MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance

PRECISE Version 2: Updated Recommendations for Reporting Prostate Magnetic Resonance Imaging in Patients on Active Surveillance for Prostate Cancer

BACKGROUND AND OBJECTIVE

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations standardise the reporting of prostate magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer. An international consensus group recently updated these recommendations and identified the areas of uncertainty.

METHODS

A panel of 38 experts used the formal RAND/UCLA Appropriateness Method consensus methodology. Panellists scored 193 statements using a 1-9 agreement scale, where 9 means full agreement. A summary of agreement, uncertainty, or disagreement (derived from the group median score) and consensus (determined using the Interpercentile Range Adjusted for Symmetry method) was calculated for each statement and presented for discussion before individual rescoring.

KEY FINDINGS AND LIMITATIONS

Participants agreed that MRI scans must meet a minimum image quality standard (median 9) or be given a score of 'X' for insufficient quality. The current scan should be compared with both baseline and previous scans (median 9), with the PRECISE score being the maximum from any lesion (median 8). PRECISE 3 (stable MRI) was subdivided into 3-V (visible) and 3-NonV (nonvisible) disease (median 9). Prostate Imaging Reporting and Data System/Likert ≥3 lesions should be measured on T2-weighted imaging, using other sequences to aid in the identification (median 8), and whenever possible, reported pictorially (diagrams, screenshots, or contours; median 9). There was no consensus on how to measure tumour size. More research is needed to determine a significant size increase (median 9). PRECISE 5 was clarified as progression to stage ≥T3a (median 9).

CONCLUSIONS AND CLINICAL IMPLICATIONS

The updated PRECISE recommendations reflect expert consensus opinion on minimal standards and reporting criteria for prostate MRI in AS.

Comparison of systematic and combined biopsy for the detection of prostate cancer

ABSTRACT

Systematic prostate biopsy has limitations, such as overdiagnosis of clinically insignificant prostate cancer and underdiagnosis of clinically significant prostate cancer. Magnetic resonance imaging (MRI)-guided biopsy, a promising alternative, might improve diagnostic accuracy. To compare the cancer detection rates of systematic biopsy and combined biopsy (systematic biopsy plus MRI-targeted biopsy) in Asian men, we conducted a retrospective cohort study of men who underwent either systematic biopsy or combined biopsy at two medical centers (Queen Mary Hospital and Tung Wah Hospital, Hong Kong, China) from July 2015 to December 2022. Descriptive statistics were calculated, and univariate and multivariate logistic regression analyses were performed. The primary and secondary outcomes were prostate cancer and clinically significant prostate cancer. A total of 1391 participants were enrolled. The overall prostate cancer detection rates did not significantly differ between the two groups (36.3% vs 36.6%, odds ratio [OR] = 1.01, 95% confidence interval [CI]: 0.81-1.26, P = 0.92). However, combined biopsy showed a significant advantage in detecting clinically significant prostate cancer (Gleason score ≥ 3+4) in patients with a total serum prostate-specific antigen (tPSA) concentration of 2-10 ng ml -1 (systematic vs combined: 11.9% vs 17.5%, OR = 1.58, 95% CI: 1.08-2.31, P = 0.02). Specifically, in the transperineal biopsy subgroup, combined biopsy significantly outperformed systematic biopsy in the detection of clinically significant prostate cancer (systematic vs combined: 12.6% vs 24.0%, OR = 2.19, 95% CI: 1.21-3.97, P = 0.01). These findings suggest that in patients with a tPSA concentration of 2-10 ng ml -1 , MRI-targeted biopsy may be of greater predictive value than systematic biopsy in the detection of clinically significant prostate cancer.

Is MRI ready to replace biopsy during active surveillance?

Active surveillance (AS) is a conservative management option recommended for patients diagnosed with low-risk prostate cancer (PCa) and selected cases with intermediate-risk PCa. The adoption of prostate MRI in the primary diagnostic setting has sparked interest in its application during AS. This review aims to examine the role and performance of multiparametric MRI (mpMRI) across the entire AS pathway, from initial stratification to follow-up, also relative to the utilization of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) criteria. Given the high negative predictive value of mpMRI in detecting clinically significant PCa (csPCa), robust evidence supports its use in patient selection and risk stratification at the time of diagnosis or confirmatory biopsy. However, conflicting results have been observed when using MRI in evaluating disease progression during follow-up. Key areas requiring clarification include addressing the clinical significance of MRI-negative csPCa, optimizing MRI quality, determining the role of biparametric MRI (bpMRI) or mpMRI protocols, and integrating artificial intelligence (AI) for improved performance. CLINICAL RELEVANCE STATEMENT: MRI plays an essential role in the selection, stratification, and follow up of patients in active surveillance (AS) for prostate cancer. However, owing to existing limitations, it cannot fully replace biopsies in the context of AS. KEY POINTS: Multiparametric MRI (mpMRI) has become a crucial tool in active surveillance (AS) for prostate cancer (PCa). Conflicting results have been observed regarding multiparametric MRI efficacy in assessing disease progression. Standardizing MRI-guided protocols will be critical in addressing current limitations in active surveillance for prostate cancer.

Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?

OBJECTIVE

To review the components of past and present active surveillance (AS) protocols, provide an overview of the current studies employing artificial intelligence (AI) in AS of prostate cancer, discuss the current challenges of AI in AS, and offer recommendations for future research.

METHODS

Research studies on the topic of MRI-based AI were reviewed to summarize current possibilities and diagnostic accuracies for AI methods in the context of AS. Established guidelines were used to identify possibilities for future refinement using AI.

