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Radiomics: the process and the challenges
Open accessAbstract
"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.
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“The first such study used 182 texture features in combination with 22 semantic features to describe CT images of lung cancer (24).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [24]
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MethodApproaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning
Yang, X., Yang, S., Sun, W., Tan, H. L., Lin, K., Li, P., Wang, Z., & Zhang, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-51666-8 · Cited by 23
“Radi-omics employs advanced computational approaches to convert medical images into quantitative features 20,21 and provides new perspectives to aid in the diagnosis, recognition, treatment response and prognosis of diseases 22,23 .”
Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI
Ali, M. U., Benfante, V., Cutaia, G., Salvaggio, L., Rubino, S. M., Portoghese, M., Ferraro, M., Corso, R., Piraino, G., Ingrassia, T., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51026-7_8 · Cited by 21
Fundamentals of Texture Processing for Biomedical Image Analysis
Depeursinge, A., Fageot, J., & Al-Kadi, O. S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00001-6 · Cited by 23
Radiomics derived from amino-acid PET and conventional MRI in patients with high-grade gliomas
Lohmann, P., Köcher, M., Steger, J., & Galldiks, N. (2018) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.18.03095-9 · Cited by 21
Chen, X., Feng, B., Li, C., Duan, X., Chen, Y., Li, Z., Liu, Z., Zhang, C., & Long, W. (2020) · Oncology Reports · doi:10.3892/or.2020.7497 · Cited by 21
“Radiomics is a field of medical research in which a large amount of high-quality, quantitative, mineable data is extracted from conventional medical images (11,12).”
Gao, F., Qiao, K., Yan, B., Wu, M., Wang, L., Chen, J., & Shi, D. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.06.018 · Cited by 20
Hashido, T., Saito, S., & Ishida, T. (2021) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001180 · Cited by 21
Santiago, R., Jiménez, J. O., Forghani, R., Muthukrishnan, N., Corpo, O. D., Karthigesu, S., Haider, M. Y., Reinhold, C., & Assouline, S. E. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101188 · Cited by 21
Meng, Y., Zhang, H., Li, Q., Liu, F., Fang, X., Li, J., Yu, J., Feng, X., Lu, J., Bian, Y., et al. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.08.013 · Cited by 21
A primer on texture analysis in abdominal radiology
Horvat, N. D. S. M. R., Miranda, J., Homsi, M. E., Peoples, J., Long, N. M., Simpson, A. L., & Gian, R. K. (2021) · Abdominal Radiology · doi:10.1007/s00261-021-03359-3 · Cited by 21
“A consistent challenge in radiomics studies is that the number of features extracted is usually much higher than the number of patients and events studied, which increases the risk of overfitting of the model [26].”
BackgroundIntarak, S., Chongpison, Y., Vimolnoch, M., Oonsiri, S., Kitpanit, S., Prayongrat, A., Kannarunimit, D., Chakkabat, C., Sriswasdi, S., Lertbutsayanukul, C., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.775248 · Cited by 21
A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images
Duan, C., Zhou, X., Wang, J., Li, N., Liu, F., Gao, S., Liu, X., & Xu, W. (2022) · British Journal of Radiology · doi:10.1259/bjr.20220141 · Cited by 21
Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram
Jing, G., Xing, P., Li, Z., Ma, X., Lu, H., Shao, C., Lü, Y., Lu, J., & Shen, F. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.918830 · Cited by 22
Guan, X., Lu, N., & Zhang, J. (2022) · Digestive Diseases and Sciences · doi:10.1007/s10620-022-07640-3 · Cited by 21
Zhou, Z., Zhang, Z., Gao, A., Tai, D., Wu, S., & Tsui, P. (2022) · Ultrasonic Imaging · doi:10.1177/01617346221120070 · Cited by 21
Yang, W., Zhu, F., & Chen, W. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110528 · Cited by 21
Nicosia, L., Pesapane, F., Bozzini, A. C., Latronico, A., Rotili, A., Ferrari, F., Signorelli, G., Raimondi, S., Vignati, S., Gaeta, A., et al. (2023) · Cancers · doi:10.3390/cancers15030964 · Cited by 21
The combination of radiomics features and VASARI standard to predict glioma grade
You, W., Mao, Y., Jiao, X., Wang, D., Liu, J., Lei, P., & Liao, W. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1083216 · Cited by 21
Jiang, Z., Xie, W., Zhou, X., Pan, W., Jiang, S., Zhang, X., Zhang, M., Zhang, Z., Lu, Y. Z., & Wang, D. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01438-1 · Cited by 21
“…feature types included: 1) shape-based (SB); 2) first-order statistics (FOS); 3) gray-level co-occurrence matrix (GLCM); 4) gray-level run-length matrix (GLRLM); 5) gray-level size zone matrix (GLSZM); and 6) gray-level correlation matrix (GLDM), which is consistent with previous studies [22, 23].”
ResultDeng, F., Liu, Z., Wei, F., Niu, L., Chu, X., Cheng, Q., Zhang, Z., Zhou, R., & Yang, G. (2023) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-023-01300-0 · Cited by 23
““Radiomics” refers to the extraction of many quantitative features from standard clinical imaging data, and then convert these to higher-dimensional mineable data [12, 13].”
