ProCAncer-I - An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer’s Continuum

Background

In Europe, prostate cancer (PC) is the second most frequent type of cancer in men and the third most lethal. Current clinical practices lead to overdiagnosis and overtreatment, necessitating more effective tools for discriminating between aggressive and non-aggressive disease. The EU-funded ProCAncer-I project proposes to develop advanced artificial intelligence models to address unmet clinical needs: diagnosis, metastases detection and prediction of response to treatment. To achieve this, partners will generate a large interoperable repository of health images, and a scalable high performance computing platform hosting the largest collection of PC Magnetic Resonance Images used for developing robust PC AI models. To ensure the rapid clinical implementation of the models developed, the project's partners will robustly monitor performance, accuracy and reproducibility.

Funding

People

Jasper Twilt

Jasper Twilt

PhD Candidate

Radboud University Medical Center

Anindo Saha

Anindo Saha

PhD Candidate

Maarten de Rooij

Maarten de Rooij

Radiologist

Radboud University Medical Center

Henkjan Huisman

Henkjan Huisman

Associate Professor

Jurgen Fütterer

Jurgen Fütterer

Interventional Radiologist & Professor

Radboud University Medical Center

Publications

  • A. Saha, M. Hosseinzadeh and H. Huisman, "End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction", Medical Image Analysis, 2021:102155.
  • J. Twilt, K. van Leeuwen, H. Huisman, J. Fütterer and M. de Rooij, "Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review", Diagnostics, 2021;11(6).
  • A. Saha, M. Hosseinzadeh and H. Huisman, "Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI", Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
  • A. Saha, J. Bosma, J. Linmans, M. Hosseinzadeh and H. Huisman, "Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI —Should Different Clinical Objectives Mandate Different Loss Functions?", Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
  • A. Saha, J. Bosma, C. Roest, M. Hosseinzadeh, J. Fütterer and H. Huisman, "Deep Learning with Bayesian Inference for Prostate Cancer Diagnosis across Longitudinal Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.