Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers
Abstract High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-09-01
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| Series: | European Radiology Experimental |
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| Online Access: | https://doi.org/10.1186/s41747-023-00364-7 |
| _version_ | 1797453936475504640 |
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| author | Sepideh Hatamikia Stephanie Nougaret Camilla Panico Giacomo Avesani Camilla Nero Luca Boldrini Evis Sala Ramona Woitek |
| author_facet | Sepideh Hatamikia Stephanie Nougaret Camilla Panico Giacomo Avesani Camilla Nero Luca Boldrini Evis Sala Ramona Woitek |
| author_sort | Sepideh Hatamikia |
| collection | DOAJ |
| description | Abstract High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models. Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks. Key points • This review presents studies using multiomics and artificial intelligence in ovarian cancer. • Current literature proves that integrative multiomics outperform models using single data types. • Around 60% of studies used a combination of imaging with clinical data. • The combination of genomics and transcriptomics with imaging data was infrequently used. Graphical Abstract |
| first_indexed | 2024-03-09T15:30:04Z |
| format | Article |
| id | doaj.art-46c78e8ec952454bb531293c5fd408ab |
| institution | Directory Open Access Journal |
| issn | 2509-9280 |
| language | English |
| last_indexed | 2024-03-09T15:30:04Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj.art-46c78e8ec952454bb531293c5fd408ab2023-11-26T12:17:37ZengSpringerOpenEuropean Radiology Experimental2509-92802023-09-017111710.1186/s41747-023-00364-7Ovarian cancer beyond imaging: integration of AI and multiomics biomarkersSepideh Hatamikia0Stephanie Nougaret1Camilla Panico2Giacomo Avesani3Camilla Nero4Luca Boldrini5Evis Sala6Ramona Woitek7Research Center for Medical Image Analysis and AI (MIAAI), Danube Private UniversityDepartment of Radiology, Montpellier Cancer Institute, University of MontpellierDipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCSDipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCSScienze Della Salute Della Donna, del bambino e Di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCSDipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCSDipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCSResearch Center for Medical Image Analysis and AI (MIAAI), Danube Private UniversityAbstract High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models. Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks. Key points • This review presents studies using multiomics and artificial intelligence in ovarian cancer. • Current literature proves that integrative multiomics outperform models using single data types. • Around 60% of studies used a combination of imaging with clinical data. • The combination of genomics and transcriptomics with imaging data was infrequently used. Graphical Abstracthttps://doi.org/10.1186/s41747-023-00364-7Artificial intelligenceBiomarkersDiagnostic imagingOvarian neoplasmsMultiomics |
| spellingShingle | Sepideh Hatamikia Stephanie Nougaret Camilla Panico Giacomo Avesani Camilla Nero Luca Boldrini Evis Sala Ramona Woitek Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers European Radiology Experimental Artificial intelligence Biomarkers Diagnostic imaging Ovarian neoplasms Multiomics |
| title | Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers |
| title_full | Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers |
| title_fullStr | Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers |
| title_full_unstemmed | Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers |
| title_short | Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers |
| title_sort | ovarian cancer beyond imaging integration of ai and multiomics biomarkers |
| topic | Artificial intelligence Biomarkers Diagnostic imaging Ovarian neoplasms Multiomics |
| url | https://doi.org/10.1186/s41747-023-00364-7 |
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