RESULTS

Preliminary results show the role of AI in a range of diagnostic tasks in AS populations, including the localization, follow-up, and prognostication of prostate cancer. Current evidence is insufficient to support a shift to AI-based AS, with studies being limited by small dataset sizes, heterogeneous inclusion and outcome definitions, or lacking appropriate benchmarks.

CONCLUSION

The AI-based integration of prostate MRI is a direction that promises substantial benefits for AS in the future, but evidence is currently insufficient to support implementation. Studies with standardized inclusion criteria and standardized progression definitions are needed to support this. The increasing inclusion of patients in AS protocols and the incorporation of MRI as a scheduled examination in AS protocols may help to alleviate these challenges in future studies.

CLINICAL RELEVANCE STATEMENT

This manuscript provides an overview of available evidence for the integration of prostate MRI and AI in active surveillance, addressing its potential for clinical optimizations in the context of established guidelines, while highlighting the main challenges for implementation.

KEY POINTS

Active surveillance is currently based on diagnostic tests such as PSA, biopsy, and imaging. Prostate MRI and AI demonstrate promising diagnostic accuracy across a variety of tasks, including the localization, follow-up and risk estimation in active surveillance cohorts. A transition to AI-based active surveillance is not currently realistic; larger studies using standardized inclusion criteria and outcomes are necessary to improve and validate existing evidence.

Biparametric MRI in prostate cancer during active surveillance: is it safe?

Active surveillance (AS) is the preferred option for patients presenting with low-intermediate-risk prostate cancer. MRI now plays a crucial role for baseline assessment and ongoing monitoring of AS. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations aid radiological assessment of progression; however, current guidelines do not advise on MRI protocols nor on frequency. Biparametric (bp) imaging without contrast administration offers advantages such as reduced costs and increased throughput, with similar outcomes to multiparametric (mp) MRI shown in the biopsy naïve setting. In AS follow-up, the paradigm shifts from MRI lesion detection to assessment of progression, and patients have the further safety net of continuing clinical surveillance. As such, bpMRI may be appropriate in clinically stable patients on routine AS follow-up pathways; however, there is currently limited published evidence for this approach. It should be noted that mpMRI may be mandated in certain patients and potentially offers additional advantages, including improving image quality, new lesion detection, and staging accuracy. Recently developed AI solutions have enabled higher quality and faster scanning protocols, which may help mitigate against disadvantages of bpMRI. In this article, we explore the current role of MRI in AS and address the need for contrast-enhanced sequences. CLINICAL RELEVANCE STATEMENT: Active surveillance is the preferred plan for patients with lower-risk prostate cancer, and MRI plays a crucial role in patient selection and monitoring; however, current guidelines do not currently recommend how or when to perform MRI in follow-up. KEY POINTS: Noncontrast biparametric MRI has reduced costs and increased throughput and may be appropriate for monitoring stable patients. Multiparametric MRI may be mandated in certain patients, and contrast potentially offers additional advantages. AI solutions enable higher quality, faster scanning protocols, and could mitigate the disadvantages of biparametric imaging.

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

BACKGROUND

 Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).

METHOD

 This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.

RESULTS AND CONCLUSION

 Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.

KEY POINTS

· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..

CITATION FORMAT

· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

Active surveillance of low-grade prostate cancer using the SurACaP Criteria: A multi-institutional series with a median follow-up of 10years

PURPOSE

To report on the oncological outcomes of active surveillance (AS) in low-grade prostate cancer (PCa) patients using the French SurACaP protocol, with a focus on long-term outcomes.

METHODS

This multicenter study recruited patients with low-grade PCa between 2007 and 2013 in four referral centers in France. The cohort included patients meeting the SurACaP inclusion criteria, i.e., aged ≤75years, with low-grade PCa (i.e., ISUP 1), clinical stage T1c/T2a, PSA ≤10ng/mL and ≤3 positive cores and tumor length ≤3mm per core. The SurACaP protocol included a digital rectal examination every six months, PSA level measurement every three months for the first two years after inclusion and twice a year thereafter, a confirmatory biopsy in the first year after inclusion, and then follow-up biopsy every two years or if disease progression was suspected. Multiparametric magnetic resonance imaging (mpMRI) was progressively included over the study period.

RESULTS

A total of 86 consecutive patients were included, with a median follow-up of 10.6 years. Only one patient developed metastases and died of PCa. The estimated rates of grade reclassification and treatment-free survival at 15 years were 53.4% and 21.2%, respectively. A negative mpMRI at baseline and a negative confirmatory biopsy were significantly associated with a lower risk of disease progression (P<0.05).

CONCLUSIONS

AS using the French SurACaP protocol is a safe and valuable strategy for patients with low-risk PCa, with excellent oncological outcomes after more than 10 years' follow-up. Future studies are crucial to broaden the inclusion criteria and develop a personalized, risk based AS protocol with the aim of de-escalating follow-up examinations.

LEVEL OF EVIDENCE

Grade 4.

Active Surveillance for Prostate Cancer: Expanding the Role of MR Imaging and the Use of PRECISE Criteria

Multiparametric magnetic resonance (MR) imaging has had an expanding role in active surveillance (AS) for prostate cancer. It can improve the accuracy of prostate biopsies, assist in patient selection, and help monitor cancer progression. The PRECISE recommendations standardize reporting of serial MR imaging scans during AS. We summarize the evidence on MR imaging-led AS and provide a clinical primer to help report using the PRECISE criteria. Some limitations to both serial imaging and the PRECISE recommendations must be considered as we move toward a more individualized risk-stratified approach to AS.

