Inez Verpalen

Amsterdam UMC

In short, Inez Verpalen's research entails the following:

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

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal form of cancer, and surgery is the only curative option. Neoadjuvant therapy (NAT) is administered before surgery for local tumor control and to target micrometastasis. Despite NAT, 40% of patients experience early diseases recurrence (within 12 months post-surgery), resulting in poor survival. Currently, there, is no reliable preoperative biomarker to predict these unfavorable survival outcomes.

In this project, we aim to develop a model using artificial intelligence (AI) to predict NAT response in PDAC patients based on computed tomography (CT) imaging. CT imaging may contain relevant information not visible to the human eye. By using AI, we hope to identify therapy-induced changes and to predict survival based on CT data. We will develop and validate this model using multicenter data from the Netherlands and international data from our consortium PHAIR-consortium partners. Applying this model is expected to lead to improved patient selection for surgery and chemotherapy, ultimately enhancing the quality of life for PDAC patients.

Short summary of progress/results January 2025

The study officially started in late February 2024 and aims to develop and externally validate CT-based machine-learning models for pre-operative response assessment to neoadjuvant therapy (NAT) in PDAC patients. Nationally, multiple centers are participating, with approval for retrospective data use from prior studies granted by the Dutch Pancreatic Group (DPCG). Data and imaging collection are progressing as planned. A segmentation model was build, and a preparatory study on a small dataset has been conducted to compare two segmentation methods and handcrafted radiomics versus deep learning approaches.

The next phase will focus on collecting international data through the Pancreatobiliary and Hepatic Artificial Intelligence Research consortium (PHAIR), for which some collaborating centers have already obtained ethical approval.

Additionally, we are exploring the creation of a dedicated PDAC imaging database. Initial discussions with a potential industrial partner are promising and may lead to its successful implementation.