Deep-learning Evaluation for Enhanced Prognostics - PSMA PET¶

News¶

Welcome to the DEEP-PSMA challenge. We are pleased to announce that we have been selected as one of the MICCAI 2025 registered challenges. Registration and data release for this challenge to commence April 2025. Please be patient as we finalise our internal data review and access terms.

In the meantime please take a moment to familiarise with the Dataset and Dataset Format pages and feel free to reach out with any questions.

Introduction¶

The discovery of the molecular target, prostate-specific membrane antigen (PSMA), has enabled new techniques to both image and target metastatic castration-resistant prostate cancer (mCRPC). Three FDA approved tracers for Positron Emission Tomography (PET) imaging - 68Ga-PSMA-11, 18F-DCFPyL and 18F-rhPSMA-7 - now enable more accurate detection of metastatic spread throughout the body and also selection of patients for radioligand treatment.

Treatment of mCRPC with the radiopharmaceutical 177Lu-PSMA-617 (LuPSMA) presents opportunities to improve quality-of-life and life expectancy in men who have progressed after receiving chemotherapy or hormone therapies. The likelihood of response to LuPMSA treatment is related to the ability to concentrate therapeutic radiation into prostate cancer cells; a factor related to the expression of PSMA receptors in target tissues which can be quantified by PSMA PET imaging. Quantitative imaging biomarkers from PSMA and FDG imaging have been shown to be predictive of response to 177Lu-PSMA therapy.

We host the DEEP-PSMA (Deep-learning Evaluation for Enhanced Prognostics - Prostate Specific Membrane Antigen) challenge to benchmark artificial intelligence models for marking up the extent of disease on PSMA PET/CT and FDG PET/CT in order to automate scoring quantitative biomarkers relevant for targeted radionuclide therapy.

DEEP-PSMA will be hosted as part of the MICCAI 2025 conference and is supported by the The Prostate Theranostics and Imaging Centre of Excellence (ProsTIC) at the Peter MacCallum Cancer Centre, Australia and through the Prostate Cancer Foundation (PCF).

Task¶

Automatic whole-body tumor lesion segmentation in both PSMA PET/CT and FDG PET/CT imaging on a database of 100 patients (training database) managed at a single centre:

• Accurate total-body tumour burden segmentation

• Avoidance of false positive regions (liver*, kidneys, brain, bladder, salivary glands)

• Potential utilisation of paired image data (PSMA PET/CT with FDG PET/CT data)

Testing will be performed on 45 (held-out final test set) all originating from the same clinic as the training database.

Publications involving manual annotation of total tumour volume for determination of predictive biomarkers in mCRPC can be found in Ferdinandus et al. and Buteau et al..