600 prostate multiparametric MRI studies with PI-RADS v2.1 scores and lesion segmentation

Overview

We are requesting a curated cohort of prostate multiparametric MRI (mpMRI) examinations to develop an AI-assisted detection and characterisation tool for clinically significant prostate cancer (csPCa, defined as Gleason grade group ≥2, equivalent to Gleason score ≥3+4=7). Each study must be acquired on a 3T scanner using a pelvic phased-array surface coil with ≥16 elements, or alternatively an endorectal coil with external pelvic array, and must include all three standard mpMRI components as specified in the PI-RADS v2.1 technical guidelines. T2-weighted imaging (T2WI) must be acquired in axial, coronal, and sagittal planes with slice thickness ≤3 mm, in-plane resolution ≤0.4 × 0.4 mm, TR ≥3000 ms, and TE 100–120 ms. Diffusion-weighted imaging (DWI) must include a minimum of b-values 0, 500, and 1000 s/mm², plus a computed or directly acquired high-b image at b=1400–2000 s/mm², and the corresponding apparent diffusion coefficient (ADC) map generated from the b0 and b1000 images. ADC maps are the dominant DWI parameter for peripheral zone scoring under PI-RADS v2.1 and must therefore be computed without signal-to-noise smoothing artefacts. Dynamic contrast-enhanced (DCE) imaging with intravenous gadolinium-based contrast agent (standard extracellular agent at 0.1 mmol/kg) must have temporal resolution ≤15 seconds per volume and total acquisition duration ≥5 minutes post-injection. MR spectroscopy (MRS) is optional and will be included as bonus data. Primary delivery format is DICOM with unmodified pixel data and sequence headers intact; NIfTI-2 with JSON metadata sidecars is also accepted and preferred for institutions already running BIDS-compatible research workflows. Each case must be annotated by a radiologist who reads ≥100 prostate mpMRI studies per year and is trained in PI-RADS v2.1 scoring. A PI-RADS score from 1 to 5 must be assigned per lesion with the dominant sequence scoring stated explicitly. For all PI-RADS 3–5 index lesions, a 3D segmentation mask delineated on the axial T2WI is required; a co-registered segmentation mask on the ADC map is strongly preferred to enable model training on both sequences simultaneously. Whole-gland prostate segmentation and zonal anatomy segmentation delineating the transition zone and peripheral zone are optional but will be purchased at a premium per case. Where available, MRI-TRUS fusion biopsy results including Gleason grade group and biopsy core location mapped to the sector model, or radical prostatectomy whole-mount pathology with sector correlation, should be provided as structured JSON or CSV metadata linked to the MRI lesion annotation. Required clinical fields include patient age, PSA level at time of MRI, PSA density (PSA divided by prostate volume), prostate volume on MRI, and prior prostate biopsy history. All data must be fully de-identified and defaced per GDPR Article 89 research exemption requirements and the DICOM PS3.15 confidentiality profile. Quality exclusion criteria include severe motion artefact on DWI, endorectal coil failure causing anterior gland signal loss, and b-value miscalculation producing erroneous ADC values outside the physiological range of 500–2000 µm²/s. This dataset will serve as training and validation data for a prostate cancer detection algorithm targeting radiologist workflow integration across European urology and radiology centres, with intended CE marking under MDR 2017/745 as a Class IIa medical device software. Cases from patients with prior treatment including external beam radiotherapy, brachytherapy, HIFU, or focal laser ablation should be flagged but are still included and scientifically valuable for post-treatment recurrence detection model development.

Progress

Data Specifications

Use Cases

• Training and validating Medical imaging AI/ML models
• Benchmarking Medical imaging detection and segmentation algorithms
• Building de-identified Medical imaging research datasets for academic studies
• Augmenting existing Medical imaging datasets to reduce class imbalance