PhD candidate (4 yrs) / Postdoc (3yrs) Artificial intelligence-based risk and outcome prediction in ductal carcinoma in situ (DCIS)

Project information
Ductal carcinoma in situ (DCIS) may progress to invasive breast cancer (IBC), but about 3 out of 4 never will. We cannot reliably distinguish the minority of high-risk, potentially progressive DCIS lesions from the low-risk, harmless ones in current practice. Consequently, almost all women with DCIS are treated with surgery, often supplemented by radiotherapy, to prevent progression to IBC. Therefore, there is an urgent need to distinguish the high- from low-risk lesions to avoid overtreatment of the latter ones. This is not trivial as DCIS is a widespread finding in women, primarily due to the introduction of population-based breast cancer screening, comprising up to 25% of all newly diagnosed breast 'malignancies.'

We have the most extensive well-annotated DCIS datasets in the world available with over 12 years of follow-up. Using this, we will develop state-of-the-art artificial intelligence (AI) algorithm to distinguish high- from low-risk DCIS at the level of histopathology. In addition, we will investigate the morphological and biological features that the AI model will select; we will also integrate AI with genomics data. This project will develop deep learning techniques for quantifying biomarkers in histopathology images (H&E and IHC) in multimodal data. As the input dimensionality can exceed 250 000 x 250 000, we will research novel self-supervised contrastive learning techniques combined with transformers/multiple instance learning models. Eventually, we will combine these with model interpretability techniques to discover new insights into DCIS. 

The job description
As a PhD-candidate/postdoc, you will be responsible for developing and evaluating state-of-the-art deep learning techniques in multidisciplinary data. You will validate these algorithms in independent cases to ensure the devised AI-algorithms’ applicability in clinical practice.

You are embedded in the Wesseling Lab (https://www.nki.nl/research/research-groups/jelle-wesseling/ ) and the Teuwen Lab (AI for Oncology https://aiforoncology.nl ) at the Netherlands Cancer. The Wesseling’ lab overarching research focus is to develop new strategies to substantially improve the prognostic and predictive power of breast cancer pathology and to understand the underlying biology. The mission of AI for Oncology Lab is to develop artificial intelligence innovations for the improvement of cancer diagnostics and therapy.

You will discuss results with our team, publish your work in artificial intelligence / medical journals, and present it at international conferences.