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

Publication ,  Journal Article
Zhang, Y-P; Zhang, X-Y; Cheng, Y-T; Li, B; Teng, X-Z; Zhang, J; Lam, S; Zhou, T; Ma, Z-R; Sheng, J-B; Tam, VCW; Lee, SWY; Ge, H; Cai, J
Published in: Mil Med Res
May 16, 2023

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.

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Published In

Mil Med Res

DOI

EISSN

2054-9369

Publication Date

May 16, 2023

Volume

10

Issue

1

Start / End Page

22

Location

England

Related Subject Headings

  • Reproducibility of Results
  • Prognosis
  • Neoplasms
  • Machine Learning
  • Humans
  • Artificial Intelligence
  • 3202 Clinical sciences
 

Citation

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Zhang, Y.-P., Zhang, X.-Y., Cheng, Y.-T., Li, B., Teng, X.-Z., Zhang, J., … Cai, J. (2023). Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res, 10(1), 22. https://doi.org/10.1186/s40779-023-00458-8
Zhang, Yuan-Peng, Xin-Yun Zhang, Yu-Ting Cheng, Bing Li, Xin-Zhi Teng, Jiang Zhang, Saikit Lam, et al. “Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Mil Med Res 10, no. 1 (May 16, 2023): 22. https://doi.org/10.1186/s40779-023-00458-8.
Zhang Y-P, Zhang X-Y, Cheng Y-T, Li B, Teng X-Z, Zhang J, et al. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res. 2023 May 16;10(1):22.
Zhang, Yuan-Peng, et al. “Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Mil Med Res, vol. 10, no. 1, May 2023, p. 22. Pubmed, doi:10.1186/s40779-023-00458-8.
Zhang Y-P, Zhang X-Y, Cheng Y-T, Li B, Teng X-Z, Zhang J, Lam S, Zhou T, Ma Z-R, Sheng J-B, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res. 2023 May 16;10(1):22.
Journal cover image

Published In

Mil Med Res

DOI

EISSN

2054-9369

Publication Date

May 16, 2023

Volume

10

Issue

1

Start / End Page

22

Location

England

Related Subject Headings

  • Reproducibility of Results
  • Prognosis
  • Neoplasms
  • Machine Learning
  • Humans
  • Artificial Intelligence
  • 3202 Clinical sciences