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.

Radiomic and dosiomic profiling of paediatric medulloblastoma tumours treated with intensity modulated radiation therapy / Talamonti, C.; Piffer, S.; Greto, D.; Mangoni, M.; Ciccarone, A.; Dicarolo, P.; Fantacci, M. E.; Fusi, F.; Oliva, P.; Palumbo, L.; Favre, C.; Livi, L.; Pallotta, S.; Retico, A.. - 1089:(2019), pp. 56-64. ((Intervento presentato al convegno 1st Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019, and 1st Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019 held at the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 tenutosi a ita nel 2019 [10.1007/978-3-030-29930-9_6].

Radiomic and dosiomic profiling of paediatric medulloblastoma tumours treated with intensity modulated radiation therapy

Oliva P.;
2019

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.
978-3-030-29929-3
978-3-030-29930-9
Radiomic and dosiomic profiling of paediatric medulloblastoma tumours treated with intensity modulated radiation therapy / Talamonti, C.; Piffer, S.; Greto, D.; Mangoni, M.; Ciccarone, A.; Dicarolo, P.; Fantacci, M. E.; Fusi, F.; Oliva, P.; Palumbo, L.; Favre, C.; Livi, L.; Pallotta, S.; Retico, A.. - 1089:(2019), pp. 56-64. ((Intervento presentato al convegno 1st Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019, and 1st Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019 held at the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 tenutosi a ita nel 2019 [10.1007/978-3-030-29930-9_6].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11388/231497
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