General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review
Due to the promise of transforming healthcare and medicine that Artificial Intelligence (AI) has posed, the number of applications has increased exponentially. These applications range from screening and disease diagnosis to prognosis, treatment planning, and follow-up. In complex topics such as chi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | EJC Paediatric Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772610X24000564 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850061473285406720 |
|---|---|
| author | Gustavo Hernández-Peñaloza Silvia Uribe Francisco Moreno García Norbert Graf Federico Álvarez |
| author_facet | Gustavo Hernández-Peñaloza Silvia Uribe Francisco Moreno García Norbert Graf Federico Álvarez |
| author_sort | Gustavo Hernández-Peñaloza |
| collection | DOAJ |
| description | Due to the promise of transforming healthcare and medicine that Artificial Intelligence (AI) has posed, the number of applications has increased exponentially. These applications range from screening and disease diagnosis to prognosis, treatment planning, and follow-up. In complex topics such as childhood cancer, these techniques are being expanded with the ambition of improving the quality of care by allowing healthcare professionals to make more informed decisions. However, the adequate application of such techniques heavily depends on the data, which creates a set of challenges including collection, bias, and scarcity among others. Furthermore, ethical, legal, and regulatory frameworks increase even more the difficulties to develop AI-powered solutions. In this paper, we present an exhaustive literature review to identify and analyse public datasets targeting two common childhood cancer types, such as neuroblastoma and nephroblastoma. Moreover, the complex context for the development of AI- based software solutions is outlined. It includes the description of the most relevant techniques to address problems associated with data sharing and training. Finally, a set of code snippets is provided to perform exploratory analysis for the available data. |
| format | Article |
| id | doaj-art-cdff557887bf4990a01cc9a81d20fdce |
| institution | DOAJ |
| issn | 2772-610X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EJC Paediatric Oncology |
| spelling | doaj-art-cdff557887bf4990a01cc9a81d20fdce2025-08-20T02:50:13ZengElsevierEJC Paediatric Oncology2772-610X2024-12-01410019610.1016/j.ejcped.2024.100196General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A reviewGustavo Hernández-Peñaloza0Silvia Uribe1Francisco Moreno García2Norbert Graf3Federico Álvarez4Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain; Corresponding author.Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, SpainEscuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, SpainDep. Paediatric Oncology and Haematology, Saarland University, Homburg, GermanyEscuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, SpainDue to the promise of transforming healthcare and medicine that Artificial Intelligence (AI) has posed, the number of applications has increased exponentially. These applications range from screening and disease diagnosis to prognosis, treatment planning, and follow-up. In complex topics such as childhood cancer, these techniques are being expanded with the ambition of improving the quality of care by allowing healthcare professionals to make more informed decisions. However, the adequate application of such techniques heavily depends on the data, which creates a set of challenges including collection, bias, and scarcity among others. Furthermore, ethical, legal, and regulatory frameworks increase even more the difficulties to develop AI-powered solutions. In this paper, we present an exhaustive literature review to identify and analyse public datasets targeting two common childhood cancer types, such as neuroblastoma and nephroblastoma. Moreover, the complex context for the development of AI- based software solutions is outlined. It includes the description of the most relevant techniques to address problems associated with data sharing and training. Finally, a set of code snippets is provided to perform exploratory analysis for the available data.http://www.sciencedirect.com/science/article/pii/S2772610X24000564Childhood cancerPaediatric oncologyChildhood cancer patientArtificial IntelligenceDataUse of data |
| spellingShingle | Gustavo Hernández-Peñaloza Silvia Uribe Francisco Moreno García Norbert Graf Federico Álvarez General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review EJC Paediatric Oncology Childhood cancer Paediatric oncology Childhood cancer patient Artificial Intelligence Data Use of data |
| title | General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review |
| title_full | General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review |
| title_fullStr | General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review |
| title_full_unstemmed | General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review |
| title_short | General context and relevant public datasets available for improving pathways in Paediatric Cancer applying Artificial Intelligence. A review |
| title_sort | general context and relevant public datasets available for improving pathways in paediatric cancer applying artificial intelligence a review |
| topic | Childhood cancer Paediatric oncology Childhood cancer patient Artificial Intelligence Data Use of data |
| url | http://www.sciencedirect.com/science/article/pii/S2772610X24000564 |
| work_keys_str_mv | AT gustavohernandezpenaloza generalcontextandrelevantpublicdatasetsavailableforimprovingpathwaysinpaediatriccancerapplyingartificialintelligenceareview AT silviauribe generalcontextandrelevantpublicdatasetsavailableforimprovingpathwaysinpaediatriccancerapplyingartificialintelligenceareview AT franciscomorenogarcia generalcontextandrelevantpublicdatasetsavailableforimprovingpathwaysinpaediatriccancerapplyingartificialintelligenceareview AT norbertgraf generalcontextandrelevantpublicdatasetsavailableforimprovingpathwaysinpaediatriccancerapplyingartificialintelligenceareview AT federicoalvarez generalcontextandrelevantpublicdatasetsavailableforimprovingpathwaysinpaediatriccancerapplyingartificialintelligenceareview |