Top Sector Life Sciences & Health (LSH) entails a broad scope of disciplines, from pharmaceuticals to medical technology and from healthcare infrastructure to vaccination. To realise its mission – vital citizens in a healthy economy - the Top Sector builds on the strengths of the Dutch LSH sector to address the biggest societal challenges in prevention, cure and care. By funding multidisciplinary public-private partnerships (PPPs) the Top Sector aims to facilitate innovation. Here we give an overview of  a number of funded R&D projects by Top Sector LSH. The page is updated continuously.

Improved Prostate Cancer Management using Imaging Data and Machine Learning

Personalised Prostate Cancer Management using Multi-parametric MRI and Machine Learning

In this project, Erasmus MC, Quantib and NKI join forces to improve clinical decision making in patients with prostate cancer, using advanced analysis with machine learning techniques on MR images of the prostate. Prostate cancer is the most prevalent form of cancer in men and the second leading cause of cancer death.

Since the prognosis of the disease of patients with high grade prostate cancer is much worse than for patients with low grade prostate cancer (most patients die with, and not due to cancer), accurate discrimination between high- and low-grade prostate cancer is of utmost importance for patients and clinicians to decide on treatment options and prognosis.

In order to improve the management of patients with prostate cancer, improved prognostic models are required that stratify patients into those that will benefit from additional diagnostic procedures and invasive treatments and those that do not. In some patients it may be better to wait and put the patient on surveillance with follow-up MRI scans, while in other cases a biopsy and possibly subsequent treatment is required. For optimal patient stratification all available patient and diagnostic information in different stages of the diagnostic and therapeutic process should be utilised, and there should be optimal use of knowledge that can be derived from previously seen patients.

It is the hypothesis of this project that this can be achieved by applying state-of-the-art machine learning techniques on prospectively acquired multicenter datasets available at Erasmus MC and NKI. The overall goal of this project is to develop a new diagnostic and prognostic tool, called PPCM4, for accurate tumor volume estimation and tumor characterisation of low-grade prostatic cancer, to improve patient outcome through precise decisions (selecting the right treatment strategy, surveillance or active treatment, for the right patient).

This collaboration project is co-funded by the PPP Allowance made available by Health~Holland, Top Sector Life Sciences & Health, to EMC to stimulate public-private partnerships. For questions, please contact EMC directly via the following email address