The PRECISE Recommendations for Prostate MRI in Patients on Active Surveillance for Prostate Cancer: A Critical Review

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations were published in 2016 to standardize the reporting of MRI examinations performed to assess for disease progression in patients on active surveillance for prostate cancer. Although a limited number of studies have reported outcomes from use of PRECISE in clinical practice, the available studies have demonstrated PRECISE to have high pooled NPV but low pooled PPV for predicting progression. Our experience in using PRECISE in clinical practice at two teaching hospitals has highlighted issues with its application and areas requiring clarification. This Clinical Perspective critically appraises PRECISE on the basis of this experience, focusing on the system's key advantages and disadvantages and exploring potential changes to improve the system's utility. These changes include consideration of image quality when applying PRECISE scoring, incorporation of quantitative thresholds for disease progression, adoption of a PRECISE 3F sub-category for progression not qualifying as substantial, and comparisons with both the baseline and most recent prior examinations. Items requiring clarification include derivation of a patient-level score in patients with multiple lesions, intended application of PRECISE score 5 (i.e., if requiring development of disease that is no longer organ-confined), and categorization of new lesions in patients with prior MRI-invisible disease.

68Ga-PSMA-PET/CT in addition to mpMRI in men undergoing biopsy during active surveillance for low- to intermediate-risk prostate cancer: study protocol for a prospective cross-sectional study

Background

In active surveillance there is significant interest in whether imaging modalities such as multiparametric magnetic resonance imaging (mpMRI) or 68Gallium prostate-specific membrane antigen positron emission tomography/computerized tomography (68Ga-PSMA-PET/CT) can improve the detection of progression to clinically significant prostate cancer (csPCa) and thus reduce the frequency of prostate biopsies and associated morbidity. Recent studies have demonstrated the value of mpMRI in active surveillance; however, mpMRI does miss a proportion of disease progression and thus alone cannot replace biopsy. To date, prostate-specific membrane antigen positron emission tomography (PSMA-PET) has shown additive value to mpMRI in its ability to detect prostate cancer (PCa) in the primary diagnostic setting. Our objective is to evaluate the diagnostic utility of PSMA-PET to detect progression to csPCa in active surveillance patients.

Methods

We will perform a prospective, cross-sectional, partially blinded, multicentre clinical trial evaluating the additive value of PSMA-PET with mpMRI against saturation transperineal template prostate biopsy. Two hundred and twenty-five men will be recruited who have newly diagnosed PCa which is suitable for active surveillance. Following enrolment, patients will undergo a PSMA-PET and mpMRI within 3 months of a repeat 12-month confirmatory biopsy. Patients who remain on active surveillance after confirmatory biopsy will then be planned to have a further mpMRI and PSMA-PET prior to a repeat biopsy in 3-4 years. The primary outcome is to assess the ability of PSMA-PET to detect or exclude significant malignancy on repeat biopsy. Secondary outcomes include (I) assess the comparative diagnostic accuracies of mpMRI and PSMA-PET alone [sensitivity/specificity/negative predictive value (NPV)/positive predictive value (PPV)] to detect progression on biopsy based on predefined histologic criteria for progression; (II) comparison of index lesion identification by template biopsies vs. MRI targeted lesions vs. PSMA targeted lesions; (III) evaluation of concordance of lesions identified on final histopathology and each imaging modality (PSMA-PET and/or mpMRI) in the subset of patients proceeding to RP.

Discussion

The results of this trial will define the role of PSMA-PET in active surveillance and potentially reduce the number of biopsies needed to detect progression to csPCa.

Trial Registration

The current trial was registered with the ANZCTR on the 3/2/2022 with the trial ID ACTRN12622000188730, it is accessible at https://www.anzctr.org.au/.

Clinical Application of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation Score for Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer

Background

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) score has been developed to standardise prostate magnetic resonance imaging (MRI) reporting in men on active surveillance (AS) for prostate cancer (PCa).

Objective

To evaluate the feasibility of PRECISE scoring and assess its diagnostic accuracy.

Design setting and participants

All PCa patients on AS with a baseline MRI and at least one follow-up MRI scan between January 2008 and September 2022 at a single tertiary referral centre were included in a database. The follow-up protocol of the Prostate Cancer International Active Surveillance (PRIAS) study was used. All scans were retrospectively re-reported by a dedicated uroradiologist and appointed a Prostate Imaging Reporting and Data System (version 2.1) and PRECISE score.

Outcome measurements and statistical analysis

Clinically significant progression was defined by histopathological upgrading (on biopsy or radical prostatectomy) to grade group ≥3 and/or evolution to T3 stage. A survival analysis was performed to assess differential progression-free survival (PFS) according to the PRECISE score.

Results and limitations

A total of 188 patients were included for an analysis with a total of 358 repeat MRI scans and 144 repeat biopsies. The median follow-up was 46 mo (interquartile range 21-74). Radiological progression (PRECISE 4-5) had sensitivity, specificity, negative predictive value, and positive predictive value of, respectively, 78%, 70%, 90%, and 49% for clinically significant progression. Four-year PFS was 91% for PRECISE 1-3 versus 66% for PRECISE 4-5 (p < 0.001). In total, 137 patients underwent a confirmation MRI scan within 18 mo after diagnosis. Four-year PFS in this group was 81% for PRECISE 1-3 versus 43% for PRECISE 4-5 (p < 0.001). Limitations include retrospective design and no strict adherence to AS protocol.

