Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high h...
| Main Authors: | , , , , |
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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 |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.980793/full |
| _version_ | 1828109798818709504 |
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| author | Meredith A. Jones Warid Islam Rozwat Faiz Xuxin Chen Bin Zheng |
| author_facet | Meredith A. Jones Warid Islam Rozwat Faiz Xuxin Chen Bin Zheng |
| author_sort | Meredith A. Jones |
| collection | DOAJ |
| description | Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies. |
| first_indexed | 2024-04-11T11:08:46Z |
| format | Article |
| id | doaj.art-a645355775264551b23fbf465e10fe7e |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T11:08:46Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-a645355775264551b23fbf465e10fe7e2022-12-22T04:28:11ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-08-011210.3389/fonc.2022.980793980793Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis predictionMeredith A. Jones0Warid Islam1Rozwat Faiz2Xuxin Chen3Bin Zheng4School of Biomedical Engineering, University of Oklahoma, Norman, OK, United StatesSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United StatesSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United StatesSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United StatesSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United StatesBreast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.https://www.frontiersin.org/articles/10.3389/fonc.2022.980793/fullbreast cancermachine learningdeep learningcomputer aided detectioncomputer aided diagnosismammography |
| spellingShingle | Meredith A. Jones Warid Islam Rozwat Faiz Xuxin Chen Bin Zheng Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction Frontiers in Oncology breast cancer machine learning deep learning computer aided detection computer aided diagnosis mammography |
| title | Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction |
| title_full | Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction |
| title_fullStr | Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction |
| title_full_unstemmed | Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction |
| title_short | Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction |
| title_sort | applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction |
| topic | breast cancer machine learning deep learning computer aided detection computer aided diagnosis mammography |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.980793/full |
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