Cited by 2,258Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification TechniquesMedical Imaging Techniques and Applications

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Radiomics: the process and the challenges

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Kumar, Virendra, Gu, Yuhua, Basu, Satrajit, Berglund, Anders, Eschrich, Steven A., Schabath, Matthew B., Forster, Kenneth, Aerts, Hugo J.W.L., Dekker, Andre, Fenstermacher, David, Goldgof, Dmitry B., Hall, Lawrence O., Lambin, Philippe, Balagurunathan, Yoganand, Gatenby, Robert A., Gillies, Robert J.·2012·Magnetic Resonance Imaging

DOI 10.1016/j.mri.2012.06.010PMC PMC3563280

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Abstract

"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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Background
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Radiogenomics for Precision Medicine With a Big Data Analytics Perspective
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“PET/CT images [5]–[7], [44].”
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“In a multicenter context, addressing variability in acquisition and reconstruction protocols is crucial to ensure reproducibility [29].”
Background
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Ortiz-Ramón, R., Larroza, A., Arana, E., & Moratal, D. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036869 · Cited by 55
“This new radiomics concept is referred to the analysis of medical images by an exhaustive extraction of features from regions of these images and the corresponding data mining to create predictive models to help in the decision support [7], [8].”
Method
Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening
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“…medical imaging for advanced image analysis in which quantitative features of tumor phenotypes are extracted from an imaging modality such as computed tomography, magnetic resonance imaging, or positron emission tomography (Kumar et al 2012, Lambin et al 2012, Aerts et al 2014, Zhang et al 2015).”
BackgroundMethod
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“Using the radiomic approach, imaging features extracted from the segmented regions may quantitatively reflect lesion heterogeneity [12].”
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Background
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“[88] Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D.”
Background
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Self-citation
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Background
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Method
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BackgroundSelf-citation
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“However, radiomics is a relatively new subject facing many challenges (Gillies et al. 2016; Kumar et al. 2012).”
Background
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“In addition, since the manual nodule segmentation is yet a gold standard, it suffers from significant inter-reader bias and low reproducibility [29].”
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“85 was considered a reproducible feature, based on previous studies (Kumar et al. 2012; Zhao et al. 2016; Larue et al. 2017).”
BackgroundMethod
CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma
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Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features
Bernatz, S., Ackermann, J., Mandel, P. C., Kaltenbach, B., Zhdanovich, Y., Harter, P. N., Döring, C., Hammerstingl, R., Bodelle, B., Smith, K., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07064-5 · Cited by 51
Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors
Forghani, R. (2020) · Radiology Imaging Cancer · doi:10.1148/rycan.2020190047 · Cited by 57
“The term radiomics was first introduced in the medical literature in 2012, defined as “high throughput extraction of quantitative imaging features with the intent of creating mineable databases from radiological images” (18,19).”
Background
Ultrasound Based Radiomics Features of Chronic Kidney Disease
Bandara, M. S., Gurunayaka, B., Lakraj, G. P., Pallewatte, A. S., Siribaddana, S., & Wansapura, J. P. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.01.006 · Cited by 50
Radiomics in cervical and endometrial cancer
Manganaro, L., Nicolino, G. M., Dolciami, M., Martorana, F., Stathis, A., Colombo, I., & Rizzo, S. (2021) · British Journal of Radiology · doi:10.1259/bjr.20201314 · Cited by 50
Artificial intelligence in multiparametric magnetic resonance imaging: A review
Li, C., Li, W., Liu, C., Zheng, H., Cai, J., & Wang, S. (2022) · Medical Physics · doi:10.1002/mp.15936 · Cited by 52
Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective
Baeßler, B., GOETZ, M. A., Antoniades, C. A., Heidenreich, J. F., Leiner, T., & Beer, M. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1120361 · Cited by 52
Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features
Rahmim, A., Bak-Fredslund, K. P., Ashrafinia, S., Lu, L., Schmidtlein, C. R., Subramaniam, R. M., Morsing, A., Keiding, S., Horsager, J., & Munk, O. L. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.02.006 · Cited by 50
Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics
van Timmeren, J. E., Carvalho, S., Leijenaar, R. T. H., Troost, E. G. C., van Elmpt, W., Ruysscher, D. D., Muratet, J. P., Denis, F., Schimek‐Jasch, T., Nestle, U., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217536 · Cited by 50
Self-citation
Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer
Bobholz, S. A., Lowman, A. K., Barrington, A., Brehler, M., McGarry, S. D., Cochran, E. J., Connelly, J., Mueller, W. M., Agarwal, M., O’Neill, D. P., et al. (2020) · Tomography · doi:10.18383/j.tom.2019.00029 · Cited by 50
A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database
Liu, M. Z., Mutasa, S., Chang, P., Siddique, M., Jambawalikar, S., & Ha, R. S. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.08.021 · Cited by 50
“Quantitative radiomics extracts data from routine medical imaging and analyzes complex imaging features, unperceivable to the human eye [14].”
