Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms
Among brain tumors, glioblastoma (GBM) is the most common and the most aggressive type, and brain metastases (BMs) occur in 20%–40% of cancer patients. Even with intensive treatment involving radiotherapy and surgery, which frequently leads to cognitive decline due to doses on healthy brain tissue,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1497195/full |
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| author | Noémie N. Moreau Noémie N. Moreau Samuel Valable Cyril Jaudet Loïse Dessoude Leleu Thomas Romain Hérault Romain Modzelewski Romain Modzelewski Dinu Stefan Juliette Thariat Juliette Thariat Alexis Lechervy Aurélien Corroyer-Dulmont Aurélien Corroyer-Dulmont |
| author_facet | Noémie N. Moreau Noémie N. Moreau Samuel Valable Cyril Jaudet Loïse Dessoude Leleu Thomas Romain Hérault Romain Modzelewski Romain Modzelewski Dinu Stefan Juliette Thariat Juliette Thariat Alexis Lechervy Aurélien Corroyer-Dulmont Aurélien Corroyer-Dulmont |
| author_sort | Noémie N. Moreau |
| collection | DOAJ |
| description | Among brain tumors, glioblastoma (GBM) is the most common and the most aggressive type, and brain metastases (BMs) occur in 20%–40% of cancer patients. Even with intensive treatment involving radiotherapy and surgery, which frequently leads to cognitive decline due to doses on healthy brain tissue, the median survival is 15 months for GBM and about 6 to 9 months for BM. Despite these treatments, GBM patients respond heterogeneously as do patients with BM. Following standard of care, some patients will respond and have an overall survival of more than 30 months and others will not respond and will die within a few months. Differentiating non-responders from responders as early as possible in order to tailor treatment in a personalized medicine fashion to optimize tumor control and preserve healthy brain tissue is the most pressing unmet therapeutic challenge. Innovative computer solutions recently emerged and could provide help to this challenge. This review will focus on 52 published research studies between 2013 and 2024 on (1) the early characterization of treatment efficacy with biomarker imaging and radiomic-based solutions, (2) predictive solutions with radiomic and artificial intelligence-based solutions, (3) interest in other biomarkers, and (4) the importance of the prediction of new treatment modalities’ efficacy. |
| format | Article |
| id | doaj-art-1bc0ba21b5134b61bed0d7f2e6e885e6 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-1bc0ba21b5134b61bed0d7f2e6e885e62025-01-30T06:22:34ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011510.3389/fonc.2025.14971951497195Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithmsNoémie N. Moreau0Noémie N. Moreau1Samuel Valable2Cyril Jaudet3Loïse Dessoude4Leleu Thomas5Romain Hérault6Romain Modzelewski7Romain Modzelewski8Dinu Stefan9Juliette Thariat10Juliette Thariat11Alexis Lechervy12Aurélien Corroyer-Dulmont13Aurélien Corroyer-Dulmont14Medical Physics Department, Centre François Baclesse, Caen, FranceUniversité de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP CYCERON, Caen, FranceUniversité de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP CYCERON, Caen, FranceMedical Physics Department, Centre François Baclesse, Caen, FranceRadiation Oncology Department, Centre François Baclesse, Caen, FranceRadiation Oncology Department, Centre François Baclesse, Caen, FranceUMR GREYC, Normandie Univ, UNICAEN, ENSICAEN, CNRS, Caen, FranceLITIS - EA4108-Quantif, University of Rouen, Rouen, FranceNuclear Medicine Department, Henri Becquerel Center, Rouen, FranceRadiation Oncology Department, Centre François Baclesse, Caen, FranceRadiation Oncology Department, Centre François Baclesse, Caen, FranceENSICAEN, CNRS/IN2P3, LPC UMR6534, Caen, FranceUMR GREYC, Normandie Univ, UNICAEN, ENSICAEN, CNRS, Caen, FranceMedical Physics Department, Centre François Baclesse, Caen, FranceUniversité de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP CYCERON, Caen, FranceAmong brain tumors, glioblastoma (GBM) is the most common and the most aggressive type, and brain metastases (BMs) occur in 20%–40% of cancer patients. Even with intensive treatment involving radiotherapy and surgery, which frequently leads to cognitive decline due to doses on healthy brain tissue, the median survival is 15 months for GBM and about 6 to 9 months for BM. Despite these treatments, GBM patients respond heterogeneously as do patients with BM. Following standard of care, some patients will respond and have an overall survival of more than 30 months and others will not respond and will die within a few months. Differentiating non-responders from responders as early as possible in order to tailor treatment in a personalized medicine fashion to optimize tumor control and preserve healthy brain tissue is the most pressing unmet therapeutic challenge. Innovative computer solutions recently emerged and could provide help to this challenge. This review will focus on 52 published research studies between 2013 and 2024 on (1) the early characterization of treatment efficacy with biomarker imaging and radiomic-based solutions, (2) predictive solutions with radiomic and artificial intelligence-based solutions, (3) interest in other biomarkers, and (4) the importance of the prediction of new treatment modalities’ efficacy.https://www.frontiersin.org/articles/10.3389/fonc.2025.1497195/fullGlioblastoma (GBM)machine learning (ML)brain tumorsartificial intelligencetreatment efficacymedical imaging |
| spellingShingle | Noémie N. Moreau Noémie N. Moreau Samuel Valable Cyril Jaudet Loïse Dessoude Leleu Thomas Romain Hérault Romain Modzelewski Romain Modzelewski Dinu Stefan Juliette Thariat Juliette Thariat Alexis Lechervy Aurélien Corroyer-Dulmont Aurélien Corroyer-Dulmont Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms Frontiers in Oncology Glioblastoma (GBM) machine learning (ML) brain tumors artificial intelligence treatment efficacy medical imaging |
| title | Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms |
| title_full | Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms |
| title_fullStr | Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms |
| title_full_unstemmed | Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms |
| title_short | Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms |
| title_sort | early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging based radiomics and artificial intelligence algorithms |
| topic | Glioblastoma (GBM) machine learning (ML) brain tumors artificial intelligence treatment efficacy medical imaging |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1497195/full |
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