BackgroundWang, H., Xu, S., Fang, K., Dai, Z., Wei, G.-Z., & Chen, L.-F. (2023) · Journal of bone oncology · doi:10.1016/j.jbo.2023.100498 · Cited by 25
“It allows for the selection of an appropriate treatment strategy prior to the patient’s health deterioration and serves as a solid foundation for informed decision-making regarding interventional treatments [3,4].”
BackgroundZheng, X., Liu, K., Li, C., Zhu, C., Gao, Y., Li, J., & Wu, X. (2023) · La radiologia medica · doi:10.1007/s11547-023-01702-w · Cited by 21
“Radiomics, first proposed by Dutch scholar Kumar in 2012 [10], refers to the rapid extraction of a large number of quantitative features from medical images through high-throughput calculation, and the establishment of a prediction model through machine learning for a deeper level of mining,…”
BackgroundZhou, Z., Qian, X., Hu, J., Chen, G., Zhang, C., Zhu, J., & Dai, Y. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-20 · Cited by 21
“The discriminative features represent the morphologic and textural changes of lesions that are associated with disease processes, which are considered to be noninvasive biomarkers capable of predicting the biologic behavior of the tumor (11,12).”
The Pursuit of Noninvasive Diagnosis of Lung Cancer
Atwater, T., Cook, C., & Massion, P. P. (2016) · Seminars in Respiratory and Critical Care Medicine · doi:10.1055/s-0036-1592314 · Cited by 20
Mühlberg, A., Museyko, O., Laredo, J., & Engelke, K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175174 · Cited by 18
Frood, R. T., Palkhi, E., Barnfield, M. C., Prestwich, R. J. D., Vaidyanathan, S., & Scarsbrook, A. F. (2018) · European Radiology · doi:10.1007/s00330-018-5524-x · Cited by 21
Radiomics in RayPlus: a Web-Based Tool for Texture Analysis in Medical Images
Yuan, R., Shi, S., Chen, J., & Cheng, G. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0128-1 · Cited by 21
Radiomics approaches in gastric cancer
Wang, Y., & Jin, Z. (2019) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000360 · Cited by 21
Dufau, J., Bouhamama, A., Leporq, B., Malaureille, L., Beuf, O., Gouin, F., Pilleul, F., & Marec-Berard, P. (2019) · Bulletin du Cancer · doi:10.1016/j.bulcan.2019.07.005 · Cited by 22
Yang, L., Zheng, T., Dong, Y., Wang, Z., Liu, D., Du, J., Wu, S., Shi, Q., & Liu, L. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27390 · Cited by 21
“Compared with CT examination based on singledensity imaging, the texture parameters obtained by MR examination are more abundant and diverse.(15) In addition to CT and MR, positron-emission tomography / computed tomography (PET/CT), as a molecular imaging technology, has demonstrated clear usefulness for preoperative…”
MethodDuan, C., Liu, F., Gao, S., Zhao, J., Niu, L., Li, N., Liu, S., Wang, G., Zhou, X., Ren, Y., et al. (2021) · Clinical Neuroradiology · doi:10.1007/s00062-021-01040-2 · Cited by 23
Fang, R. S., Mazur, T. R., Mutic, S., & Khan, R. F. H. (2018) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2018.10.002 · Cited by 20
“Beyond scanner parameters, variables including software [13], reconstruction algorithm [3,14] and object location [15] have also been demonstrated to alter CT numbers.”
MethodBeaumont, H., Iannessi, A., Bertrand, A.-S., Cucchi, J. M., & Lucidarme, O. (2021) · European Radiology · doi:10.1007/s00330-020-07641-8 · Cited by 20
Bevilacqua, V., Altini, N., Prencipe, B., Brunetti, A., Villani, L., Sacco, A., Morelli, C., Ciaccia, M., & Scardapane, A. (2021) · Electronics · doi:10.3390/electronics10202475 · Cited by 22
“Therefore, the exploitation of feature selection or dimensionality reduction techniques is strongly recommended in order to generate highly informative, reproducible, and non-redundant feature vectors [47].”
BackgroundMethod
Metadata
- Authors
- Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., Goldgof, D. B., Hall, L. O., Lambin, P., Balagurunathan, Y., Gatenby, R. A., & Gillies, R. J.
- Affiliations
- Moffitt Cancer Center (US)
- University of South Florida (US)
- Harvard University (US)
- Maastricht University Medical Centre (NL)
- Corresponding author
- Gillies, Robert J.
- ORCID
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 30
- Issue
- 9
- Pages
- 1234-1248
- ISSN
- 0730-725X (ISSN-L)
- PMC
- PMC3563280
- OpenAlex
- W2097475056
- Cited by
- 2,258
- References
- 104
- Citations by year
- 2026: 106 · 2025: 189 · 2024: 224 · 2023: 236 · 2022: 277 · 2021: 290 · 2020: 253 · 2019: 209 · 2018: 190 · 2017: 127 · 2016: 86 · 2015: 43 · 2014: 16 · 2013: 6 · 2012: 3
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Radiological Society of North America
- National Cancer Institute (U01CA143062, P30CA076292)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, quantitative imaging features, image segmentation, feature extraction, data sharing, non-small cell lung cancer, computed tomography, positron emission tomography, prediction model
- MeSH terms
- Algorithms, Carcinoma, Non-Small-Cell Lung, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Medical Informatics, Pattern Recognition, Automated, Risk Factors, Software, Tomography, X-Ray Computed, Reproducibility of Results, Multivariate Analysis, Radiation Oncology, Phantoms, Imaging, Positron-Emission Tomography
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