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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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Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma
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Korte, J. C., Cardenas, C. E., Hardcastle, N., Kron, T., Wang, J., Bahig, H., Elgohari, B., Ger, R. B., Court, L. E., Fuller, C. D., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-96600-4 · Cited by 40
Xu, Y., Lü, L., Sun, S. H., Linning, E., Lian, W., Yang, H., Schwartz, L. H., Yang, Z., & Zhao, B. (2021) · European Radiology · doi:10.1007/s00330-021-08274-1 · Cited by 40
Jimenez, J. E., Abdelhafez, A. H., Mittendorf, E. A., Elshafeey, N. A., Yung, J. P., Litton, J. K., Adrada, B. E., Candelaria, R. P., White, J. B., Thompson, A. M., et al. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110220 · Cited by 40
Non-Operative Management of Patients with Rectal Cancer: Lessons Learnt from the OPRA Trial
Goffredo, P., Quezada-Díaz, F., García‐Aguilar, J., & Smith, J. J. (2022) · Cancers · doi:10.3390/cancers14133204 · Cited by 39
Standardized Data Collection to Build Prediction Models in Oncology: A Prototype for Rectal Cancer
Meldolesi, E., van Soest, J., Damiani, A., Dekker, A. L. A. J., Alitto, A. R., Campitelli, M., Dinapoli, N., Gatta, R., Gambacorta, M. A., Lanzotti, V., et al. (2015) · Future Oncology · doi:10.2217/fon.15.295 · Cited by 39
Self-citationApplied mathematics and nonlinear sciences in the war on cancer
Pérez‐García, V. M., Fitzpatrick, S. M., Pérez-Romasanta, L. A., Pešić, M., Schucht, P., Arana, E., & Sánchez‐Gómez, P. (2016) · Applied Mathematics and Nonlinear Sciences · doi:10.21042/amns.2016.2.00036 · Cited by 39
Demidov, V. V., Maeda, A., Sugita, M., Madge, V., Sadanand, S., Flueraru, C., & Vitkin, I. A. (2018) · Scientific Reports · doi:10.1038/s41598-017-18635-w · Cited by 39
2016 New Horizons Lecture: Beyond Imaging—Radiology of Tomorrow
Hricak, H. (2018) · Radiology · doi:10.1148/radiol.2017171503 · Cited by 41
Chirra, P. V., Leo, P., Yim, M. C., Bloch, B., Rastinehad, A. R., Purysko, A. S., Rosen, M. A., Madabhushi, A., & Viswanath, S. E. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.024502 · Cited by 39
Park, J. E., Ham, S., Kim, H. S., Park, S. Y., Yun, J., Lee, H., Choi, S. H., & Kim, N. (2020) · European Radiology · doi:10.1007/s00330-020-07414-3 · Cited by 39
Lin, F., Wang, Z., Zhang, K., Yang, P., Ma, H., Shi, Y., Liu, M., Wang, Q., Cui, J., Mao, N., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.573630 · Cited by 39
Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer
Aonpong, P., Iwamoto, Y., Han, X., Lin, L., & Chen, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3088234 · Cited by 39
Sellami, S., Bourbonne, V., Hatt, M., Tixier, F., Bouzid, D., Lucia, F., Pradier, O., Goasduff, G., Visvikis, D., & Schick, U. (2021) · Acta oncologica · doi:10.1080/0284186x.2021.1983207 · Cited by 30
“Radiomics, which corresponds to the high throughput extraction of quantitative imaging features from medical images, could provide useful quantitative information to develop models for cancer diagnosis and prognosis, to guide clinical decisions in HNSCC [2,3].”
BackgroundInfluentialMammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs
Siviengphanom, S., Gandomkar, Z., Lewis, S. J., & Brennan, P. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.09.025 · Cited by 40
Cordova, J. S., Schreibmann, E., Hadjipanayis, C. G., Guo, Y., Shu, H. G., Shim, H., & Holder, C. A. (2014) · Translational Oncology · doi:10.1593/tlo.13835 · Cited by 39
“[13] Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, et al.”
BackgroundAn annotated test-retest collection of prostate multiparametric MRI
Fedorov, A., Schwier, M., Clunie, D., Herz, C., Pieper, S., Kikinis, R., Tempany, C. M., & Fennessy, F. (2018) · Scientific Data · doi:10.1038/sdata.2018.281 · Cited by 39
Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)
Hajjo, R., Sabbah, D. A., Bardaweel, S. K., & Tropsha, A. (2021) · Diagnostics · doi:10.3390/diagnostics11050742 · Cited by 38
“Diverse cancer types [52,53] Radiomic signature [54] DCE-MR parameters Q Data endpoints, feature detection protocols, and classifiers are important factors in lung cancer prediction [55] Radiomic signature is significantly associated with lymph node (LN) status in colorectal cancer [56] Evaluating therapeutic effect s…”
BackgroundInsights into radiomics: impact of feature selection and classification
Perniciano, A., Loddo, A., Ruberto, C. D., & Pes, B. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20388-4 · Cited by 50
“However, this method is highly time-consuming, rendering it impractical for use with large datasets [55].”