Conclusions

Implementation of PRECISE scoring for PCa patients on AS is feasible and offers a prognostic value. Patients with PRECISE score 4-5 on confirmation MRI within 18 mo after diagnosis have a three-fold higher risk of clinically significant progression after 4 yr.

Patient summary

Patients with low-risk prostate cancer can be followed up carefully. In this study, we evaluate the standardised reporting of repeat magnetic resonance imaging scans (using the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation [PRECISE] recommendations). PRECISE scoring is feasible and helps identify patients in need of further treatment.

mpMRI Interpretation in Active Surveillance for Prostate Cancer-An overview of the PRECISE score

Active surveillance (AS) is now included in all major guidelines for patients with low-risk PCa and selected patients with intermediate-risk PCa. Several studies have highlighted the potential benefit of multiparametric magnetic resonance imaging (mpMRI) in AS and it has been adopted in some guidelines. However, uncertainty remains about whether serial mpMRI can help to safely reduce the number of required repeat biopsies under AS. In 2017, the European School of Oncology initiated the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) panel which proposed the PRECISE scoring system to assess the likelihood of radiological tumor progression on serial mpMRI. The PRECISE scoring system remains the only major system evaluated in multiple publications. In this review article, we discuss the current body of literature investigating the application of PRECISE as it is not as yet an established standard in mpMRI reporting. We delineate the strengths of PRECISE and its potential added value. Also, we underline potential weaknesses of the PRECISE scoring system, which might be tackled in future versions to further increase its value in AS.

Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance

The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSAD_NA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSAD_A). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSAD_NA significantly outperformed both PSAD_A and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSAD_NA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml.

Patient summary

Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.

Contemporary Review of Multimodality Imaging of the Prostate Gland

Tissue changes and the enlargement of the prostate, whether benign or malignant, are among the most common groups of diseases that affect men and can have significant impacts on length and quality of life. The prevalence of benign prostatic hyperplasia (BPH) increases significantly with age and affects nearly all men as they grow older. Other than skin cancers, prostate cancer is the most common cancer among men in the United States. Imaging is an essential component in the diagnosis and management of these conditions. Multiple modalities are available for prostate imaging, including several novel imaging modalities that have changed the landscape of prostate imaging in recent years. This review will cover the data relating to commonly used standard-of-care prostate imaging modalities, advances in newer technologies, and newer standards that impact prostate gland imaging.

Active Surveillance for Prostate Cancer: Past, Current, and Future Trends

In response to the rising incidence of indolent, low-risk prostate cancer (PCa) due to increased prostate-specific antigen (PSA) screening in the 1990s, active surveillance (AS) emerged as a treatment modality to combat overtreatment by delaying or avoiding unnecessary definitive treatment and its associated morbidity. AS consists of regular monitoring of PSA levels, digital rectal exams, medical imaging, and prostate biopsies, so that definitive treatment is only offered when deemed necessary. This paper provides a narrative review of the evolution of AS since its inception and an overview of its current landscape and challenges. Although AS was initially only performed in a study setting, numerous studies have provided evidence for the safety and efficacy of AS which has led guidelines to recommend it as a treatment option for patients with low-risk PCa. For intermediate-risk disease, AS appears to be a viable option for those with favourable clinical characteristics. Over the years, the inclusion criteria, follow-up schedule and triggers for definitive treatment have evolved based on the results of various large AS cohorts. Given the burdensome nature of repeat biopsies, risk-based dynamic monitoring may further reduce overtreatment by avoiding repeat biopsies in selected patients.

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.

Prospective Implementation and Early Outcomes of a Risk-stratified Prostate Cancer Active Surveillance Follow-up Protocol

Background

Active surveillance (AS) is a major management option for men with early prostate cancer. Current guidelines however advocate identical AS follow-up for all without considering different disease trajectories. We previously proposed a pragmatic three-tier STRATified CANcer Surveillance (STRATCANS) follow-up strategy based on different progression risks from clinic-pathological and imaging features.

Objective

To report early outcomes from the implementation of the STRATCANS protocol in our centre.

Design setting and participants

Men on AS were enrolled into a prospective stratified follow-up programme.

Intervention

Three tiers of increasing follow-up intensity based on National Institute for Health and Care Excellence (NICE): Cambridge Prognostic Group (CPG) 1 or 2, prostate-specific antigen density, and magnetic resonance imaging (MRI) Likert score at entry.

Outcome measurements and statistical analysis

Rates of progression to CPG ≥3, any pathological progression, AS attrition, and patient choice for treatment were assessed. Differences in progression were compared with chi-square statistics.

Results and limitations

Data from 156 men (median age 67.3 yr) were analysed. Of these, 38.4% had CPG2 disease and 27.5% had grade group 2 disease at diagnosis. The median time on AS was 4 yr (interquartile range 3.2-4.9) and 1.5 yr on STRATCANS. Overall, 135/156 (86.5%) men remained on AS or converted to watchful waiting and 6/156 (3.8%) stopped AS by choice by the end of the evaluation period. Of the 156 patients, 66 (42.3%) were allocated to STRATCANS 1 (least intense follow-up), 61 (39.1%) to STRATCANS 2, and 29 (18.6%) to STRATCANS 3 (highest intensity). By increasing STRATCANS tier, progression rates to CPG ≥3 and any progression events were 0% and 4.6%, 3.4% and 8.6%, and 7.4% and 22.2%, respectively (p = 0.019). Modelling resource usage suggested potential reductions in appointments by 22% and MRI by 42% compared with current NICE guideline recommendations (first 12 months of AS). The study is limited by short follow-up, a relatively small cohort, and being single centre.

