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
Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
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“However, the performance of radiomics relies on accurate labeling of the lesion region (Kumar et al. 2012).”
Background
Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine
Napel, S. A., & Giger, M. L. (2015) · Journal of medical imaging · doi:10.1117/1.jmi.2.4.041001 · Cited by 20
“Radio-mics 19,20 is a name given to the science of converting medical images into computer-accessible and -searchable data.”
Background
Radiomics: a Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer
Wei, W., Yu, R., Liu, Z., Zhou, B., Tang, Z., Wang, S., Dong, D., Zang, Y., Guo, Y., & Tian, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513351 · Cited by 20
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Experimental phantom evaluation to identify robust positron emission tomography (PET) radiomic features
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“Radiomics studies have four main steps to be considered [16]: image acquisition and recon-”
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Association of bone mineral density with bone texture attributes extracted using routine magnetic resonance imaging
Maciel, J. G., de Araújo, I. M., Trazzi, L. C., de Azevedo‐Marques, P. M., Salmón, C. E. G., de Paula, F. J. A., & Nogueira‐Barbosa, M. H. (2020) · Clinics · doi:10.6061/clinics/2020/e1766 · Cited by 19
“Radiomics is a research area that has shown promising growth and usefulness in diagnostic and prognostic evaluations and therapeutic decision making (32-34).”
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SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography
Yousefi, B., Akbari, H., Hershman, M., Kawakita, S., Fernandes, H., Ibarra‐Castanedo, C., Ahadian, S., & Maldague, X. (2021) · Applied Sciences · doi:10.3390/app11073248 · Cited by 19
“The results of the system were compared to the ground truth to measure the accuracy of the system.”
Result
Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study
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“From the viewpoint of radiomics, it can be predicted that the phenotype of medical images includes genetic information of tumors and prognostic information of cases 53, 54 .”
Background
Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions
Khaleel, S. S., Katims, A., Cumarasamy, S., Rosenzweig, S. J., Attalla, K., Hakimi, A. A., & Mehrazin, R. (2022) · Cancers · doi:10.3390/cancers14092085 · Cited by 22
“As mentioned earlier, radiomics refers to the extraction and analysis of quantitative imaging features from cross-sectional imaging modalities, while radiogenomics refers to the study of the translational phenotype underlying these imaging features [51].”
Background
Multi-task multi-scale learning for outcome prediction in 3D PET images
Amyar, A., Modzelewski, R., Véra, P., Morard, V., & Ruan, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106208 · Cited by 19
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“Manual delineation is usually performed by oncologists or experienced radiologists [1], while computer-aided delineation is implemented by automatic or semi-automatic segmentation methods [3].”
Background
Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis
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“Moreover, radiomic analysis based on image features has objectivity and is valuable in predicting clinical outcomes [33, 34].”
Background
Potential biomarkers for predicting immune response and outcomes in lung cancer patients undergoing thermal ablation
Sang, J., & Ye, X. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1268331 · Cited by 22
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“…morphological, intensity, and texture features from regions of interest (ROI) in non-invasive medical images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans, providing insights into the biological characteristics of tissues [9–12].”
Background
Staging of Lung Cancer
de Groot, P. M., Carter, B. W., Cuellar, S. L. B., & Erasmus, J. J. (2015) · Clinics in Chest Medicine · doi:10.1016/j.ccm.2015.02.004 · Cited by 19
Biomedical informatics and panomics for evidence‐based radiation therapy
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“56 , 57 The potential of this new field to monitor and predict response to radiotherapy has been demonstrated in esophagus 58 , head and neck, 59 , 60 cervix, 59 , 61 and lung 62 , 63 cancers, in turn allowing for adapting and individualizing treatment.”
