Integrative analysis of cancer imaging readouts by networks

Dominietto, Marco; Tsinoremas, Nicholas F.; Capobianco, Enrico

doi:10.1016/j.molonc.2014.08.013

Cited by 20 publications

(20 citation statements)

References 90 publications

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“…Data connectivity in radiomics was discussed in Dominietto et al (11). In principle, connected biomarkers open new avenues to therapeutic paths, and allows assessment of emerging digital biomarkers (12).…”

Section: Our Contributionmentioning

confidence: 99%

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Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

et al. 2020

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Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

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Section: Our Contributionmentioning

confidence: 99%

“…The value of the proposed analysis is toward our understanding of the potential of a network inference approach within the radiomics [see also (11,12) for treatment of the topic]. From the image domain, specific features were selected from repeated measurements (MRI) and mapped onto a network domain.…”

Section: Our Contributionmentioning

confidence: 99%

Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

et al. 2020

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“…To bridge with the earlier introduced digital biomarkers, it is tempting to enrich the network with therapy-related variables, such as response, follow-up, drug effects, etc. (Dominietto et al, 2015;Dominietto and Capobianco, 2016) (c) Dependence in networks is naturally imported through the inherent metric based on the node connectivity patterns. This metric might be extended to cover highly complex network configurations (De Domenico et al, 2013.…”

Section: Differential Measures Beyond Expressionmentioning

confidence: 99%

Precision Oncology: The Promise of Big Data and the Legacy of Small Data

Capobianco

2017

Front. ICT

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Big data are expected to exert profound impacts on medicine. High-throughput technologies, electronic medical records, high-resolution imaging, multiplexed omics, these are examples of fields that are progressing at a fast pace. Because they all yield complex heterogeneous data types, managing such variety and volumes is a challenge. While the computation power required to analyze them is available, the main difficulty consists in interpreting the results. In light of the emerging precision medicine paradigm, oncology is influenced by digital phenotypes characterizing disease expression, In particular, digital biomarkers could become critical for the evaluation of clinical endpoints. Currently, integrative approaches are conceived for the analysis of multi-evidenced data, i.e., data generated from multiple sources, such as cells, organs, individual lifestyle and social habits, environment, population dynamics, etc. The granularity, the scales of measurement, the model prediction accuracy, these are factors justifying an ongoing progressive differentiation from evidence-based medicine, typically based on a relatively small and unique scale of the experiments, thus well assimilated by a mathematical or statistical model. A premise of precision medicine is the N-of-1 paradigm, inspired by a focus on individualization. However, diversity, amount, and complexity of input data points that are needed for individual assessments, suggest centrality of systems inference principles. In turn, a revised paradigm is acquiring relevance, say (N-of-1) c , where the exponent c indicates connectivity. What makes connectivity such a key factor? For instance, the synergy embedded but often latent in the data layers, namely signatures, profiles, etc., which can lead to many stratified directions. Reference then goes to the biological and medical insights due to data integration, here discussed in view of the current oncological trends.

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“…Intuitively, the tumor is seen as a group of microscopic elements organized in macroscopic structures interacting together and with the host organ. Therefore, we can model the tumor as a complex network where the nodes are the microscopic elements that are connected together on the basis of their physiological behavior (Dominietto et al, 2015). Nodes that behave similarly form macroscopic structures, or clusters, in turn interacting between them (intra-cluster), and with the surrounded healthy tissue.…”

Section: Physiological Maps: Deciphering Complexitymentioning

confidence: 99%