Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Military Medical Research

Review

Jia-Bao Sheng¹  Saikit Lam¹  Shara W. Y. Lee¹  Jiang Zhang¹  Ta Zhou¹  Zong-Rui Ma¹  Victor C. W. Tam¹  Xin-Zhi Teng¹  Jing Cai²  Yu-Ting Cheng³  Xin-Yun Zhang³  Yuan-Peng Zhang⁴  Bing Li⁵  Hong Ge⁵ 

[1] Department of Health Technology and Informatics, the Hong Kong Polytechnic University, 999077, Hong Kong, China;Department of Health Technology and Informatics, the Hong Kong Polytechnic University, 999077, Hong Kong, China;The Hong Kong Polytechnic University Shenzhen Research Institute, 518000, Shenzhen, Guangdong, China;Department of Medical Informatics, Nantong University, 226001, Nantong, Jiangsu, China;Department of Medical Informatics, Nantong University, 226001, Nantong, Jiangsu, China;Department of Health Technology and Informatics, the Hong Kong Polytechnic University, 999077, Hong Kong, China;The Hong Kong Polytechnic University Shenzhen Research Institute, 518000, Shenzhen, Guangdong, China;Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, 450008, Zhengzhou, Henan, China;

关键词: Artificial intelligence;    Radiomics;    Feature extraction;    Feature selection;    Modeling;    Interpretability;    Multi-modalities;    Head and neck cancer;   

DOI  :  10.1186/s40779-023-00458-8

 received in 2022-12-22, accepted in 2023-05-04,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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