Background
Imaging and the completion of the omics paradigm in breast cancer
Leithner, D., Horvat, J. V., Ochoa‐Albiztegui, R. E., Thakur, S. B., Wengert, G., Morris, E. A., Helbich, T. H., & Pinker, K. (2018) · Der Radiologe · doi:10.1007/s00117-018-0409-1 · Cited by 19
“Radiogenomics is not synonymous with radiomics, which is defined as the conversion of medical images to higherdimensional, mineable data using computer classification algorithms and correlating these features with various data of interest such as patient characteristics, outcomes, and omics data for improved decision…”
Background
Review of the Role of Radiomics in Tumour Risk Classification and Prognosis of Cancer
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“Scanner/site-induced radiomics variability may perturb the multivariate model and, although merging data from multiple sites may facilitate more generalizability to the multivariate model, leave-one-site-out type of analysis has not yet been performed to illustrate robustness and/or uncertainty about the adaptability…”
Volumetric tumor delineation and assessment of its early response to radiotherapy with optical coherence tomography
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Background
Predicting Malignant Nodules from Screening CTs
Carter, B. W., Godoy, M. C. B., & Erasmus, J. J. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2016.09.117 · Cited by 19
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“It can generalize image data and extract high-dimensional computational features to provide information about the tumour phenotype and surroundings (Aerts et al., 2014; Kumar et al., 2012).”
Background
Effects of slice thickness on CT radiomics features and models for staging liver fibrosis caused by chronic liver disease
Hu, P., Chen, L., Zhong, Y., Lin, Y., Yu, X., Hu, X., Tao, X., Lin, S., Niu, T., Chen, R., et al. (2022) · Japanese Journal of Radiology · doi:10.1007/s11604-022-01284-z · Cited by 18
“CT radiomics, including texture analysis, could provide more quantitative information from medical images [7, 8], and it is currently one of the trending topics in the field.”
Background
Radiotherapy-drug combinations in the treatment of glioblastoma: a brief review
McAleavey, P. G., Walls, G., & Chalmers, A. J. (2022) · CNS Oncology · doi:10.2217/cns-2021-0015 · Cited by 18
“Two forms of radiomics are in use: ‘hand-crafted’ and ‘deep learning’; the latter allows for increased automaticity and a higher yield of outcome discriminators [96].”
Background
Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities
Merkaj, S., Bahar, R. C., Zeevi, T., Lin, M., Ikuta, I., Bousabarah, K., Petersen, G. C., Staib, L. H., Payabvash, S., Mongan, J., et al. (2022) · Cancers · doi:10.3390/cancers14112623 · Cited by 18
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Background
A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
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Influence of local aortic calcification on periaortic adipose tissue radiomics texture features—a primary analysis on PCCT
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“Limitations concerning the radiomics methodology, especially reproducibility [24], need to be considered but have been addressed by a singlescanner approach using a high-resolution detector.”
Method
CT-Based Radiomics Nomogram Improves Risk Stratification and Prediction of Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy
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Biomaterials of human source for 3D printing strategies
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“This is accomplishable by using fast, and (semi-)automatic software, as the result of decades of computer-assisted diagnostics, prognostics, and therapeutics research [40, 41].”
Method
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“To address these issues, radiomics has been proposed as an image analysis approach that allows a highthroughput extraction of objective quantitative features (morphological, statistical, and textural) that are missed by human eye [12, 13].”
Background
Development and validation of intraoral periapical radiography-based machine learning model for periodontal defect diagnosis
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“23,29,30 It aims to predict patient-specific outcomes based on high-throughput analysis and mining of advanced imaging biomarkers by machine learning algorithms.”
Background
Advances in the Radiological Evaluation of and Theranostics for Glioblastoma
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“CT has a distinct advantage over MRI in radiomics, as Hounsfield Units (HU) correlate well with tissue attenuation [8].”
Background
Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care
Kumar, R. R., Sporn, K., Khanna, A., Paladugu, P., Gowda, C., Ngo, A., Jagadeesan, R., Zaman, N., & Tavakkoli, A. (2025) · Diagnostics · doi:10.3390/diagnostics15111377 · Cited by 21
“Data fusion strategies, such as early fusion (raw image integration), feature-level fusion (combining extracted features), and decision-level fusion (aggregating model outputs), are being actively explored to optimize model performance and clinical applicability (Figure 4) [123,124].”
Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
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“These publications usually report impressive results with high level of statistical significance [4–6], but most if not all of these publications end with the same disclaimers related to reproducibility [5, 7, 8].”
Background
Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy
Jones, E. F., Hathi, D., Freimanis, R. I., Mukhtar, R. A., Chien, A. J., Esserman, L. J., Veer, L. J. V., Joe, B. N., & Hylton, N. M. (2020) · Cancers · doi:10.3390/cancers12061511 · Cited by 19
“Recent advances in the radiomics-based analysis have demonstrated the power of transforming imaging data into multi-dimensional mineable radiomic features [181,182] that are relatable to gene expression patterns [183–185].”
Standardization of histogram- and gray-level co-occurrence matrices-based radiomics in the presence of blur and noise
Gang, G. J., Deshpande, R., & Stayman, J. W. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abeea5 · Cited by 18
“Amajor contributor to such concern is the variability in radiomics values as a © 2021 Institute of Physics and Engineering inMedicine result of different image acquisition and reconstruction protocols (Kumar et al 2012, Rizzo et al 2018).”
Background
Machine Learning-Based Radiomics in Neuro-Oncology
Ehret, F., Kaul, D., Clusmann, H., Delev, D., & Kernbach, J. M. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_18 · Cited by 20
Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net
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“It is also utilized in radiomics [44,45], a new technology that extracts a vast number of quantitative information from medical images and uses them to develop models that predict things like tumor phenotype [46], survival [47,48], and categorization [49].”
Estimating Local Cellular Density in Glioma Using MR Imaging Data
Gates, E. D. H., Weinberg, J. S., Prabhu, S. S., Lin, J. S., Hamilton, J. D., Hazle, J. D., Fuller, G. N., Baladandayuthapani, V., Fuentes, D., & Schellingerhout, D. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6884 · Cited by 18
Multimodal Ultrasound Radiomic Technology for Diagnosing Benign and Malignant Thyroid Nodules of Ti-Rads 4-5: A Multicenter Study
Wang, L., Wang, C., Deng, X., Li, Y., Zhou, W., Huang, Y., Chu, X., Wang, T. F., Li, H., & Chen, Y. (2024) · Sensors · doi:10.3390/s24196203 · Cited by 17
Detection and Analysis of Lung Cancer Using Radiomic Approach
Shallu, Nanglia, P., Kumar, S., & Luhach, A. K. (2019) · Springer eBooks · doi:10.1007/978-981-13-6295-8_2 · Cited by 17
Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times
Dionisio, F. C. F., Oliveira, L. S., Hernandes, M. D. A., Engel, E. E., de Azevedo‐Marques, P. M., & Nogueira‐Barbosa, M. H. (2021) · Radiologia Brasileira · doi:10.1590/0100-3984.2020.0028 · Cited by 17
“Although automatic segmentation appears to be more effective, especially considering its time-saving potential, it requires complex computational resources and, in general, it is not easy to obtain satisfactory results((7)).”
BackgroundInfluential
A radiomics model that predicts lymph node status in pancreatic cancer to guide clinical decision making: A retrospective study
Liang, X., Cai, W., Liu, X., Jin, M. L., Ruan, L.-X., & Yan, S. (2021) · Journal of Cancer · doi:10.7150/jca.61101 · Cited by 17
“Moreover, radiomics can extract large amounts of high-dimensional quantitative features from medical images, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) [16, 21].”