Conclusions

A simple risk-tiered AS strategy is possible with early outcomes supporting stratified follow-up intensity. STRATCANS implementation could de-escalate follow-up in men at a low risk of progression while husbanding resources for those who need closer follow-up.

Patient summary

We report a practical way to personalise follow-up for men on active surveillance for early prostate cancer. Our method may allow reductions in the follow-up burden for men at a low risk of disease change while maintaining vigilance for those at a higher risk.

Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Modern Active Surveillance in Prostate Cancer: A Narrative Review

The use of PSA screening has led to downstaging and downgrading of prostate cancer at diagnosis, increasing detection of indolent disease. Active surveillance aims to reduce over-treatment by delaying or avoiding radical treatment and its associated morbidity. However, there is not a consensus on the selection criteria and monitoring schedules that should be used. This article aims to summarize the evidence supporting the safety of active surveillance, the current selection criteria recommended and in use, the incidence of active surveillance, barriers existing to its uptake and future developments in patient selection.

Development and External Validation of the STRATified CANcer Surveillance (STRATCANS) Multivariable Model for Predicting Progression in Men with Newly Diagnosed Prostate Cancer Starting Active Surveillance

For men with newly diagnosed prostate cancer, we aimed to develop and validate a model to predict the risk of progression on active surveillance (AS), which could inform more personalised AS strategies. In total, 883 men from 3 European centres were used for model development and internal validation, and 151 men from a fourth European centre were used for external validation. Men with Cambridge Prognostic Group (CPG) 1-2 disease at diagnosis were eligible. The endpoint was progression to the composite endpoint of CPG3 disease or worse (≥CPG3). Model performance at 4 years was evaluated through discrimination (C-index), calibration plots, and decision curve analysis. The final multivariable model incorporated prostate-specific antigen (PSA), Grade Group, magnetic resonance imaging (MRI) score (Prostate Imaging Reporting & Data System (PI-RADS) or Likert), and prostate volume. Calibration and discrimination were good in both internal validation (C-index 0.742, 95% CI 0.694-0.793) and external validation (C-index 0.845, 95% CI 0.712-0.958). In decision curve analysis, the model offered net benefit compared to a 'follow-all' strategy at risk thresholds of ≥0.08 and ≥0.04 in development and external validation, respectively. In conclusion, our model demonstrated good accuracy and clinical utility in predicting the progression on AS at 4 years post-diagnosis. Men with lower risk predictions could subsequently be offered less-intense surveillance. Further external validation in larger cohorts is now required.

Prostate Multiparametric MRI: Evaluation of Recurrence and Post-treatment Changes

Purpose of Review

This article reviews all the most common therapeutic strategies of prostate cancer, systemic or local, and all the following morpho-structural alterations, with the aim of helping the radiologist to recognize the signs of recurrence by using mp-MRI.

Recent Findings

According to the most recent evidences, prostate mp-MRI has now become a strong, non-invasive, and valid tool to evaluate all patient treated for prostatic carcinoma across the time, especially in the suspicion of biochemical recurrence.

Summary

The minimal signs of focal recurrence can put a strain on radiologists, especially if they are novice with multi-parametric prostate MRI. Familiarizing themselves with the outcomes of treatment, local or systemic, and its characteristics to MR imaging is indispensable to avoid diagnostic pitfalls and, subsequently, unnecessary reinterventions.

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.

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility. We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (±0.11) compared to 0.70 (±0.15) for landmarking Cox and 0.67 (±0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years. In summary, we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time.

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.

Could 68Ga-PSMA PET/CT Evaluation Reduce the Number of Scheduled Prostate Biopsies in Men Enrolled in Active Surveillance Protocols?

Background: To evaluate the accuracy of 68Ga-prostate specific membrane antigen (PSMA) PET/CT in the diagnosis of clinically significant prostate cancer (csPCa) (Grade Group > 2) in men enrolled in Active Surveillance (AS) protocol. Methods: From May 2013 to May 2021, 173 men with very low-risk PCa were enrolled in an AS protocol study. During the follow-up, 38/173 (22%) men were upgraded and 8/173 (4.6%) decided to leave the AS protocol. After four years from confirmatory biopsy (range: 48−52 months), 30/127 (23.6%) consecutive patients were submitted to mpMRI and 68Ga-PSMA PET/CT scan before scheduled repeated biopsy. All the mpMRI (PI-RADS > 3) and 68Ga-PET/TC standardised uptake value (SUVmax) > 5 g/mL index lesions underwent targeted cores (mpMRI-TPBx and PSMA-TPBx) combined with transperineal saturation prostate biopsy (SPBx: median 20 cores). Results: mpMRI and 68Ga-PSMA PET/CT showed 14/30 (46.6%) and 6/30 (20%) lesions suspicious for PCa. In 2/30 (6.6%) men, a csPCa was found; 68Ga-PSMA-TPBx vs. mpMRI-TPBx vs. SPBx diagnosed 1/2 (50%) vs. 1/2 (50%) vs. 2/2 (100%) csPCa, respectively. In detail, mpMRI and 68Ga-PSMA PET/TC demonstrated 13/30 (43.3%) vs. 5/30 (16.7%) false positive and 1 (50%) vs. 1 (50%) false negative results. Conclusion: 68Ga-PSMA PET/CT did not improve the detection for csPCa of SPBx but would have spared 24/30 (80%) scheduled biopsies showing a lower false positive rate in comparison with mpMRI (20% vs. 43.3%) and a negative predictive value of 85.7% vs. 57.1%, respectively.

