Background
One in eight men is confronted with prostate cancer (PCa). The current diagnostic trajectory has many shortcomings. The Prostate-Specific Antigen (PSA) blood test has too many false alarms, causing unnecessary biopsies and missing treatable clinically significant PCa (csPCa). Ultrasound (US)‐guided biopsies are inadequate as a follow‐up to the PSA test. Due to their unreliability, these biopsies frequently result in either overdiagnosis or underdiagnosis, complicating treatment decisions. As a result, early detection is unfeasible, and prostate cancer is often detected only at a late, possibly fatal stage. Research into timely, accurate, widely available diagnostic modalities is therefore crucial. Recently, multiparametric magnetic resonance imaging (mpMRI) has evidenced clinical value in reducing unnecessary biopsies. However, MRI is costly and requires expertise both during reading and biopsy. Current MRI-US fusion biopsy improves over US-guided biopsies but still face difficulties. New technology research can result in a breakthrough. Firstly, novel US parameters show increased diagnostic accuracy, evidencing the potential for multiparametric US (mpUS) to become a valuable, cost-effective option for diagnosis and biopsy guidance. Secondly, artificial intelligence (AI) is emerging as a powerful tool to enhance medical image interpretation and reduce diagnostic and interventional workflow and costs. However, the current rise of mpMRI and MRI‐guided ultrasound provides a solid basis for generating ground‐truth data of csPCa presence to train and explore AI methodology.
Prognostic Imaging of prostate Cancer by Ultrasound (PICUS) is a recently awarded NWO project that leverages these novel mpUS advances and AI assistance to predict the presence, location, and aggressiveness of PCa. PICUS is led by the Technical University Eindhoven (prof. Massimo Misschi) in collaboration with Radboudumc and various other public and private partners: Canon Medical, General Electric, Angiogenesis Analytics, Martini Klinik Hamburg, Prostaatkanker Stichting. The Radboudumc team will focus on developing AI‐assisted fusion of mpMRI and mpUS extending upon our state‐of‐the‐art mpMRI AI. The AI has three purposes: (1) to expand mpMRI in its ability to characterize lesions in order to improve specificity; (2) to improve biopsy targeting by fusing mpUS and mpMRI. (3) to act as standalone detection of prostate cancer on mpUS as a cost-effective alternative to mpMRI.
In this project, you will collaborate with the clinical biopsy team in the urology clinic at Radboudumc and set up a data acquisition pipeline. You will link ultrasound and MRI data and expand our collection of reference standard meta-parameters. You will investigate advanced machine learning methods for end-to-end training on ultrasound images, research uncertain AI, and explore the usages of foundation models. You will work together with international partners in the PICUS consortium. You will work with over 80 PhDs in the DIAG group and present results at international meetings.
Profile
You should be a highly motivated, creative, and enthusiastic researcher with an MSc degree in Computer Science, Data Science, Physics, Mathematics, Engineering, or equivalent. You have a thorough understanding of deep learning and can adapt deep learning algorithms. Good knowledge of image processing and ultrasound is highly desirable. The research should result in a Ph.D. thesis, requiring you to have good communication skills to excel at presenting results at scientific meetings and in scientific journals.
We offer
- An exciting position in the world's top-4 research group in Radiology AI.
- An international work environment with an informal atmosphere.
- 4-year contract of 36 hours per week (full time).
- Salary scale 10A.
- An annual vacation allowance of 8%, and you will receive an end-of-year bonus of 8.3%.
- 168 vacation hours (over 23 days) per year.
- Radboudumc provides 70% coverage of the pension premium. You pay the rest of the premium with your gross salary.
- A discount on health insurance.
Organisation
The research group of Professor Huisman is part of the Department of Radiology of the Radboud University Medical Center (Radboudumc). The lab is also part of the cross-departmental Diagnostic Image Analysis Group (DIAG) at Radboudumc, with researchers in other departments, such as Radiology and Nuclear Medicine, Pathology.
We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided diagnosis (CAD). Application areas include diagnostics and prognostics of breast, colon, prostate and lung cancer. Our group is among the international front runners in the field, witnessed, for instance, by the highly successful PICAI Camelyon and Panda grand challenges which we organized.
Radboudumc strives to be a leading developer of sustainable, innovative, and affordable healthcare to improve the health and well-being of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare. To get a better picture of what this entails, check out our strategy.
Employment conditions
Working at Radboud University Medical Center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud University Medical Center, you will be given trust, and you will take responsibility for handling everything together.
- A gross monthly salary between € 3.017 and € 3.824 (scale 10A) based on full-time employment.
- An annual vacation allowance of 8% and an end-of-year bonus of 8.3%.
- As a full-time employee (36 hours per week), you are entitled to 176 vacation hours (over 24 days) per year. Radboud University Medical Center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
- You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
- We provide annual courses, both professional and personal.
- In addition to our terms of employment, we also offer employees various other attractive facilities, such as childcare and sports facilities. Want to learn more? Take a look at the CAO UMC.
Read more about the Radboudumc employment conditions and what our International Office can do for you when moving to the Netherlands.
Application
If you are interested, please apply here.
