Standardizing digital biobanks : integrating imaging, genomic, and clinical data for precision medicine
© 2024. The Author(s)..
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Journal of translational medicine - 22(2024), 1 vom: 05. Feb., Seite 136 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Brancato, Valentina [VerfasserIn] |
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| Links: |
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| Themen: |
Big data |
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| Anmerkungen: |
Date Completed 07.02.2024 Date Revised 10.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12967-024-04891-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368064964 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Biobanking | |
| 650 | 4 | |a Clinical decision support systems (CDSS) | |
| 650 | 4 | |a Data integration | |
| 650 | 4 | |a Imaging | |
| 650 | 4 | |a NGS | |
| 650 | 4 | |a Pathomics | |
| 650 | 4 | |a Precision medicine | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Standardization | |
| 700 | 1 | |a Esposito, Giuseppina |e verfasserin |4 aut | |
| 700 | 1 | |a Coppola, Luigi |e verfasserin |4 aut | |
| 700 | 1 | |a Cavaliere, Carlo |e verfasserin |4 aut | |
| 700 | 1 | |a Mirabelli, Peppino |e verfasserin |4 aut | |
| 700 | 1 | |a Scapicchio, Camilla |e verfasserin |4 aut | |
| 700 | 1 | |a Borgheresi, Rita |e verfasserin |4 aut | |
| 700 | 1 | |a Neri, Emanuele |e verfasserin |4 aut | |
| 700 | 1 | |a Salvatore, Marco |e verfasserin |4 aut | |
| 700 | 1 | |a Aiello, Marco |e verfasserin |4 aut | |
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