Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging

Tu, Shu-Ju; Chen, Weiyuan; Wu, Chiao‐En

doi:10.1007/s00330-021-07943-5

Cited by 4 publications

(1 citation statement)

References 50 publications

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“…Midya et al [ 91 ] found that image acquisition parameters (i.e., tube current and noise index) and reconstruction techniques strongly affected the reproducibility of CT-based radiomics features. There is inevitable noise interference in image acquisition, and Tu et al [ 92 ] found that in the presence of the quantum noise inherent in CT images, the “ShortRunHighGrayLevelEmpha”, “ShortRunLowGrayLevelEmpha”, “LowGrayLevelRunEmpha” and “LongRunLowGrayLevelEmpha” features were the most stable, whereas the cluster shadow and maximum probability features were the most unstable. Image noise can also be reduced by increasing the tube current, as this increases the reproducibility of radiomics features [ 91 ].…”

Section: Ai-driven Radiomics Studiesmentioning

confidence: 99%

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

Zhang

Cheng

et al. 2023

Military Med Res

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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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Section: Ai-driven Radiomics Studiesmentioning

confidence: 99%

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

Zhang

Cheng

et al. 2023

Military Med Res

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Impact of improved spatial resolution on radiomic features using photon-counting-detector CT

Dunning

Rajendran

Fletcher

et al. 2022

Medical Imaging 2022: Image Processing

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Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

Hussain,

Lafarga-Osuna,

Ali

et al. 2023

BMC Bioinformatics

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Background Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. Objective and methods This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. Results A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. Conclusion Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.

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Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging

Cited by 4 publications

References 50 publications

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

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

Impact of improved spatial resolution on radiomic features using photon-counting-detector CT

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

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