Background
Biomarkers for respiratory diseases: Present applications and future discoveries
Liu, X., Cui, B., Wang, Q., Ma, Y., Li, L., & Chen, Z. H. (2021) · Clinical and Translational Discovery · doi:10.1002/ctd2.11 · Cited by 17
Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation
Cellini, F., Manfrida, S., Casà, C., Romano, A., ARCELLI, A., Zamagni, A., Luca, V. D., Colloca, G. F., D’Aviero, A., Fuccio, L., et al. (2022) · Cancers · doi:10.3390/cancers14020431 · Cited by 17
An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions
Zhang, R., Wei, W., Li, R., Li, J., Zhou, Z., Ma, M., Zhao, R., & Zhao, X. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.733260 · Cited by 17
A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules
Lv, Y., Ye, J., Yin, Y. L., Ling, J., & Pan, X. (2022) · Clinical Radiology · doi:10.1016/j.crad.2022.06.004 · Cited by 17
Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential
Flammia, F., Innocenti, T., Galluzzo, A., Danti, G., Chiti, G., Grazzini, G., Bettarini, S., Tortoli, P., Busoni, S., Dragoni, G., et al. (2023) · La radiologia medica · doi:10.1007/s11547-023-01609-6 · Cited by 17
Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets
Tulum, G. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220841 · Cited by 17
An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients
Prencipe, B., Delprete, C., Garolla, E., Corallo, F., Gravina, M., Natalicchio, M. I., Buongiorno, D., Bevilacqua, V., Altini, N., & Brunetti, A. (2023) · Bioengineering · doi:10.3390/bioengineering10070747 · Cited by 17
“RF n_estimators [10, 50, 100] min_samples_split [2, 5, 10] min_samples_leaf [1, 2, 4] criterion ‘Gini’ max_depth [5, 10, 15, 20]”
Texture and Radiomics inspired Data-Driven Cancerous Lung Nodules Severity Classification
Gupta, H., Singh, H., & Kumar, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105543 · Cited by 17
Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC
Lu, J., Ji, X., Liu, X., Jiang, Y., Li, G., Fang, P., Li, W., Zuo, A., Guo, Z., Yang, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-023-50984-7 · Cited by 22
“By utilizing radiomics data, descriptive and predictive models can be developed, providing invaluable diagnostic, prognostic, or predictive information 50 .”
Method
Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein–Protein Interaction Network and 18F-FDG PET/CT Radiomics
Ju, H., Kim, K., Kim, B. I., & Woo, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25020698 · Cited by 17
“Radiomics is a technique that involves the analysis of medical images to extract various tumor characteristics [4].”
Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma
Xing, X., Li, L., Sun, M., Yang, J., Zhu, X., Peng, F., Du, J., & Feng, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34163 · Cited by 17
“It has shown its unique value in evaluating lung adenocarcinoma [21].”
Background
Evaluation of temporomandibular joint disc displacement with MRI-based radiomics analysis
Yüksel, H. D., Orhan, K., Evlice, B., & Kaya, Ö. (2024) · Dentomaxillofacial Radiology · doi:10.1093/dmfr/twae066 · Cited by 17
An Introduction to Radiomics: An Evolving Cornerstone of Precision Medicine
Ranjbar, S., & Mitchell, J. R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00008-9 · Cited by 17
Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography
Hoye, J., Solomon, J., Sauer, T. J., Robins, M. L., & Samei, E. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.1.013504 · Cited by 17
Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma
Tang, Z., Yu, D., Ni, T., Zhao, T., Jin, Y., & Dong, E. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.19.21625 · Cited by 17
Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images
Sun, K., Liu, Z., Li, Y., Wang, L., Tang, Z., Wang, S., Zhou, X., Shao, L., Sun, C., Liu, X., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01096 · Cited by 17
“The concept of “radiomics” first described in 2012 (12, 13), calls a comprehensive analysis of medical images (14).”
