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In Canada, prostate cancer is the most common form of cancer in men andaccounted for 20% of new cancer cases for this demographic in 2022. Due torecent successes in leveraging machine learning for clinical decision support,there has been significant interest in the development of deep neural networksfor prostate cancer diagnosis, prognosis, and treatment planning usingdiffusion weighted imaging (DWI) data. A major challenge hindering widespreadadoption in clinical use is poor generalization of such networks due toscarcity of large-scale, diverse, balanced prostate imaging datasets fortraining such networks. In this study, we explore the efficacy of latentdiffusion for generating realistic prostate DWI data through the introductionof an anatomic-conditional controlled latent diffusion strategy. To the best ofthe authors' knowledge, this is the first study to leverage conditioning forsynthesis of prostate cancer imaging. Experimental results show that theproposed strategy, which we call Cancer-Net PCa-Gen, enhances synthesis ofdiverse prostate images through controllable tumour locations and betteranatomical and textural fidelity. These crucial features make it well-suitedfor augmenting real patient data, enabling neural networks to be trained on amore diverse and comprehensive data distribution. The Cancer-Net PCa-Genframework and sample images have been made publicly available athttps://www.kaggle.com/datasets/deetsadi/cancer-net-pca-gen-dataset as a partof a global open-source initiative dedicated to accelerating advancement inmachine learning to aid clinicians in the fight against cancer.

Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion