The ReIMAGINE prostate cancer risk study protocol: A prospective cohort study in men with a suspicion of prostate cancer who are referred onto an MRI-based diagnostic pathway with donation of tissue, blood and urine for biomarker analyses

INTRODUCTION: The ReIMAGINE Consortium was conceived to develop risk-stratification models that might incorporate the full range of novel prostate cancer (PCa) diagnostics (both commercial and academic). METHODS: ReIMAGINE Risk is an ethics approved (19/LO/1128) multicentre, prospective, observational cohort study which will recruit 1000 treatment-naive men undergoing a multi-parametric MRI (mpMRI) due to an elevated PSA (≤20ng/ml) or abnormal prostate examination who subsequently had a suspicious mpMRI (score≥3, stage ≤T3bN0M0). Primary outcomes include the detection of ≥Gleason 7 PCa at baseline and time to clinical progression, metastasis and death. Baseline blood, urine, and biopsy cores for fresh prostate tissue samples (2 targeted and 1 non-targeted) will be biobanked for future analysis. High-resolution scanning of pathology whole-slide imaging and MRI-DICOM images will be collected. Consortium partners will be granted access to data and biobanks to develop and validate biomarkers using correlation to mpMRI, biopsy-based disease status and long-term clinical outcomes. RESULTS: Recruitment began in September 2019(n = 533). A first site opened in September 2019 (n = 296), a second in November 2019 (n = 210) and a third in December 2020 (n = 27). Acceptance to the study has been 65% and a mean of 36.5ml(SD+/-10.0), 12.9ml(SD+/-3.7) and 2.8ml(SD+/-0.7) urine, plasma and serum donated for research, respectively. There are currently 4 academic and 15 commercial partners spanning imaging (~9 radiomics, artificial intelligence/machine learning), fluidic (~3 blood-based and ~2urine-based) and tissue-based (~1) biomarkers. CONCLUSION: The consortium will develop, or adjust, risk models for PCa, and provide a platform for evaluating the role of novel diagnostics in the era of pre-biopsy MRI and targeted biopsy

External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images

Introduction: State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop " to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images.Methods: Eighty-five biopsy proven prostate cancer patients who underwent Ga-68 PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at and data processing has been done in agreement with the reference work.Results: When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring).Discussion: In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice

Prevalence of MRI lesions in men responding to a GP-led invitation for a prostate health check: a prospective cohort study

OBJECTIVE: In men with a raised prostate-specific antigen (PSA), MRI increases the detection of clinically significant cancer and reduces overdiagnosis, with fewer biopsies. MRI as a screening tool has not been assessed independently of PSA in a formal screening study. We report a systematic community-based assessment of the prevalence of prostate MRI lesions in an age-selected population. METHODS AND ANALYSIS: Men aged 50–75 were identified from participating general practice (GP) practices and randomly selected for invitation to a screening MRI and PSA. Men with a positive MRI or a raised PSA density (≥0.12 ng/mL2) were recommended for standard National Health Service (NHS) prostate cancer assessment. RESULTS: Eight GP practices sent invitations to 2096 men. 457 men (22%) responded and 303 completed both screening tests. Older white men were most likely to respond to the invitation, with black men having 20% of the acceptance rate of white men. One in six men (48/303 men, 16%) had a positive screening MRI, and an additional 1 in 20 men (16/303, 5%) had a raised PSA density alone. After NHS assessment, 29 men (9.6%) were diagnosed with clinically significant cancer and 3 men (1%) with clinically insignificant cancer. Two in three men with a positive MRI, and more than half of men with clinically significant disease had a PSA <3 ng/mL. CONCLUSIONS: Prostate MRI may have value in screening independently of PSA. These data will allow modelling of the use of MRI as a primary screening tool to inform larger prostate cancer screening studies. TRIAL REGISTRATION NUMBER: NCT04063566

Quantification of Prostate Cancer Metabolism Using 3D Multiecho bSSFP and Hyperpolarized [1-13 C] Pyruvate: Metabolism Differs Between Tumors of the Same Gleason Grade

BACKGROUND: Three-dimensional (3D) multiecho balanced steady-state free precession (ME-bSSFP) has previously been demonstrated in preclinical hyperpolarized (HP) 13 C-MRI in vivo experiments, and it may be suitable for clinical metabolic imaging of prostate cancer (PCa). PURPOSE: To validate a signal simulation framework for the use of sequence parameter optimization. To demonstrate the feasibility of ME-bSSFP for HP 13 C-MRI in patients. To evaluate the metabolism in PCa measured by ME-bSSFP. STUDY TYPE: Retrospective single-center cohort study. PHANTOMS/POPULATION: Phantoms containing aqueous solutions of [1-13 C] lactate (2.3 M) and [13 C] urea (8 M). Eight patients (mean age 67 ± 6 years) with biopsy-confirmed Gleason 3 + 4 (n = 7) and 4 + 3 (n = 1) PCa. FIELD STRENGTH/SEQUENCES: 1 H MRI at 3 T with T2 -weighted turbo spin-echo sequence used for spatial localization and spoiled dual gradient-echo sequence used for B0 -field measurement. ME-bSSFP sequence for 13 C MR spectroscopic imaging with retrospective multipoint IDEAL metabolite separation. ASSESSMENT: The primary endpoint was the analysis of pyruvate-to-lactate conversion in PCa and healthy prostate regions of interest (ROIs) using model-free area under the curve (AUC) ratios and a one-directional kinetic model (kP ). The secondary objectives were to investigate the correlation between simulated and experimental ME-bSSFP metabolite signals for HP 13 C-MRI parameter optimization. STATISTICAL TESTS: Pearson correlation coefficients with 95% confidence intervals and paired t-tests. The level of statistical significance was set at P  0.96). Therefore, the simulation framework was used for sequence optimization. Whole prostate metabolic HP 13 C-MRI, observing the conversion of pyruvate into lactate, with a temporal resolution of 6 seconds was demonstrated using ME-bSSFP. Both assessed metrics resulted in significant differences between PCa (mean ± SD) (AUC = 0.33 ± 012, kP  = 0.038 ± 0.014) and healthy (AUC = 0.15 ± 0.10, kP  = 0.011 ± 0.007) ROIs. DATA CONCLUSION: Metabolic HP 13 C-MRI in the prostate using ME-bSSFP allows for differentiation between aggressive PCa and healthy tissue. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

: The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation