Suzanne Gisbertz

Amsterdam UMC

In short, Suzanne Gisbertz's research entails the following:

AI for complete pathological response evaluation with image-based machine learning models in esophageal carcinoma: AIFORCE

Esophageal cancer is a prevalent malignancy worldwide with a poor prognosis, with 572.000 new patients and 508.600 esophageal cancer related death worldwide per year.
Chemoradiotherapy (carboplatin, paclitaxel, and concurrent 41.4 Gy radiotherapy) for esophageal cancer followed by surgery is currently regarded as a standard of care in
clinical practice in the Netherlands. A significant proportion of patients have a pathological complete response(pCR) following neoadjuvant chemoradiotherapy(nCRT). However, pre-operative determination via endoscopies and imaging remains unreliable for patient selection. The objective of this study is to develop an image-based machine learning prediction model to assess pathologic response (treatment effect) to nCRT in esophageal cancer, by evaluating 18F-FDG PET/CT and DW-MRI / DCE-MRI scans prior, during and after nCRT, compared to the histopathological assessment of the resection specimen.