Stefano Trebeschi

Stefano Trebeschi, PhD #

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Research Interest #

Development, application, and deployment of artificial intelligence for better follow-up and monitoring of cancer patients. Leading the research line in prognostic monitoring.

Education #

  • 2010-2013 BSc in Computer Science and Engineering,;
  • 2013-2016 MSc in Informatics,;
  • 2016-2021 PhD in Artificial Intelligence for Imaging in Immunotherapy, Supervisors: Prof. Regina Beets-Tan, Prof. Hugo Aerts (

Work experience #

  • 2012-2013 Research assistant, Faculty of Computer Science;
  • 2015-2016 Research assistant, Dept. of Neuroradiology,;
  • 2016-2021 PhD Student, Dept. Radiology,;
  • 2021-on Post Doc, Dept. Radiology,

Selected Publications as First Author #

Stefano Trebeschi et al. “Prognostic value of deep learning mediated treatment monitoring in lung cancer patients receiving immunotherapy”. In: Frontiers in Oncology, 10.3389/fonc.2021.609054 (2021)

Stefano Trebeschi et al. “Development of a prognostic AI-monitor for metastatic urothelial cancer patients receiving immunotherapy”. In: Frontiers in Oncology, 10.3389/fonc.2021.637804 (2021)

Stefano Trebeschi et al. “Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers”. In: Annals of Oncology, 30.6 (2019), pp. 998–1004

Stefano Trebeschi et al. “Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR”. In: Scientific Reports volume 7, Article number: 5301 (2017)

Zuhir Bodalal, Stefano Trebeschi, and Regina Beets-Tan. “Radiomics: a critical step towards integrated healthcare”. In: Insights into imaging, 9.6 (2018), pp. 911–914

Promovendi & Alumni #

MSc in Technical Medicine (University Twente) #

  • Kevin Groot Lipman developed a deep learning algorithm to imitate the legal, medical assessment of asbestosis. Kevin’s graduation project was supervised in a collaboration between Radiology (Zuhir, Thierry and I) and Thoracic Oncology (Dr. Sjaak Burgers). His work was awarded the highest grade (10/10). MSc Thesis - News Article;

  • Ivar Wamelink developed deep learning framework for advanced image processing of brain metastases. The results of his graduation project served as the groundwork for the research line in prognostic monitoring. He is currently pursuing a PhD at the Vrije Universiteit of Amsterdam. MSc Thesis - VUMc personal page;

  • Ingmar Paul Loohuis investigated the usage of anatomical changes, modeled as vector fields via image-to-image registration, in the context of prognostication, answering the question: what anatomical changes in the body of the patient are most predictive of survival? Ingmar is currently working in the machine learning group of the University Hospital of Utrecht. MSc Thesis;

  • Joyce Griedanus investigated the usage of computer algorithms-derived MRI features in muscle-invasive bladder cancer receiving neoadjuvant immunotherapy. This project was realized under the collaboration of our centers with the San Raffaele Hospital in Milan, Italy. She is now working on her PhD within the departments of radiology, urology, and medical oncology – [MSc Thesis][(;

  • Iris van der Loo extended the work of Ingmar link to brain anatomy: using deep learning to track tumor and tumor-induced changes in the brain anatomy, and their influence on survival. This work was done in collaboration between our centers and the Deventer Hospital. She now started her PhD in the department of radiology – MSc Thesis.

MSc Human Factors and Engineering Psychology (University of Twente) #

  • Christof Schulz evaluated and improved the interaction between humans and AI in medical imaging through a three-phase research approach. His work identifies usability problems and low satisfaction with the initial AI implementation, applies human-centered design to develop a new prototype. Christof’s project was in collaboration with Dr. Simone Borsci, and daily supervision by Kevin Groot Lipman – link not yet available.

PhD in Artificial Intelligence (Maastricht University) #

  • Kevin Groot Lipman is working to improve response evaluation in mesothelioma patients, their prognostic value, and the impact of such technologies on both patients and clinics;
  • Melissa de Bruin is, similarly to Kevin, working to improve response evaluation in locally advanced colon cancer patients in neoadjuvant settings;
  • Melda Yeghaian is studying the potential added value of non-imaging techniques in terms of prognostication during follow-up;
  • Teresa Tareco-Bucho is investigating the potential broader role in the clinics, and for the patient, of novel response evaluation techniques compared to current clinical standards;
  • Laura Estacio Cerquin is developing novel techniques to leverage whole-body imaging in response evaluation;
  • Iris van der Loo is developing novel techniques to leverage local and oligometastatic imaging for response evaluation.

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