Computer Vision News Computer Vision News 34 Congrats, Doctor Esther! Building PET-radiomic signatures from a biological rationale Differences in energy consumption in cancer correlates to poor prognosis and drives therapy resistance. [18F]FDG-PET visualise glucose consumption in cancer lesions. Esther used these PET scans to investigate different glucose consumption subtypes in PDAC patients. Over the last decade, a rapidly growing body of literature reported a prognostic or predictive value of “radiomics” features extracted from PET-scans. However, the lack of a clear biological underpinning of these features has been regarded as an obstacle for clinical translation. To address this paucity, Esther developed an image analysis pipeline building PETradiomic signatures from biological features derived from immunohistochemistry (see figure). She stained histology slides of PDAC tissue with an MCT4-marker, a molecule involved in glucose consumption. Using these histology slides Esther visualized the distribution of the MCT4-marker. After scanning these slides Esther extracted the intensity signal of MCT4-positive staining and investigated the distribution of this molecule on whole slide via density maps. She then mirrored her PETbased image analysis to pathology data, extracting “pathomics” features using the same texture descriptors. Esther Smeets completed her PhD at the Radboud university in Nijmegen, The Netherlands. She focused on building bridges between biology, medical imaging analyses and AI in pancreatic cancer. Congrats, doctor Esther! Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest types of cancer and its biology is still poorly understood. The primary goal of Esther’s thesis was predicting biological subtypes of PDAC by using machine learning and computer vision approaches to analyse PET scans and digital pathology images of immunohistochemically stained slides. The secondary goal was to delve into innovative treatment for PDAC patients.
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