Blackledge, M., Collins, D. J., Koh, D., & Leach, M. O. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.12.002 · Cited by 39
Torheim, T., Malinen, E., Hole, K. H., Lund, K. V., Indahl, U. G., Lyng, H., Kvaal, K., & Futsæther, C. M. (2017) · Acta Oncologica · doi:10.1080/0284186x.2017.1285499 · Cited by 38
“An automatic tumour segmentation method could therefore be a valuable and time saving tool for radiologists [6,9].”
BackgroundMethodTexture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment
Betrouni, N., Yasmina, M., Bombois, S., Pétrault, M., Dondaine, T., Lachaud, C., Laloux, C., Mendyk, A., Hénon, H., & Bordet, R. (2019) · Translational Stroke Research · doi:10.1007/s12975-019-00746-3 · Cited by 38
CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor
Zhao, Z., Bian, Y., Jiang, H., Fang, X., Li, J., Cao, K., Ma, C., Wang, L., Zheng, J., Yue, X., et al. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.01.002 · Cited by 38
Lü, L., Sun, S. H., Yang, H., Linning, E., Guo, P., Schwartz, L. H., & Zhao, B. (2020) · Tomography · doi:10.18383/j.tom.2020.00017 · Cited by 36
Kawka, M., Dawidziuk, A., Jiao, L. R., & Gall, T. (2021) · Translational Gastroenterology and Hepatology · doi:10.21037/tgh-20-242 · Cited by 39
“Most recently, radiomics approach to imaging analysis has been proposed, involving a multi-step process to derive large datasets of radiological features, via image acquisition, segmentation, feature extraction and automated analysis of patterns by using high throughput computing (67).”
MethodCui, L., Fu, Y., Liu, L., Wei, Y., Wu, X., Xi, Y.-B., Wang, H.-N., Yin, H. J., & Yin, H. J. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.15046 · Cited by 40
“Radiomics (Kumar et al., 2012; Lambin et al., 2012), a central technique aiding decision-making in clinical practice, is considered as “the bridge between medical imaging and personalised medicine” (Lambin et al.”
BackgroundRe, M. D., Cucchiara, F., Rofi, E., Fontanelli, L., Petrini, I., Gri, N., Pasquini, G., Rizzo, M., Gabelloni, M., Belluomini, L., et al. (2020) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-020-02810-6 · Cited by 38
Cangır, A., Orhan, K., Kahya, Y., Özakıncı, H., Kazak, B. B., Balcı, B. M. K., Karasoy, D., & Uzun, Ç. (2021) · World Journal of Surgical Oncology · doi:10.1186/s12957-021-02259-6 · Cited by 38
Shu, Z., Mao, D., Xu, Y., Shao, Y., Pang, P., & Gong, X. (2021) · Therapeutic Advances in Neurological Disorders · doi:10.1177/17562864211029551 · Cited by 39
“20,21 In addition, it is known that the E4 allele of the apolipoprotein (APOE4) is the major known genetic risk factor for late-onset AD.”
Rinaldi, L. M., Angelis, S. P. D., Raimondi, S., Rizzo, S., Fanciullo, C., Rampinelli, C., Mariani, M., Lascialfari, A., Cremonesi, M., Orecchia, R., et al. (2022) · European Radiology Experimental · doi:10.1186/s41747-021-00258-6 · Cited by 38
Mattonen, S. A., Tetar, S., Palma, D. A., Louie, A. V., Senan, S., & Ward, A. D. (2015) · Journal of medical imaging · doi:10.1117/1.jmi.2.4.041010 · Cited by 38
“This study also did not consider the effect of different scanners and reconstruction techniques in the analysis; however, all images were taken with the same acquisition and reconstruction parameters to minimize discrepancies.(34) Determining the effect of different acquisition parameters on classification results is…”
BackgroundCucchiara, F., Petrini, I., Romei, C., Crucitta, S., Lucchesi, M., Valleggi, S., Scavone, C., Capuano, A., LIPERI, A. D., Chella, A., et al. (2021) · Pharmacological Research · doi:10.1016/j.phrs.2021.105643 · Cited by 39
Thrall, J. H. (2016) · Radiology · doi:10.1148/radiol.2016160293 · Cited by 39
“The term radiomics has been used to describe a particular application of advanced image analysis—the extraction of numeric elements representing quantitative phenotypes in cancer (26).”
MethodMagnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy
Yang, F., Ford, J. C., Dogan, N., Padgett, K. R., Breto, A., Abramowitz, M. C., Pra, A. D., Pollack, A. S., & Stoyanova, R. S. (2018) · Translational Andrology and Urology · doi:10.21037/tau.2018.06.05 · Cited by 37
““Radiomics”, as it refers to the extraction and analysis of large number of advanced quantitative imaging features from medical images using high throughput methods (22,23), can probe and capture inherent tissue characteristics in the multiple series of prostate mpMRI and quantify the regions of interest (24).”
Background
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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