How MRI is changing prostate cancer management: a focus on early detection and active surveillance: Comment l'IRM est en train de révolutionner la prise en charge du cancer de la prostate : focus sur la détection précoce et la surveillance active

INTRODUCTION

The last decade has witnessed major changes in prostate cancer management. Among these, the advent of magnetic resonance imaging (MRI), by allowing the visualisation of the cancerous lesion inside the prostatic gland, opened new management horizons.

MATERIAL AND METHODS

We conducted a narrative review of the literature published since 2010, focusing on the place of MRI in the early detection, active surveillance and prostate cancer screening settings.

RESULTS

Multiparametric magnetic resonance imaging (mpMRI), interpreted using the PI-RADS scoring system, has allowed a shift from systematic to mpMRI-targeted biopsies, supported by level I evidence. Studies are ongoing to evaluate the role of MRI as a triage and screening tool. The integration of mpMRI has allowed for a better selection of active surveillance candidates, reducing the risk of misclassification. The PRECISE recommendations have been created to assess the likelihood of radiological change over time from the previous or baseline mpMRI scan, and serial mpMRI appears promising to reduce the need for repeat biopsy in active surveillance.

CONCLUSION

Growing evidence supports the use of MRI at all stages of the prostate cancer pathway, relying on images of optimal diagnostic quality and experience in prostate MRI reporting and biopsy targeting. © 2022 Elsevier Masson SAS. All rights reserved.

The current role of MRI for guiding active surveillance in prostate cancer

Active surveillance (AS) is the recommended treatment option for low-risk and favourable intermediate-risk prostate cancer management, preserving oncological and functional outcomes. However, active monitoring using relevant parameters in addition to the usual clinical, biological and pathological considerations is necessary to compensate for initial undergrading of the tumour or to detect early progression without missing the opportunity to provide curative therapy. Indeed, several studies have raised concerns about inadequate biopsy sampling at diagnosis. However, the implementation of baseline MRI and targeted biopsy have led to improved initial stratification of low-risk disease; baseline MRI correlates well with disease characteristics and AS outcomes. The use of follow-up MRI during the surveillance phase also raises the question of the requirement for serial biopsies in the absence of radiological progression and the possibility of using completely MRI-based surveillance, with triggers for biopsies based solely on MRI findings. This concept of a tailored-risk, imaging-based monitoring strategy is aimed at reducing invasive procedures. However, the abandonment of serial biopsies in the absence of MRI progression can probably not yet be recommended in routine practice, as the data from real-life cohorts are heterogeneous and inconclusive. Thus, the evolution towards a routine, fully MRI-guided AS pathway has to be preceded by ensuring quality programme assessment for MRI reading and by demonstrating its safety in prospective trials.

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

OBJECTIVE

To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression.

METHODS

This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity.

RESULTS

60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (-11.7% and -9.5%) compared to non-progressors (-6.1% and -1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67.

CONCLUSION

AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression.

ADVANCES IN KNOWLEDGE

This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (-10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.

Optimal biopsy approach for detection of clinically significant prostate cancer

Prostate cancer (PCa) diagnostic and therapeutic work-up has evolved significantly in the last decade, with pre-biopsy multiparametric MRI now widely endorsed within international guidelines. There is potential to move away from the widespread use of systematic biopsy cores and towards an individualised risk-stratified approach. However, the evidence on the optimal biopsy approach remains heterogeneous, and the aim of this review is to highlight the most relevant features following a critical assessment of the literature. The commonest biopsy approaches are via the transperineal (TP) or transrectal (TR) routes. The former is considered more advantageous due to its negligible risk of post-procedural sepsis and reduced need for antimicrobial prophylaxis; the more recent development of local anaesthetic (LA) methods now makes this approach feasible in the clinic. Beyond this, several techniques are available, including cognitive registration, MRI-Ultrasound fusion imaging and direct MRI in-bore guided biopsy. Evidence shows that performing targeted biopsies reduces the number of cores required and can achieve acceptable rates of detection whilst helping to minimise complications and reducing pathologist workloads and costs to health-care facilities. Pre-biopsy MRI has revolutionised the diagnostic pathway for PCa, and optimising the biopsy process is now a focus. Combining MR imaging, TP biopsy and a more widespread use of LA in an outpatient setting seems a reasonable solution to balance health-care costs and benefits, however, local choices are likely to depend on the expertise and experience of clinicians and on the technology available.

Integration of Prostate Biopsy Results with Pre-Biopsy Multiparametric Magnetic Resonance Imaging Findings Improves Local Staging of Prostate Cancer

PURPOSE

To assess the added value of histological information for local staging of prostate cancer (PCa) by comparing the accuracy of multiparametric MRI alone (mpMRI) and mpMRI with biopsy Gleason grade (mpMRI+Bx).

METHODS

133 consecutive patients who underwent preoperative 3T-MRI and subsequent radical prostatectomy for PCa were included in this single-centre retrospective study. mpMRI imaging was reviewed independently by two uroradiologists for the presence of extracapsular extension (ECE) and seminal vesicle invasion (SVI) on a 5-point Likert scale. For second reads, the radiologists received results of targeted fused MR/US biopsy (mpMRI+Bx) prior to re-staging.