Background
Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI
Han, Y., Wang, T., Wu, P. S., Zhang, H., Chen, H., & Yang, C. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.11.009 · Cited by 50
A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage
Song, Z., Tang, Z., Liu, H., Guo, D., Cai, J., & Zhou, Z. (2021) · European Radiology · doi:10.1007/s00330-021-07828-7 · Cited by 50
CT Radiomics in Predicting EGFR Mutation in Non-small Cell Lung Cancer: A Single Institutional Study
Wu, S., Shen, G., Mao, J., & Gao, B. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.542957 · Cited by 47
“Finally, the model is constructed by logistic regression method (12).”
Method
Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
Wong, J., Baine, M. J., Wisnoskie, S., Bennion, N. R., Zheng, D., Lei, Y., Dalal, V., Hollingsworth, M. A., Lin, C. H., & Zheng, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-95152-x · Cited by 49
Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease
Ge, X.-Y., Lan, Z.-K., Lan, Q., Lin, H., Wang, G.-D., & Chen, J. (2022) · European Radiology · doi:10.1007/s00330-022-09268-3 · Cited by 53
RadioPathomics: Multimodal learning in non-small cell lung cancer for adaptive radiotherapy
Tortora, M., Cordelli, E., Sicilia, R., Nibid, L., Ippolito, E., Perrone, G., Ramella, S., & Soda, P. (2023) · IEEE Access · doi:10.1109/access.2023.3275126 · Cited by 49
“• Radiomics: it is based on the extraction of quantitative features from radiology images routinely collected in order to predict clinical outcomes or guide clinical decisions using AI algorithms [3, 4].”
Artificial intelligence and digital pathology: clinical promise and deployment considerations
Zarella, M. D., McClintock, D. S., Batra, H. V., Gullapalli, R. R., Valante, M., Tan, V. O., Dayal, S., Oh, K. S., Lara, H., Garcia, C., et al. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.5.051802 · Cited by 49
Semiquantitative Computed Tomography Characteristics for Lung Adenocarcinoma and Their Association With Lung Cancer Survival
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“Computational technical development has permitted a high-throughput process in which a large number of shape, edge, and texture imaging features are extracted.(8,9) However, computerized algorithms are more highly dependent on harmonized acquisition and reconstruction parameters than are human readers.”
BackgroundSelf-citation
Cancer heterogeneity and imaging
O’Connor, J. P. B. (2016) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2016.10.001 · Cited by 50
“Studies have varied from employing tens of parameters, to around 200 features (88), over 400 features (87) or over 600 features (89).”
Background
A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks
Hossain, S., Najeeb, S., Shahriyar, A., Abdullah, Z. R., & Haque, M. A. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683802 · Cited by 49
“There has been a significant amount of research related to radiomics and biomedical image processing utilizing computer vision and data driven algorithms in recent times [3, 7, 8].”
