More than € 6 million granted for the 5th Hanarth Fonds call!

The Hanarth Fonds received 70 financing applications during the 2023 call. Following a careful assessment process, it has been determined that in 2024, 13 projects will be funded by the Hanarth Fonds*.

The Scientific Advisory Board (WAR) and expert (international) reviewers have assessed the proposals on criteria such as feasibility and quality of the proposal, the experience of the applicant and whether the application is in accordance with the purpose of the Hanarth Fonds. Based on all assessments, the Hanarth Fonds Board has made a well-considered financing decision.

Granted research projects 2023

Below you will find the research projects that have been granted funding*. Soon you will find a link to the summaries of these research projects on this page as well.

MaLMeC: Machine-Learned DNA Methylation Classification to enable tumor subtyping from liquid biopsies
Jeroen de Ridder
UMC Utrecht

Artificial intelligence for risk group classification and staging of Wilms tumors
Ronald de Krijger
Princess Máxima

Development and implementation of Image-based machine-learning models to Determine the response to neoadjuvant therApy in panCreatic ducTal adenocarcinoma (DIDACT)
Inez Verpalen
Amsterdam UMC

AI-IMAGINE - Automated Intraoperative assessment of IMAGINg Endpoints for first-time right liver thermal ablation
Kristian Overduin
Radboudumc

SalvIdentify: improving salivary gland tumor diagnostics by artificial intelligence based classification
Danielle Cohen
LUMC

Improved residual disease detection after (chemo-)radiotherapy for locally advanced head and neck squamous cell carcinoma
Cornelis van den Berg
UMC Utrecht

TowArds IndividuaLized PSMA PET/CT-guided Treatment in Metastastatic PrOstate CanceR Using Machine Learning-Derived Risk Stratification (TAILOR-MADE)
Arthur Braat
UMC Utrecht

Deep uLMS: Deep Learning To Improve Uterine LeiomyoSarcoma Diagnostics
Tjalling Bosse
LUMC

Predicting functional and cognitive decline after glioma surgery (PREDICT)
Linda Douw
Amsterdam UMC

Response prediction to neoadjuvant chemotherapy in patients with triple negative breast cancer based on integrated diagnostics
Carolien van Deurzen
Erasmus MC

Improving early detection of PANcreatic cancer in HIgh-risk individuals through Artificial Intelligence methods (PAN-HI-AI)
Jeanin van Hooft
LUMC

Physics-informed Neural networks to standardize brain MRI: boosting AI applications in gliomas and meningiomas
Alessandro Sbrizzi / Stefano Mandija
UMC Utrecht

Artificial Intelligence-based MRI diagnosis of Prostate Cancer: a two-step research approach to realize clinical implementation
Derya Yakar
UMCG

* on the condition that the required agreements will be signed between parties