PhD position: Predicting radiotherapy outcome using Artificial Intelligence
The PhD student will be part of a dynamic, multidisciplinary research team from UMCG including an active group of PhD students, physicians, medical physicists, and other researchers. PhD students are registered within the Graduate School of Medical Sciences in Groningen, and graduate with a PhD degree from the University of Groningen, Faculty of Medicine.
The Department of Radiation Oncology in the UMCG is the first in the Netherlands with an integrated state of the art photon-proton clinic. Currently, radiotherapy is provided for over 4.500 patients annually. The department has the largest prospective follow-up program of systematically collected clinical, toxicity and tumour data of cancer patients in the world. The department is actively involved in patient care, research, education, and training.
The University Medical Centre Groningen, located in the centre of Groningen, is one of the Netherlands’ seven university medical centres and the largest employer in the Northern Netherlands. It has an ambitious, dynamic, international environment with state-of-the-art facilities. More than 13.000 employees provide patient care, are involved in medical education, and perform cutting-edge scientific research, focused on healthy ageing. Data science and large-scale studies are important focal points in Groningen.
Radiotherapy is an important treatment modality for lung cancer patients, yet they are often faced with toxicities due to radiation damage. These toxicities do not only have a big impact on the quality-of-life of these patients, but can also have a negative impact on their prognosis. This project aims to improve the prediction of treatment related toxicities and tumor response in order to prevent toxicity and (related) fatalities by improved treatment selection (e.g. proton vs photon therapy).
Deep learning techniques have the capacity to select relevant features directly from 3D data itself, which allows for incorporating the three-dimensional radiation damage in and around multiple organs. Current models can only deal with single values to represent the radiation dose to an organ, thus large improvement can be expected with AI. Large cohort sizes with complete data are needed to train DL prediction models: UMCG has an unique dataset of 900+ lung cancer patients with complete outcome, toxicity imaging and radiation dose information available for this project.
The PhD candidate for this position will develop their own convolutional neural network (CNN) to predict pneumonitis and survival based on available 3D dose distributions, multi-organ segmentations and imaging.
- You have a Master's degree in Physics, Mathematics, Computer Science, Technical Medicine or a related field.
- You are enthusiastic and driven with an interest in radiotherapy.
- You have experience in programming deep learning, with experience in 3D imaging processing being a plus.
- You have proficiency in programming using Python and/or Matlab.
- You have excellent communication skills in English.
- You have a flexible and collaborative attitude.
- You are available to start the PhD project this year (aimed start date between August and September 2023; total 4 years).
The UMCG has a preventive Hepatitis B policy. The UMCG can provide you with the vaccination, should it be required for your position.
In case of specific professions a ‘Certificate of Good Conduct’ is required.
Conditions of employment
The PhD position concerns a fulltime appointment (36 hours a week) for first one year. If you are proven to be suitable, the appointment will be extended for another 3 years. Your salary will be a minimum of € 2.789,- gross per month in the first year and a maximum of € 3.536,- gross per month (scale PhD) in the final (4th) year, based on a full-time appointment. In addition, the UMCG will offer you 8% holiday pay, and 8.3% end-of-year bonus. The conditions of employment comply with the Collective Labour Agreement for Medical Centres (CAO-UMC).
For more information about this vacancy you may contact:
Sanne van Dijk
Applying for a job
Please use the the digital application form at the bottom of this page - only these will be processed.
You can apply until 5 July 2023.
Within half an hour after sending the digital application form you will receive an email- confirmation with further information.
Digital application form