RESULTS

The median patient age was 63 years (interquartile range (IQR) 58-67 years) and median PSA was 6.5 ng/mL (IQR 5.0-10.0 ng/mL). Extracapsular extension was present in 85/133 (63.9%) patients and SVI was present in 22/133 (16.5%) patients. For ECE prediction, mpMRI showed sensitivity and specificity of 63.5% and 81.3%, respectively, compared to 77.7% and 81.3% achieved by mpMRI+Bx. At an optimal cut-off value of Likert score ≥ 3, areas under the curves (AUCs) was .85 for mpMRI+Bx and .78 for mpMRI, P < .01. For SVI prediction, AUC was .95 for mpMRI+Bx compared to .92 for mpMRI; P = .20. Inter-reader agreement for ECE and SVI prediction was substantial for mpMRI (k range, .78-.79) and mpMRI+Bx (k range, .74-.79).

CONCLUSIONS

MpMRI+Bx showed superior diagnostic performance with an increased sensitivity for ECE prediction but no significant difference for SVI prediction. Inter-reader agreement was substantial for both protocols. Integration of biopsy information adds value when staging prostate mpMRI.

Hyperpolarised 13C-MRI identifies the emergence of a glycolytic cell population within intermediate-risk human prostate cancer

Hyperpolarised magnetic resonance imaging (HP 13C-MRI) is an emerging clinical technique to detect [1-13C]lactate production in prostate cancer (PCa) following intravenous injection of hyperpolarised [1-13C]pyruvate. Here we differentiate clinically significant PCa from indolent disease in a low/intermediate-risk population by correlating [1-13C]lactate labelling on MRI with the percentage of Gleason pattern 4 (%GP4) disease. Using immunohistochemistry and spatial transcriptomics, we show that HP 13C-MRI predominantly measures metabolism in the epithelial compartment of the tumour, rather than the stroma. MRI-derived tumour [1-13C]lactate labelling correlated with epithelial mRNA expression of the enzyme lactate dehydrogenase (LDHA and LDHB combined), and the ratio of lactate transporter expression between the epithelial and stromal compartments (epithelium-to-stroma MCT4). We observe similar changes in MCT4, LDHA, and LDHB between tumours with primary Gleason patterns 3 and 4 in an independent TCGA cohort. Therefore, HP 13C-MRI can metabolically phenotype clinically significant disease based on underlying metabolic differences in the epithelial and stromal tumour compartments.

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

OBJECTIVES

To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS).

METHODS

The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test.

RESULTS

The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77).

CONCLUSIONS

PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients.

KEY POINTS

• The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Role of multi-parametric magnetic resonance imaging fusion biopsy in active surveillance of prostate cancer: a systematic review

Background:

Our goal is to review current literature regarding the role of multi-parametric magnetic resonance imaging (mpMRI) in the active surveillance (AS) of prostate cancer (PCa) and identify trends in rate of reclassification of risk category, performance of fusion biopsy (FB) versus systematic biopsy (SB), and progression-free survival.

Methods:

We performed a comprehensive literature search in PubMed and identified 121 articles. A narrative summary was performed.

Results:

Thirty-two articles were chosen to be featured in this review. SB and FB are complementary in detecting higher-grade disease in follow-up. While FB was more likely than SB to detect clinically significant disease, FB missed 6.4–11% of clinically significant disease. Imaging factors that predicted upgrading include number of lesions on magnetic resonance imaging (MRI), lesion density, and MRI suspicion level.

Conclusion:

Incorporating mpMRI FB in conjunction with SB should be part of contemporary AS protocols. mpMRI should additionally be used routinely for follow-up; however, mpMRI is not currently sensitive enough in detecting disease progression to replace biopsy in the surveillance protocol.

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer

PURPOSE

⁶⁸ Ga-PSMA PET/CT has high specificity and sensitivity for the detection of both intraprostatic tumor focal lesions and metastasis. However, approximately 10% of primary prostate cancer are invisible on PSMA-PET (exhibit no or minimal uptake). In this work, we investigated whether machine learning-based radiomics models derived from PSMA-PET images could predict invisible intraprostatic lesions on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer.

METHODS

In this retrospective study, patients with or without prostate cancer who underwent ⁶⁸ Ga-PSMA PET/CT and presented negative on PSMA-PET image at either of two different institutions were included: institution 1 (between 2017 and 2020) for the training set and institution 2 (between 2019 and 2020) for the external test set. Three random forest (RF) models were built using selected features extracted from standard PET images, delayed PET images, and both standard and delayed PET images. Then, subsequent tenfold cross-validation was performed. In the test phase, the three RF models and PSA density (PSAD, cut-off value: 0.15 ng/ml/ml) were tested with the external test set. The area under the receiver operating characteristic curve (AUC) was calculated for the models and PSAD. The AUCs of the radiomics model and PSAD were compared.

RESULTS

A total of 64 patients (39 with prostate cancer and 25 with benign prostate disease) were in the training set, and 36 (21 with prostate cancer and 15 with benign prostate disease) were in the test set. The average AUCs of the three RF models from tenfold cross-validation were 0.87 (95% CI: 0.72, 1.00), 0.86 (95% CI: 0.63, 1.00), and 0.91 (95% CI: 0.69, 1.00), respectively. In the test set, the AUCs of the three trained RF models and PSAD were 0.903 (95% CI: 0.830, 0.975), 0.856 (95% CI: 0.748, 0.964), 0.925 (95% CI:0.838, 1.00), and 0.662 (95% CI: 0.510, 0.813). The AUCs of the three radiomics models were higher than that of PSAD (0.903, 0.856, and 0.925 vs. 0.662, respectively; P = .007, P = .045, and P = .005, respectively).

CONCLUSION

Random forest models developed by ⁶⁸ Ga-PSMA-11 PET-based radiomics features were proven useful for accurate prediction of invisible intraprostatic lesion on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer and showed better diagnostic performance compared with PSAD.

