing to mpMRI invisibility.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Simple Summary

The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted.

Abstract

The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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.

A Modified Newcastle-Ottawa Scale for Assessment of Study Quality in Genetic Urological Research

Our modification of the traditional Newcastle-Ottawa scale enables urological researchers to effectively appraise and communicate the quality of genetic-based research in urology.

Histopathological features of prostate cancer conspicuity on multiparametric MRI: protocol for a systematic review and meta-analysis

INTRODUCTION

Multiparametric MRI (mpMRI) has improved risk stratification for men with suspected prostate cancer. Indeed, mpMRI-visible tumours tend to be larger and of higher pathological grade than mpMRI-invisible tumours; however, concern remains around significant cancer that is undetected by mpMRI. There has been considerable recent interest to investigate whether tumour conspicuity on mpMRI is associated with additional histopathological features (including cellular density, microvessel density and unusual prostate cancer subtypes), which may have important clinical implications in both diagnosis and prognosis. Furthermore, analysis of these features may help reveal the radiobiology that underpins the actual mechanisms of mpMRI visibility (and invisibility) of prostate tumours. Here, we describe a protocol for a systematic review of the histopathological basis of prostate cancer conspicuity on mpMRI.

METHODS AND ANALYSIS

A systematic search of the MEDLINE, PubMed, Embase and Cochrane databases will be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines will be used to guide screening, thematic reporting and conclusions drawn from all eligible studies. Included papers will be full-text, English-language articles, comparing the histopathological characteristics of mpMRI-visible lesions and mpMRI-invisible tumours. All studies published between January 1950 and January 2020 will be eligible for inclusion. Studies using confirmatory immunohistochemistry for the identification of immune subsets or structural components will be included. Study bias and quality will be assessed using a modified Newcastle-Ottawa scale. To ensure methodological rigour, this protocol is written in accordance with the PRISMA Protocol 2015 checklist. If appropriate, a meta-analysis will be conducted comparing histopathological feature frequency between mpMRI-visible and mpMRI-invisible disease.

ETHICS AND DISSEMINATION

No ethical approval will be required as this is an academic review of published literature. Findings will be disseminated through publications in peer-reviewed journals and presentations at national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020176049.