DCS Online Webinar 3: Data Science for Advanced Medical Imaging - Spatial Metabolomics & Radiomics
The Dutch Chemometrics Society is pleased to invite you to its next online seminar: “Data Science for Advanced Medical Imaging: Spatial Metabolomics & Radiomics”, taking place on Wednesday, 16 September 2026, from 12:00 to 13:00 h.
This session will feature two researchers working at the intersection of data science, advanced medical imaging, and biomedical research: Benjamin Balluff and Petros Kalendralis. Their presentations will highlight how computational methods, machine learning, and reproducible data-analysis workflows can help extract meaningful information from complex spatial and medical imaging data. Together, the talks will cover two rapidly developing areas: mass spectrometry imaging for spatial metabolomics and radiomics for clinical oncology.
Benjamin Balluff will open the seminar with his presentation, “Software-enabled mass spectrometry imaging across spatial, spectral and meta levels”. Mass spectrometry imaging (MSI) provides spatially resolved information on metabolites in tissue and can complement other spatial-omics approaches, including transcriptomics and proteomics. Benjamin will demonstrate how software developed at M4i can support MSI analysis at multiple levels, from compartmentalizing spatial data to increasing confidence in molecular identification using pathway mass fingerprints and enabling meta-analysis of lipid MSI data in atherosclerosis.
Benjamin Balluff is Assistant Professor for Imaging Bioinformatics at M4i. With a background in bioinformatics, his research focuses on data-science approaches for mass spectrometry imaging using spatial statistics, image processing, machine learning, and classical bioinformatics, including pathway analysis.
The second presentation will be given by Petros Kalendralis and is titled “Radiomics and reproducible AI workflows for clinical oncology”. Medical imaging contains a wealth of quantitative information that can be used to support clinical research and predictive modelling. Petros will present practical applications of radiomics in radiation oncology, ranging from automated CT and MRI processing and quantitative feature extraction to the development of prognostic models for lung, head and neck, and prostate cancers. Particular attention will be given to FAIR data principles and reproducible workflows as essential components of transparent, interoperable, and clinically translatable research.
Petros Kalendralis is a clinical data scientist and postdoctoral researcher at Maastro, Maastricht University Medical Centre+. His work spans artificial intelligence in radiotherapy, radiomics, clinical data engineering, and FAIR data, including the development of open datasets and prognostic models for multiple cancer types.
This seminar will be of interest to researchers, students, and practitioners working in chemometrics, data science, medical imaging, mass spectrometry imaging, radiomics, machine learning, and biomedical data analysis. It will also provide insight into how advanced data-science methods and reproducible computational workflows can contribute to both fundamental biomedical research and clinically oriented applications.
The seminar will take place online via Teams.
DCS seminar 3: Data Science for Advanced Medical Imaging – Spatial Metabolomics & Radiomics
Wednesday, 16 September 2026
12:00 - 13:00
Meeting link: DCS seminar 3 | Microsoft Teams
