Imaging Biobanks, Big Data, and Population-Based Imaging Biomarkers

Mantarro, Annalisa; Scalise, Paola; Neri, Emanuele

doi:10.1007/978-3-319-43504-6_13

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…This will have significant potential to link imaging biomarkers with other biomarkers and clinical data. The inclusion of new imaging biobanks in standard biobanks is crucial to achieve data integration, to access large amounts of high-quality data and, consequently, to foster a personalised medicine setting [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project

et al. 2021

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PRIMAGE is a European Commission-financed project dealing with medical imaging and artificial intelligence aiming to create an imaging biobank in oncology. The project includes a task dedicated to the interoperability between imaging and standard biobanks. We aim at linking Digital imaging and Communications in Medicine (DICOM) metadata to the Minimum Information About BIobank data Sharing (MIABIS) standard of biobanking. A very first integration model based on the fusion of the two existing standards, MIABIS and DICOM, has been developed. The fundamental method was that of expanding the MIABIS core to the imaging field, adding DICOM metadata derived from CT scans of 18 paediatric patients with neuroblastoma. The model was developed with the relational database management system Structured Query Language. The integration data model has been built as an Entity Relationship Diagram, commonly used to organise data within databases. Five additional entities have been linked to the “Image Collection” subcategory in order to include the imaging metadata more specific to the particular type of data: Body Part Examined, Modality Information, Dataset Type, Image Analysis, and Registration Parameters. The model is a starting point for the expansion of MIABIS with further DICOM metadata, enabling the inclusion of imaging data in biorepositories.

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Section: Discussionmentioning

confidence: 99%

DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project

et al. 2021

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“…112,132,133 • Brain tumor survival prediction [134][135][136] : • Glioblastoma recurrence prediction. 137…”

Section: Applications Of Artificial Intelligence In Oncologymentioning

confidence: 99%

“…113 Radiomics can be combined with ML, where features are extracted using radiomics and models are trained, validated and tested using ML techniques. 137…”

Section: Radiomics Workflowmentioning

confidence: 99%

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

Mahajan

Chakrabarty

Majithia

et al. 2023

Indian J Med Paediatr Oncol

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With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.

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“…Repositories of human tissue sample stored in an organized manner for research purpose is known as “biobank”, and collection of medical image data for long term storage and retrieval for research is known as “imaging biobank” ( 26 , 27 ) Digital Imaging and Communications in Medicine (DICOM) is the universal format for Picture Archiving and Communication System (PACS) storage and data sharing across all institutions ( 26 ). The data needs to be de-identified and informed consent of the patient obtained prior to data archiving ( 28 ).…”

Section: Imaging Biobanksmentioning

confidence: 99%

Editorial: The use of deep learning in mapping and diagnosis of cancers

Mahajan

Chakrabarty

2022

Front. Oncol.

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Editorial on the Research TopicThe use of deep learning in mapping and diagnosis of cancers Deep Learning (DL) is a subset and an augmented version of Machine Learning (ML), which in turn is a subgroup of Artificial Intelligence (AI), that uses layers of neural networks, similar to human brain, for performing complex tasks quickly and accurately. AI can recognize patterns in a large volume of data and extract characteristics imperceptible to the human eye (1). Convolutional Neural Network (CNN) is the most commonly used network of DL, which contains multiple layers, with weighted connections between neurons that are trained iteratively to improve performance. DL can be supervised or unsupervised, but most of the practical uses of DL in cancer has been with supervised learning where labelled images are used for data training (2). Despite the growing number of uses of DL in cancer mapping and diagnosis, there are uncharted territories in DL which remain to be explored to utilize it to its full capacity. Also, in spite of the revolution in cancer research that DL has ushered in, there are a lot of challenges to overcome, before DL can be widely used and accepted in every corner of the world. Role of DL in oncologyThere has been an unprecedented surge in DL based research in oncology due to the availability of big data, powerful hardware and robust algorithms. Screening and diagnosis of cancer, prediction of treatment response, and survival outcome and recurrence prediction, are the various roles of ML and DL in cancer management. AI algorithms integrated with clinical decision support (CDS) tools can automatically mine electronic health record (EHR) and identify cohort that would benefit maximum from Frontiers in Oncology frontiersin.org 01

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Imaging Biobanks, Big Data, and Population-Based Imaging Biomarkers

Cited by 3 publications

References 29 publications

DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project

DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

Editorial: The use of deep learning in mapping and diagnosis of cancers

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