Background
Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma
Kaissis, G. A., Ziegelmayer, S., Lohöfer, F. K., Harder, F. N., Jungmann, F., Sasse, D., Muckenhuber, A., Yen, H.-Y., Steiger, K., Siveke, J. T., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9030724 · Cited by 50
“This lack of multi-institutional prospective validation and datasets of sufficient statistical power is a notable issue with many radiomic studies [26].”
Background
Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method
Kawahara, D., Tang, X., Lee, C. K. K., Nagata, Y., & Watanabe, Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.569461 · Cited by 49
“Radiomics analyzes the medical image quantitatively to explore features unique to a patient (11).”
Background
Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer
Shu, Z., Mao, D., Song, Q., Xu, Y., Pang, P., & Zhang, Y. (2021) · European Radiology · doi:10.1007/s00330-021-08242-9 · Cited by 49
“Additionally, predictive and prognostic models are important in radiomics [14].”
Background
Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy
Polk, S. L., Choi, J. W., McGettigan, M. J., Rose, T., Ahmed, A., Kim, J., Jiang, K., Balagurunathan, Y., Qi, J., Farah, P. T., et al. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i24.3458 · Cited by 48
Self-citation
Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review
Michaely, H. J., Aringhieri, G., Cioni, D., & Neri, E. (2022) · Diagnostics · doi:10.3390/diagnostics12040799 · Cited by 48
Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis
Hou, Z., Yang, Y., Li, S., Yan, J., Ren, W., Liu, J., Wang, K., Liu, B., & Wan, S. (2018) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2018.05.01 · Cited by 48
“Radiomics is a non-invasive approach that extracts quantitative features from medical images and allows for comprehensive visualization and characterization of the tumor region and corresponding microenvironment, which has been found to show significant predictive power for gene expression, pathological classification…”
Background
Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis
Xiao, D., Yan, P.-F., Wang, Y., Osman, M. S., & Zhao, H. (2018) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2018.08.004 · Cited by 48
Radiomics With Attribute Bagging for Breast Tumor Classification Using Multimodal Ultrasound Images
Li, Y., Liu, Y., Zhang, M., Zhang, G., Wang, Z., & Luo, J. (2019) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15115 · Cited by 48
“Radiomics refers to extraction and analysis of high-throughput quantitative features from medical images to reveal the underlying difference between tissues.(15,16) Radiomics of US images has been used for breast tumor diagnosis.”
Background
Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans
Wang, X., Zhang, L., Yang, X., Tang, L., Zhao, J., Chen, G., Li, X., Yan, S., Li, S., Yang, Y., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109150 · Cited by 48
Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
Zhang, Y., Lobo-Mueller, E. M., Karanicolas, P., Gallinger, S., Haider, M. A., & Khalvati, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-80998-y · Cited by 62
Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study
Schlett, C. L., Hendel, T., Weckbach, S., Reiser, M. F., Kauczor, H., Nikolaou, K., Günther, M., Forsting, M., Hosten, N., Völzke, H., et al. (2016) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/s-0042-104510 · Cited by 47
Trends in radiology and experimental research
Sardanelli, F. (2017) · European Radiology Experimental · doi:10.1186/s41747-017-0006-5 · Cited by 47
“Thus, radiology is well positioned for mapping cancer heterogeneity in the individual patient and guiding the adaptive therapy, especially if molecular imaging, radiomics, radiogenomics and habitat imaging techniques are used [6, 12, 13].”
