Gastro-Esophageal cancer (GEC) has a dismal prognosis. Novel immunotherapy treatment (IMT) shows promising therapeutic results in a subset of all cancer patients but is highly costly.

To select patients that will benefit, medical specialists like pathologist need to quantify biomarkers that are predictive for IMT outcome. Various cancer types require either PD-L1, or tumor infiltrating lymphocytes (TIL) or tumor-foreignness biomarker testing. These biomarker assessments are however complex, expensive and suffer from interobserver variability. Current limited testing methods result in unwanted variation in patient outcome and high healthcare costs.

An evident clinical need exists for objective, integrated and easy to use decision support tools, to optimize personalized treatment and identify GEC patients that will respond to IMT. We will address these needs using Artificial Intelligence (AI) at multiple levels, as simultaneous biomarker assessment is currently not standard operational procedure and integrated assessment is too complex for individual medical specialists. In the first part of the project, we intend to alleviate this problem, by using medical imaging and computer vision algorithms to accurately quantify the multimodal morphological and genomic biomarker parameters PD-L1, TIL and tumor foreignness directly on standard histopathology H&E slides without the need of performing additional test. Secondly, as the primary goal of all these analyses is to predict response to immunotherapy and disease outcome, we will also consider the problem from a knowledge discovery perspective which biomarkers or combinations are responsible for this specific outcome and response prediction?

In this project we will need to develop state of the art deep learning techniques for determination, integration of spatial quantification of individual biomarkers from histopathology slides, and outcome prediction in multidisciplinary clinical data. Additionally, we will design novel self-supervised geometric deep learning techniques and combine these with model interpretability techniques to discover new knowledge about gastro-esophageal cancer and outcome to immunotherapy treatment.

Our goal is to bring these models into the daily clinical practice of pathologists and oncologists to identify patients who may benefit most from immunotherapy or could be spared unnecessary treatment.

About your role
As a PhD-candidate, you will be responsible for developing and evaluating state-of-the-art deep learning techniques in multidisciplinary medical data. You will be involved in preparing histopathology datasets of GEC patients for cancer-immune interaction and clinical outcome data. Finally, you will validate the algorithms you developed with immunotherapy treatment outcome in independent patient cohorts to ensure the devised AI-algorithms' applicability in clinical practice.

• You will collaborate with other researchers within the research labs of the SELECT-AI consortium (Amsterdam UMC departments of Pathology and Medical Oncology, University of Amsterdam the Institute of Informatics, and the department of Pathology from NKI-AvL).
• Regularly present internally on your progress
• Regularly present intermediate research at international conferences and workshops, publish them in proceedings and journals, help with submitting applications
• Assist in relevant teaching activities
• Complete and defend a PhD thesis

For this project two PhD students are recruited, one student with more focus on fundamental AI-algorithm development and one on clinical applicable AI methods.

We are looking for motivated, goal-oriented, independent, and proactive PhD candidates who are enthusiastic about working in a multidisciplinary setting of medical imaging, treatment outcome and AI.

• Preferably you have a master's degree in artificial intelligence, computer science or (technical) medicine.
• In any case, you should have affinity to work with clinical data, have excellent programming skills and experience with deep learning.

• A flying start to your career in research work in a metropolis with a diverse and open culture, and a multicultural society.
• Working with motivated colleagues from all corners of the world.
• You will start with a fulltime contract for one year in accordance with the CAO UMC 2022-2023, with the possibility of extension (as long as project budget is available).
• PhD students (Onderzoeker in Opleiding) are placed in scale 21, with a fulltime gross salary. The starting salary is € 2.789,- and increases to € 3.536,- in the fourth year. PhD students with a Medical degree (Arts-Onderzoeker) are placed in scale 10. The starting salary is €3.536,- and increases to a maximum of €5.088,- gross per month.
• In addition to a good basic salary, you will receive, among other things, 8.3% year-end bonus and 8% vacation allowance. Calculate your net salary here.
• Pension via BeFrank
• Excellent accessibility by public transport and reimbursement of a large part of your travel expenses. We also have sufficient parking spaces at the AMC location and a good bicycle scheme.

Amsterdam UMC

This project is funded by KWF-Health Holland public-private partnership for two PhD students, and you'll be embedded in the labs of the SELECT-AI consortium and collaborate with the private partner Ellogon.AI. Our multidisciplinary team focuses on the development, validation and clinical implementation of AI solutions for medical imaging data analysis challenges in cancer patients. The group aims at designing and enabling socially responsible AI innovations in healthcare.

Research groups Amsterdam University Medical Centers:

• Sybren Meijer, Artificial Intelligence in Gastro-Esophageal Oncology, department of Pathology
• Professor Clárisa Sanchez from qurAI, an interfaculty and multidisciplinary group between the Institute of Informatics of the University of Amsterdam and the Department of Biomedical Engineering and Physics of the Amsterdam UMC, location AMC.

Research groups on Institute of Informatics, University of Amsterdam:

• Erik Bekkers from the Amsterdam Machine Learning lab
• Efstratios Gavves from the QUVA Lab, embedded in the Video & Image Sense lab

Starting a new job at AMR!