Background
Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging
Tamponi, M., Crivelli, P., Montella, R. A., Sanna, F., Gabriele, D., Poggiu, A., Sanna, E., Marini, P., Meloni, G. B., Sverzellati, N., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.014 · Cited by 17
“Real-world” radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues
Doran, S., Kumar, S., Orton, M. R., d’Arcy, J. A., Kwaks, F., O'Flynn, E. A. M., Ahmed, Z., Downey, K., Dowsett, M., Turner, N. C., et al. (2021) · Cancer Imaging · doi:10.1186/s40644-021-00406-6 · Cited by 18
“, [3]) believe that image acquisition factors should be carefully controlled when used with radiomic analyses, especially in MRI, so as to prevent spurious findings, by avoiding artefactual clustering that may result from differences in scanner properties and/or acquisition techniques.”
Background
A Combination Model of Radiomics Features and Clinical Biomarkers as a Nomogram to Differentiate Nonadvanced From Advanced Liver Fibrosis: A Retrospective Study
Hu, P., Hu, X., Lin, Y., Yu, X., Tao, X., Sun, J., & Wu, X. (2021) · Academic Radiology · doi:10.1016/j.acra.2020.08.029 · Cited by 17
Quantitative computed tomography applied to interstitial lung diseases
Obert, M., Kampschulte, M., Limburg, R., Barańczuk, S., & Krombach, G. A. (2018) · European Journal of Radiology · doi:10.1016/j.ejrad.2018.01.018 · Cited by 17
Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach
Thüring, J., Rippel, O., Haarburger, C., Merhof, D., Schad, P., Bruners, P., Kuhl, C. K., & Truhn, D. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-00148-3 · Cited by 17
“Second, generalisation of machine learning algorithms to different scanner manufacturers and acquisition protocols remains a challenging problem that is currently under active investigation [43, 44].”
Background
Correlation between NF1 genotype and imaging phenotype on whole-body MRI
Liu, Y., Jordan, J. T., Bredella, M. A., Erdin, S., Walker, J. A., Vangel, M. G., Harris, G. J., Plotkin, S. R., & Cai, W. (2020) · Neurology · doi:10.1212/wnl.0000000000009490 · Cited by 17
Biomedical Imaging Informatics
Rubin, D. L., Greenspan, H. K., & Brinkley, J. F. (2013) · Springer eBooks · doi:10.1007/978-1-4471-4474-8_9 · Cited by 19
Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
Zhang, Y., Lobo-Mueller, E. M., Karanicolas, P. J., Gallinger, S., Haider, M. A., & Khalvati, F. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.550890 · Cited by 16
Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy
Jiao, Z., Li, H., Xiao, Y., Aggarwal, C., Galperin-Aizenberg, M., Pryma, D. A., Simone, C. B., Feigenberg, S. J., Kao, G. D., & Yong Fan, A. (2020) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2020.12.014 · Cited by 17
Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging
Dong, M., Hou, G., Li, S., Li, N., Zhang, L., & Xu, K. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.558428 · Cited by 16
Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI
Xian, M., Zheng, X., Xu, J., Li, X., Chen, L., & Wang, W. (2021) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2021.20031 · Cited by 16
“These features include shape, edge, and texture metrics, which can provide important insights into the tumor phenotype and the interaction of the tumor with its microenvironment (14, 15).”
Background
Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T 2 *‐weighted images of cervical spondylotic myelopathy
Zhang, M., Ouyang, H., Jiang, L., Wang, C., Liu, J., Jin, D., Ni, M., Liu, X., Lang, N., & Yuan, H. (2021) · JOR Spine · doi:10.1002/jsp2.1178 · Cited by 16
“preference.(11,12) Although tree-based models (eg, RF and Adaboost) combined with feature reduction were reported to be more preferable,(8,38) the SVM combined with the linear SVM preprocessor was observed to be the best in our study.”
Method
A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
Cangır, A., Orhan, K., Kahya, Y., Yücemen, A. U., Aktürk, İ., Özakıncı, H., Çoruh, A. G., & Sak, S. D. (2022) · Diagnostics · doi:10.3390/diagnostics12020416 · Cited by 16
“It is possible to extract multiple quantitative features from medical images, including CT and MRI, through the application of high-throughput computing [15].”