Background
Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival
Yuan, M., Zhang, Y., Pu, X.-H., Zhong, Y., Li, H., Wu, J., & Yu, T. (2017) · European Radiology · doi:10.1007/s00330-017-4855-3 · Cited by 47
CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma
Chen, A., Lü, L., Pu, X.-H., Yu, T., Yang, H., Schwartz, L. H., & Zhao, B. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20591 · Cited by 47
Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis
Huang, B., Tian, J., Zhang, H., Luo, Z., Qin, J., Huang, C., He, X., Luo, Y., Zhou, Y., Guo, D., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3043236 · Cited by 48
Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma
Fan, Y., Yu, Y., Wang, X., Hu, M., & Hu, C. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00633-0 · Cited by 47
Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development
Chiloiro, G., Cusumano, D., Franco, P. D., Lenkowicz, J., Boldrini, L., Carano, D., Barbaro, B., Corvari, B., Dinapoli, N., Giraffa, M., et al. (2021) · La radiologia medica · doi:10.1007/s11547-021-01421-0 · Cited by 49
Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis
Jia, L.-L., Zhao, J.-X., pan, N., Shi, L., Zhao, L., Tian, J., & Huang, G. (2022) · European Journal of Radiology Open · doi:10.1016/j.ejro.2022.100438 · Cited by 48
Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)
Wang, Y., Lin, W., Zhuang, X., Wang, X., He, Y., Li, L., & Lyu, G. (2024) · Oncology Reports · doi:10.3892/or.2024.8705 · Cited by 46
“Semi‑automatic segmentation, based on automatic segmentation, allows clinicians to ‘proofread’ the delineated edges manually, which can improve the efficiency and accuracy of the delineation (28).”
Background
Personalized radiotherapy: concepts, biomarkers and trial design
Ree, A. H., & Redalen, K. R. (2015) · British Journal of Radiology · doi:10.1259/bjr.20150009 · Cited by 46
Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer
Kim, H., Park, C. M., Goo, J. M., Wildberger, J. E., & Kauczor, H. (2015) · Investigative Radiology · doi:10.1097/rli.0000000000000152 · Cited by 46
“The so-called radiomics approach that uses an automated high-throughput extraction of hundreds of quantitative features of medical images is on theway.(108,109) Therefore, novel quantitative imaging biomarkers are expected to be extracted in the near future and may help to infer phenotypes or gene-protein signatures,…”
Medical Imaging Informatics
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Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors
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ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy)
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“CT, MRI, PET) that can be used as a starting point to develop predictive models for H&N cancer, useful in offering assistance in clinical decision-making [27,28,29,30].”
Background
Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes
Altazi, B. A., Fernandez, D. C., Zhang, G. G., Hawkins, S. H., Naqvi, S. M. H., Kim, Y., Hunt, D., Latifi, K., Biagioli, M. C., Venkat, P. S., et al. (2018) · Physica Medica · doi:10.1016/j.ejmp.2017.10.009 · Cited by 46
“The extraction of underlying information from medical images based on quantitatively derived features (Radiomics) is presented as a developing process in the field of medical oncological imaging [10,11].”
Background
Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features
Xie, H., Hu, J., Zhang, X., Ma, S., Liu, Y., & Wang, X. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.04.004 · Cited by 46
Prediction of the Depth of Tumor Invasion in Gastric Cancer: Potential Role of CT Radiomics
Wang, Y., Liu, W., Yang, Y., Liu, J.-J., Jiang, L., Xue, H., Lei, J., Jin, Z., & Yu, J. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.020 · Cited by 46
Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives
Dercle, L., Henry, T., Carré, A., Nikos Paragios, A., Deutsch, É., & Robert, C. (2020) · Methods · doi:10.1016/j.ymeth.2020.07.003 · Cited by 47
Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases
Rizzetto, F., Calderoni, F., Mattia, C. D., Defeudis, A., Giannini, V., Mazzetti, S., Vassallo, L., Ghezzi, S., Bianchi, A. S., Marsoni, S., et al. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-00189-8 · Cited by 46

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: Kumar, Virendra
Gu, Yuhua
Berglund, Anders
Eschrich, Steven A.
Schabath, Matthew B.
Aerts, Hugo J.W.L.
Dekker, Andre
Fenstermacher, David
Goldgof, Dmitry B.
Hall, Lawrence O.
Lambin, Philippe
Balagurunathan, Yoganand
Gatenby, Robert A.
Gillies, Robert J.
Venue: Magnetic Resonance Imaging
Publisher: Elsevier BV
Published: 2012-11
Volume: 30
Issue: 9
Pages: 1234-1248
ISSN: 0730-725X (ISSN-L)
DOI: 10.1016/j.mri.2012.06.010
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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