Multimodal data integration in early-stage breast cancer
The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-neg...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Breast |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0960977625000219 |
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| author | Arnau Llinas-Bertran Maria Butjosa-Espín Vittoria Barberi Jose A. Seoane |
| author_facet | Arnau Llinas-Bertran Maria Butjosa-Espín Vittoria Barberi Jose A. Seoane |
| author_sort | Arnau Llinas-Bertran |
| collection | DOAJ |
| description | The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors.The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers.This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings. |
| format | Article |
| id | doaj-art-92a595f09e8f4102908c394b3d56f390 |
| institution | Kabale University |
| issn | 1532-3080 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Breast |
| spelling | doaj-art-92a595f09e8f4102908c394b3d56f3902025-02-09T04:59:43ZengElsevierBreast1532-30802025-04-0180103892Multimodal data integration in early-stage breast cancerArnau Llinas-Bertran0Maria Butjosa-Espín1Vittoria Barberi2Jose A. Seoane3Cancer Computational Biology Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, SpainCancer Computational Biology Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, SpainBreast Cancer Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, SpainCancer Computational Biology Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain; Corresponding author. Vall d’Hebron Institute of Oncology (VHIO), Centro Saturnino, Carrer Saturnino Calleja 11-13, 08035, Barcelona, Spain.The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors.The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers.This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.http://www.sciencedirect.com/science/article/pii/S0960977625000219Data integrationMachine learningDeep learningStratificationMultimodal data integrationMulti-omics |
| spellingShingle | Arnau Llinas-Bertran Maria Butjosa-Espín Vittoria Barberi Jose A. Seoane Multimodal data integration in early-stage breast cancer Breast Data integration Machine learning Deep learning Stratification Multimodal data integration Multi-omics |
| title | Multimodal data integration in early-stage breast cancer |
| title_full | Multimodal data integration in early-stage breast cancer |
| title_fullStr | Multimodal data integration in early-stage breast cancer |
| title_full_unstemmed | Multimodal data integration in early-stage breast cancer |
| title_short | Multimodal data integration in early-stage breast cancer |
| title_sort | multimodal data integration in early stage breast cancer |
| topic | Data integration Machine learning Deep learning Stratification Multimodal data integration Multi-omics |
| url | http://www.sciencedirect.com/science/article/pii/S0960977625000219 |
| work_keys_str_mv | AT arnaullinasbertran multimodaldataintegrationinearlystagebreastcancer AT mariabutjosaespin multimodaldataintegrationinearlystagebreastcancer AT vittoriabarberi multimodaldataintegrationinearlystagebreastcancer AT joseaseoane multimodaldataintegrationinearlystagebreastcancer |