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Title: | Artificial intelligence-driven radiomics study in cancer : the role of feature engineering and modeling | Authors: | Zhang, YP Zhang, XY Cheng, YT Li, B Teng, XZ Zhang, J Lam, S Zhou, T Ma, ZR Sheng, JB Tam, VCW Lee, SWY Ge, H Cai, J |
Issue Date: | 2023 | Source: | Military medical research, 2023, v. 10, 22 | Abstract: | Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’ anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research. | Keywords: | Artificial intelligence Feature extraction Feature selection Head and neck cancer Interpretability Modeling Multi-modalities Radiomics |
Publisher: | BioMed Central Ltd. | Journal: | Military medical research | EISSN: | 2054-9369 | DOI: | 10.1186/s40779-023-00458-8 | Rights: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. The following publication Zhang, YP., Zhang, XY., Cheng, YT. et al. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Military Med Res 10, 22 (2023) is available at https://doi.org/10.1186/s40779-023-00458-8. |
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