Repeat MRI during active surveillance: natural history of prostatic lesions and upgrading rates

OBJECTIVES

To assess upgrading rates in patients on active surveillance (AS) for prostate cancer (PCa) after serial multiparametric magnetic resonance imaging (mpMRI).

METHODS

We conducted a retrospective analysis of 558 patients. Five different criteria for mpMRI progression were used: 1) a Prostate Imaging Reporting and Data System (PI-RADS) score increase; 2) a lesion size increase; 3) an extraprostatic extension score increase; 4) overall mpMRI progression; and 5) the number of criteria met for mpMRI progression (0 vs 1 vs 2-3). In addition, two definitions of PCa upgrading were evaluated: 1) International Society of Urological Pathology Grade Group (ISUP GG) ≥2 with >10% of pattern 4 and 2) ISUP GG ≥ 3. Estimated annual percent changes methodology was used to show the temporal trends of mpMRI progression criteria. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of mpMRI progression criteria were also analysed. Multivariable logistic regression models tested PCa upgrading rates.

RESULTS

Lower rates over time for all mpMRI progression criteria were observed. The NPV of serial mpMRI scans ranged from 90.5% to 93.5% (ISUP GG≥2 with >10% of pattern 4 PCa upgrading) and from 98% to 99% (ISUP GG≥3 PCa upgrading), depending on the criteria used for mpMRI progression. A prostate-specific antigen density (PSAD) threshold of 0.15 ng/mL/mL was used to substratify those patients who would be able to skip a prostate biopsy. In multivariable logistic regression models assessing PCa upgrading rates, all five mpMRI progression criteria achieved independent predictor status.

CONCLUSION

During AS, approximately 27% of patients experience mpMRI progression at first repeat MRI. However, the rates of mpMRI progression decrease over time at subsequent mpMRI scans. Patients with stable mpMRI findings and with PSAD < 0.15 ng/mL/mL could safely skip surveillance biopsies. Conversely, patients who experience mpMRI progression should undergo a prostate biopsy.

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T₂-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481-0.743) to 0.75 (95% CI 0.64-0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

Evaluation of image-based prognostic parameters of post-prostatectomy urinary incontinence: A literature review

Prostate cancer is the second most common male cancer, and radical prostatectomy is a highly effective treatment for intermediate and high-risk disease. However, post-prostatectomy urinary incontinence remains a major functional side-effect in patients undergoing radical prostatectomy. Despite recent improvements in preoperative imaging quality and surgical techniques, it remains challenging to predict or prevent occurrence of this complication. The aim of this research was to review the current published literature on pre- and postoperative imaging evaluation of the prostate and pelvic structures, to identify added value in the prediction of post-prostatectomy urinary incontinence. A computerized bibliographic search of the PubMed library was carried out to identify imaging-based articles evaluating the pelvic floor and surrounding structures pre- and/or postradical prostatectomy to predict post-prostatectomy urinary incontinence. A total of 32 articles were included. Of these, 29 papers assessed the importance of magnetic resonance imaging evaluation, with a total of 16 parameters evaluated. The most common parameters were intravesical protrusion, the membranous urethral length, prostatic volume and periurethral fibrosis. Preoperative membranous urethral length and its preservation after surgery showed the strongest correlation with urinary incontinence. Three studies evaluated ultrasound, with all carried out postoperatively. This technique benefits from a dynamic evaluation, and the results are promising for proximal urethral hypermobility and the degree of bladder neck funneling on the Valsalva maneuver. Several imaging studies evaluated the predictors of post-prostatectomy urinary incontinence, with preoperative membranous urethral length offering the most promise. However, the current literature is limited by the single-center nature of studies, and the heterogeneity in patient populations and methodologies used.

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.

Reproducibility of magnetic resonance fingerprinting-based T1 mapping of the healthy prostate at 1.5 and 3.0 T: A proof-of-concept study

Facilitating clinical translation of quantitative imaging techniques has been suggested as means of improving interobserver agreement and diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) of the prostate. One such technique, magnetic resonance fingerprinting (MRF), has significant competitive advantages over conventional mapping techniques in terms of its multi-site reproducibility, short scanning time and inherent robustness to motion. It has also been shown to improve the detection of clinically significant prostate cancer when added to standard mpMRI sequences, however, the existing studies have all been conducted on 3.0 T MRI systems, limiting the technique's use on 1.5 T MRI scanners that are still more widely used for prostate imaging across the globe. The aim of this proof-of-concept study was, therefore, to evaluate the cross-system reproducibility of prostate MRF T1 in healthy volunteers (HVs) using 1.5 and 3.0 T MRI systems. The initial validation of MRF T1 against gold standard inversion recovery fast spin echo (IR-FSE) T1 in the ISMRM/NIST MRI system revealed a strong linear correlation between phantom-derived MRF and IR-FSE T1 values was observed at both field strengths (R2 = 0.998 at 1.5T and R2 = 0.993 at 3T; p = < 0.0001 for both). In young HVs, inter-scanner CVs demonstrated marginal differences across all tissues with the highest difference of 3% observed in fat (2% at 1.5T vs 5% at 3T). At both field strengths, MRF T1 could confidently differentiate prostate peripheral zone from transition zone, which highlights the high quantitative potential of the technique given the known difficulty of tissue differentiation in this age group. The high cross-system reproducibility of MRF T1 relaxometry of the healthy prostate observed in this preliminary study, therefore, supports the technique's prospective clinical validation as part of larger trials employing 1.5 T MRI systems, which are still widely used clinically for routine mpMRI of the prostate.