Abstract
The aim of this work is to describe the state of progress of a study developed in the framework of AIM (Artificial Intelligence in Medicine). It is a project funded by INFN, Italy, and it involves researchers from INFN, Hospital Meyer and Radiotherapy Unit of University of Florence. The aim of the proposed study is to apply a retrospective exploratory MR-CT-based radiomics and dosiomic analysis based on emerging machine-learning technologies, to investigate imaging biomarkers of clinical outcomes in paediatric patients affected by medulloblastoma, from images. Features from MR-CT scans will be associated with overall survival, recurrence-free survival, and loco-regional recurrence-free survival after intensity modulated radiotherapy. Dosimetric analysis data will be integrated with the objective of increase predictive value. This approach could have a large impact for precision medicine, as radiomic biomarkers are non-invasive and can be applied to imaging data that are already acquired in clinical settings.
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Talamonti, C. et al. (2019). Radiomic and Dosiomic Profiling of Paediatric Medulloblastoma Tumours Treated with Intensity Modulated Radiation Therapy. In: Vento, M., et al. Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-030-29930-9_6
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