Cómo integrar la información cuantitativa en el informe radiológico del paciente oncológico

Martí‐Bonmatí, Luis; Ruiz-Martínez, E.; Ten, A.

doi:10.1016/j.rx.2018.02.005

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…Nevertheless, most results emerging from radiomic studies are negatively affected by a reduced number of cases collected, and novel biomarkers need to be validated in large multicentre populations. A small sample size can be a critical shortcoming for finding statistically significant correlations between treatment outcomes and radiomics features with high confidence intervals [80,81]. This problem is especially significant when the different data repositories that form biobanks are intended to be used as a basis for the generation of predictive models (i.e.…”

Section: Discussionmentioning

confidence: 99%

Bridging gaps between images and data: a systematic update on imaging biobanks

et al. 2022

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Background and objectiveThe systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks. Methods Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database (https:// cordis. europa. eu/ proje cts) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes. Results Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request. Conclusions Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. Key Points• Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request.• While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.

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Section: Discussionmentioning

confidence: 99%

Bridging gaps between images and data: a systematic update on imaging biobanks

et al. 2022

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“…Oncologic imaging represents a suitable field for the discovery and validation of new biomarkers from different imaging modalities (such as computed tomography, magnetic resonance, positron emission tomography, and ultrasound), since cancer patients are frequently monitored for staging and treatment response follow-up [7]. Many imaging biomarkers have been proposed over the last years to measure tumour anatomy, morphology, pathophysiology, metabolism, or molecular profiles in order to estimate different cancer hallmarks, such as proliferation/ growth, angiogenesis, and evasion or metastasis [8].…”

Section: Imaging Biobanks and In Silico Modelsmentioning

confidence: 99%

“…This project will bring major advancements in the validation of novel imaging biomarkers; it will create advanced computational models for tumour growth simulation, given response to specific CEPs. A very limited number of imaging biomarkers have been used in routine clinical practice to guide clinical decisions [7][8][9]. Therefore, PRIMAGE predictive models will enable unprecedented effectiveness in the translation from clinical Big Data to personalised predictors for malignant solid tumours, particularly NB and DIPG, by incorporating these assets for Big Data to usable clinical knowledge translation.…”

Section: Clinical Datamentioning

confidence: 99%

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

et al. 2020

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PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.

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“…Imaging biobanks store image collections, just as standard biobanks store biological samples, and donors of images can be described in the same way as donors of samples. Most imaging biobanks focus on the collection of cancer-related data and oncologic imaging biomarkers [ 14 , 15 ].…”

Section: Introductionmentioning

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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Cómo integrar la información cuantitativa en el informe radiológico del paciente oncológico

Cited by 11 publications

References 19 publications

Bridging gaps between images and data: a systematic update on imaging biobanks

Bridging gaps between images and data: a systematic update on imaging biobanks

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

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

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