Amsterdam UMC, location VUMC
In short, Ronald Boellaard's research entails the following:
Artificial intelligence and machine learning for FDG PET/CT response prediction in diffuse large B cell lymphoma
'The project aims at the application of artificial intelligence (AI) methods to improve prognosis and treatment response prediction based on FDG PET/CT studies of diffuse large B-cell lymphoma patients. This project is an extension of a successfully running international consortium project PETRA (petralymphoma.org). The Hanarth Fonds grant will be used to investigate the use of radiomics analysis in combination with machine learning as well as the use of convolution neural networks for deep radiomics analysis of FDG PET/CT to better predict response to treatment, thereby avoiding futile treatments, as compared to current FDG PET/CT reads.'
Short summary of progress/results 2022
During the first 6 month of the project a data harmonisation method was succesfully implemented, applied and tested to mitigate differences in FDG PET uptake metrics due to use of different image reconstructions (as seen in multicenter studies) and tumor segmentation methods (as seen in different softwares). We found that the method was able to harmonize data for some segmentation methods, but also that use of fixed size thresholds seem to allow extraction of features fairly independent on image reconstruction and thereby bypasses the need for retrospective data harmonization.
Secondly, we developped a deep learning method (CNN) that used maximum intensity projection of the FDG PET/CT data and we found that the method has feasibility to predict 2 years time to progression in DLBCL patients with an externally validated ROC-AUC of 0.70. Currently, we aim to extend to model including other image derived metrics as well as clinical data to further improve performance. Moreover, explainable components will be added to the deep learning method(s) as to assist the user in assessing the plausiblity of the provided predictions.