Background
Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis
Yang, C., Jiang, Z., Cheng, T., Zhou, R., Wang, G., Jing, D., Bo, L., Huang, P., Wang, J., Zhang, D., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.893103 · Cited by 16
How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis?
Zheng, Q., Zhang, Y., Li, H., Tong, X., & Ouyang, M. (2022) · European Radiology · doi:10.1007/s00330-022-09081-y · Cited by 16
“In recent studies, radiomic features played an important role in diagnosing disease [7, 23, 24].”
Background
Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosis
Xu, J., Lai, M., Li, S., Ye, K., Li, L., Hu, Q., Ai, R., Zhou, J., Li, J., Zhen, J., et al. (2022) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2022.107478 · Cited by 16
Artificial Intelligence in the Era of Precision Oncological Imaging
Cellina, M. I., Cè, M., Khenkina, N., Sinichich, P., Cervelli, M., Poggi, V., Boemi, S., Ierardi, A. M., & Carrafiello, G. (2022) · Technology in Cancer Research & Treatment · doi:10.1177/15330338221141793 · Cited by 18
“…in 2012 as “ high throughput extraction of quantitative imaging features with the intent of creating mineable databases from radiological images, ” 11 radiomics represents a new approach to medical imaging analysis and allows to further bridge the gap between raw image data and clinical and…”
Background
Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
Lyu, J., Xu, Z., Sun, H., Zhai, F., & Qu, X. (2023) · Scientific Reports · doi:10.1038/s41598-023-30678-w · Cited by 16
“Nevertheless, semi-supervised approaches using human input have allowed this avenue to arrive at the conception of the field of radiomics 11,12 .”
Background
REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution
Koçak, B., Yardımcı, A. H., Nazlı, M. A., Yüzkan, S., Mutlu, S., Güzelbey, T., Özdemir, M. Ş., Akin, M., Yücel, S., Bulut, E., et al. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110893 · Cited by 16
Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis
Feng, L., Chen, Q., Huang, L., & Long, L. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1194200 · Cited by 18
Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics
Demircioğlu, A. (2023) · Diagnostics · doi:10.3390/diagnostics13203266 · Cited by 18
A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas
Nguyen, T. M., Bertolus, C., Giraud, P., Burgun, A., Saintigny, P., Bibault, J., & Foy, J.-P. (2023) · Cancers · doi:10.3390/cancers15225369 · Cited by 15
“Computational medical imaging, known as radiomics, is an emerging ﬁeld ﬁrst deﬁned in 2012 as the high-throughput extraction of large amounts of image features from radiographic images [9,10].”
Background
Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review
Faiella, E., Vaccarino, F., Ragone, R., D’Amone, G., Cirimele, V., Piccolo, C. L., Vertulli, D., Grasso, R. F., Zobel, B. B., & Santucci, D. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12227032 · Cited by 16
“The resulting information can be used to develop descriptive and predictive models, combining image features and phenotypes with anamnestic data or gene and protein signatures (Radiogenomics) [9–11].”
Method
Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy
Su, X., Shu, L., & Huang, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-47449-2 · Cited by 17
Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges
Pierre, K., Gupta, M., Raviprasad, A. G., Razavi, S. M. S., Patel, A. B., Peters, K., Hochhegger, B., Mancuso, A., & Forghani, R. (2023) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2023.2286001 · Cited by 18
“It plays a pivotal role in radiology, where it aids in the interpretation of medical images, playing a significant part in evolving imaging omics, which include radiomics and texture analysis [2–6].”
Background
Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma
Khongwirotphan, S., Oonsiri, S., Kitpanit, S., Prayongrat, A., Kannarunimit, D., Chakkabat, C., Lertbutsayanukul, C., Sriswasdi, S., & Rakvongthai, Y. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298111 · Cited by 16
Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow
Hering, A., Westphal, M., Gerken, A., Almansour, H., Maurer, M., Geisler, B., Kohlbrandt, T., Eigentler, T., Amaral, T., Leßmann, N., et al. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03181-4 · Cited by 16

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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