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2022
Conference article
Open Access
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
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ISTI Repository 
| ieeexplore.ieee.org 
| CNR ExploRA
2022
Journal article
Open Access
NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients
Borgheresi R., Barucci A., Colantonio S., Aghakhanyan G., Assante M., Bertelli E., Carlini E., Carpi R., Caudai C., Cavallero D., Cioni D., Cirillo R., Colcelli V., Dell'Amico A., Di Gangi D., Erba P. A., Faggioni L., Falaschi Z., Gabelloni M., Gini R., Lelii L., Liò P., Lorito A., Lucarini S., Manghi P., Mangiacrapa F., Marzi C., Mazzei M. A., Mercatelli L., Mirabile A., Mungai F., Miele V., Olmastroni M., Pagano P., Paiar F., Panichi G., Pascali M. A., Pasquinelli F., Shortrede J. E., Tumminello L., Volterrani L., Neri E., On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: European radiology experimental Online 6 (2022). doi:10.1186/s41747-022-00306-9
DOI: 10.1186/s41747-022-00306-9
Metrics:
Borgheresi R., Barucci A., Colantonio S., Aghakhanyan G., Assante M., Bertelli E., Carlini E., Carpi R., Caudai C., Cavallero D., Cioni D., Cirillo R., Colcelli V., Dell'Amico A., Di Gangi D., Erba P. A., Faggioni L., Falaschi Z., Gabelloni M., Gini R., Lelii L., Liò P., Lorito A., Lucarini S., Manghi P., Mangiacrapa F., Marzi C., Mazzei M. A., Mercatelli L., Mirabile A., Mungai F., Miele V., Olmastroni M., Pagano P., Paiar F., Panichi G., Pascali M. A., Pasquinelli F., Shortrede J. E., Tumminello L., Volterrani L., Neri E., On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: European radiology experimental Online 6 (2022). doi:10.1186/s41747-022-00306-9
DOI: 10.1186/s41747-022-00306-9
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eurradiolexp.springeropen.com | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Score-driven generalized fitness model for sparse and weighted temporal networks
Di Gangi D., Bormetti G., Lillo F.
Temporal network data have recently received increasing attention due to the rich information content and valuable insight that appropriate modeling of links' dynamics can unveil. While most of the literature on temporal network models focuses on binary graphs, each link of a real networks is often associated with a weight, a positive number describing the intensity of the relation between the nodes. Here we propose a novel dynamical model for sparse and weighted temporal networks as a combination of an extension of the fitness model and of the score-driven framework. We consider a zero-augmented generalized linear model to handle the weights and an observation-driven approach to describe time-varying parameters. We propose a flexible approach where the existence probability of a link is independent of its expected weight. This fact represents a crucial difference with alternative specifications proposed in the recent literature, with relevant implications both for the model's flexibility and for the forecasting capability. Our approach also accommodates the network dynamics' dependence on external variables. We present a link forecasting analysis to data describing the overnight exposures in the Euro interbank market and investigate whether the influence of EONIA rates on the interbank network dynamics has changed over time during the sovereign debt crisis.Source: Information sciences 612 (2022): 1226–1245. doi:10.1016/j.ins.2022.08.058
DOI: 10.1016/j.ins.2022.08.058
Metrics:
Di Gangi D., Bormetti G., Lillo F.
Temporal network data have recently received increasing attention due to the rich information content and valuable insight that appropriate modeling of links' dynamics can unveil. While most of the literature on temporal network models focuses on binary graphs, each link of a real networks is often associated with a weight, a positive number describing the intensity of the relation between the nodes. Here we propose a novel dynamical model for sparse and weighted temporal networks as a combination of an extension of the fitness model and of the score-driven framework. We consider a zero-augmented generalized linear model to handle the weights and an observation-driven approach to describe time-varying parameters. We propose a flexible approach where the existence probability of a link is independent of its expected weight. This fact represents a crucial difference with alternative specifications proposed in the recent literature, with relevant implications both for the model's flexibility and for the forecasting capability. Our approach also accommodates the network dynamics' dependence on external variables. We present a link forecasting analysis to data describing the overnight exposures in the Euro interbank market and investigate whether the influence of EONIA rates on the interbank network dynamics has changed over time during the sovereign debt crisis.Source: Information sciences 612 (2022): 1226–1245. doi:10.1016/j.ins.2022.08.058
DOI: 10.1016/j.ins.2022.08.058
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ISTI Repository | Information Sciences | www.sciencedirect.com | CNR ExploRA
2022
Journal article
Open Access
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model
Campajola C., Di Gangi D., Lillo F., Tantari D.
A common issue when analyzing real-world complex systems is that the interactions between their elements often change over time. Here we propose a new modeling approach for time-varying interactions generalising the well-known Kinetic Ising Model, a minimalistic pairwise constant interactions model which has found applications in several scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology allows to extract from data and interpret the presence of temporal patterns describing time-varying interactions. We identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics and we introduce a method to dynamically learn its value from the data, without specifying parametrically the system's dynamics. We extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time, and show how our methodology applies to a variety of complex systems such as financial markets, temporal (social) networks, and neuronal populations.Source: Scientific reports (Nature Publishing Group) 12 (2022). doi:10.1038/s41598-022-23770-0
DOI: 10.1038/s41598-022-23770-0
Project(s): SoBigData-PlusPlus
Metrics:
Campajola C., Di Gangi D., Lillo F., Tantari D.
A common issue when analyzing real-world complex systems is that the interactions between their elements often change over time. Here we propose a new modeling approach for time-varying interactions generalising the well-known Kinetic Ising Model, a minimalistic pairwise constant interactions model which has found applications in several scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology allows to extract from data and interpret the presence of temporal patterns describing time-varying interactions. We identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics and we introduce a method to dynamically learn its value from the data, without specifying parametrically the system's dynamics. We extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time, and show how our methodology applies to a variety of complex systems such as financial markets, temporal (social) networks, and neuronal populations.Source: Scientific reports (Nature Publishing Group) 12 (2022). doi:10.1038/s41598-022-23770-0
DOI: 10.1038/s41598-022-23770-0
Project(s): SoBigData-PlusPlus
Metrics:
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ISTI Repository | www.nature.com | CNR ExploRA