Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challeng...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.924245/full |
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| author | Ming Zhu Sijia Li Yu Kuang Virginia B. Hill Amy B. Heimberger Amy B. Heimberger Lijie Zhai Lijie Zhai Shengjie Zhai |
| author_facet | Ming Zhu Sijia Li Yu Kuang Virginia B. Hill Amy B. Heimberger Amy B. Heimberger Lijie Zhai Lijie Zhai Shengjie Zhai |
| author_sort | Ming Zhu |
| collection | DOAJ |
| description | Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area. |
| first_indexed | 2024-12-11T19:02:48Z |
| format | Article |
| id | doaj.art-f0886d6f6af44079ae5ac438b6dd375e |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-11T19:02:48Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-f0886d6f6af44079ae5ac438b6dd375e2022-12-22T00:53:59ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-08-011210.3389/fonc.2022.924245924245Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspectiveMing Zhu0Sijia Li1Yu Kuang2Virginia B. Hill3Amy B. Heimberger4Amy B. Heimberger5Lijie Zhai6Lijie Zhai7Shengjie Zhai8Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United StatesKirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, United StatesMedical Physics Program, Department of Health Physics, University of Nevada Las Vegas, Las Vegas, NV, United StatesDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United StatesDepartment of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United StatesMalnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United StatesDepartment of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United StatesMalnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United StatesDepartment of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United StatesRadiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.https://www.frontiersin.org/articles/10.3389/fonc.2022.924245/fullartificial intelligencemachine learningbrain tumorimmunotherapyradiomicstumor classification |
| spellingShingle | Ming Zhu Sijia Li Yu Kuang Virginia B. Hill Amy B. Heimberger Amy B. Heimberger Lijie Zhai Lijie Zhai Shengjie Zhai Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective Frontiers in Oncology artificial intelligence machine learning brain tumor immunotherapy radiomics tumor classification |
| title | Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective |
| title_full | Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective |
| title_fullStr | Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective |
| title_full_unstemmed | Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective |
| title_short | Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective |
| title_sort | artificial intelligence in the radiomic analysis of glioblastomas a review taxonomy and perspective |
| topic | artificial intelligence machine learning brain tumor immunotherapy radiomics tumor classification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.924245/full |
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