Imaging diagnostic has entered a new era with the availability of high-performance GPUs on entry level workstations, which has permitted to extract a huge number of information from medical images often using well-established machine learning techniques. Our research group (CVG) has been working for more than 20 years in multi-feature analyses of medical and biomedical images, in the research field newly named radiomics, to develop diagnostic, prognostic and predictive imaging biomarkers in breast, lung, liver, prostate, pancreatic, oesophageal and gastroesophageal cancer. In fact, image phenotyping can reveal information regarding the underlying tissue's texture, tumour physiology, or even molecular properties of cancer that are concealed from visual inspection. The RADIOMICS Project aims at exploiting our well-established quantitative imaging, machine learning and artificial intelligence techniques to investigate local radiomic heterogeneity of tumour habitat using single- and multi-modal imaging (e.g. MR, mpMR, CT, CT perfusion, PET) and hybrid technologies (CT/PET, PET/MR). The goal is developing diagnostic, prognostic and predictive imaging biomarkers, also providing a relevant contribution for patient's stratification and staging.
Imaging diagnostic has entered a new era with the availability of high-performance GPUs on entry level workstations, which has permitted to extract a huge number of information from medical images often using well-established machine learning techniques. Our research group (CVG) has been working for more than 20 years in multi-feature analyses of medical and biomedical images, in the research field newly named radiomics, to develop diagnostic, prognostic and predictive imaging biomarkers in breast, lung, liver, prostate, pancreatic, oesophageal and gastroesophageal cancer. In fact, image phenotyping can reveal information regarding the underlying tissue's texture, tumour physiology, or even molecular properties of cancer that are concealed from visual inspection. The RADIOMICS Project aims at exploiting our well-established quantitative imaging, machine learning and artificial intelligence techniques to investigate local radiomic heterogeneity of tumour habitat using single- and multi-modal imaging (e.g. MR, mpMR, CT, CT perfusion, PET) and hybrid technologies (CT/PET, PET/MR). The goal is developing diagnostic, prognostic and predictive imaging biomarkers, also providing a relevant contribution for patient's stratification and staging.
RADIOMICS - Local RADiomic features extracted from multimodal sequences to characterIze heterOgeneity of tuMour habItat and produCe imaging biomarkerS
Alessandro Bevilacqua
In corso di stampa
Abstract
Imaging diagnostic has entered a new era with the availability of high-performance GPUs on entry level workstations, which has permitted to extract a huge number of information from medical images often using well-established machine learning techniques. Our research group (CVG) has been working for more than 20 years in multi-feature analyses of medical and biomedical images, in the research field newly named radiomics, to develop diagnostic, prognostic and predictive imaging biomarkers in breast, lung, liver, prostate, pancreatic, oesophageal and gastroesophageal cancer. In fact, image phenotyping can reveal information regarding the underlying tissue's texture, tumour physiology, or even molecular properties of cancer that are concealed from visual inspection. The RADIOMICS Project aims at exploiting our well-established quantitative imaging, machine learning and artificial intelligence techniques to investigate local radiomic heterogeneity of tumour habitat using single- and multi-modal imaging (e.g. MR, mpMR, CT, CT perfusion, PET) and hybrid technologies (CT/PET, PET/MR). The goal is developing diagnostic, prognostic and predictive imaging biomarkers, also providing a relevant contribution for patient's stratification and staging.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
