Jeanin van Hooft

LUMC

In short, Jeanin van Hooft's research entails the following:

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

Pancreatic cancer (PC) has a dismal survival due to most patients presenting with advanced stage disease. Magnetic resonance imaging (MRI) and endoscopic ultrasound (EUS) surveillance programs for high-risk populations, whose lifetime risk of PC varies between 5-35%, have been established.

There is a need to improve the modalities used to surveil these high-risk individuals (HRI), as only a minority of tumors are discovered as precursor lesions with high-grade dysplasia (HGD) or early-stage cancers. Artificial intelligence (AI) applied to MRI, specifically radiomics and deep learning models, could aid by complex pattern recognition of subtle changes associated with progression to malignancy in these HRI, enabling detection at a curable stage as precursor lesions or early-cancers.

A previously developed automated pancreas segmentation algorithm will be validated on the complete LUMC MRI dataset of 2400 longitudinal MRIs from 340 individuals. Subsequent external validation is assured through the 1200 MRI database of Sweden’s Karolinska Institute.

After the segmentation algorithm has delineated the pancreatic region of interest, the LUMC and Karolinska cohorts will be used for training and validating a diagnostic classification algorithm for detecting PC. The classification algorithm will take a longitudinal approach, incorporating multiple MRI sequences and demographic variables into predicting precursor lesions with HGD and/or cancer.

The final step is reserved for a robust clinical trial design aiming to prospectively validate the longitudinal classifier in HRI surveillance cohorts.