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Radiomics is a field of medical study that aims to extract large amount of quantitative features from medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics that fail to be appreciated by the naked eye. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy. Radiomics emerged from the medical field of oncology and is the most advanced in applications within that field. However, the technique can be applied to any medical study where a disease or a condition can be tomographically imaged.
- 1 Process
- 2 Features
- 2.1 Multiparametric Radiomics
- 2.2 Prediction of clinical outcomes
- 2.3 Prediction risk of distant metastasis
- 2.4 Assessment of cancer genetics
- 2.5 Image Guided Radiotherapy
- 2.6 Distinguishing True Progression From Radionecrosis in Radiation Therapy after stereotactic radiosurgery (SRS) for brain metastases
- 2.7 Prediction of physiological events
- 3 See also
- 4 References
The underlying image data that is used to characterize tumors is provided by medical scanning technology. Instead of taking a picture like a camera, the scans produce raw volumes of data which must be further processed to be usable in medical investigations. To get actual images that are interpretable, a reconstruction tool must be used.
There are a variety of reconstruction algorithms, so consideration must be taken to determine the most suitable one for each case, as the resultant images will differ. This influences the quality and usability of the images, which in turn determines how easily an abnormal finding can be detected and how well it can be characterized.
The reconstructed images are saved in a large database. A public database to which all clinics have access enables broadly collaborative and cumulative work in which all can benefit from growing amounts of data, ideally enabling a more precise workflow.
After the images have been saved in the database, they have to be reduced to the essential parts, in this case the tumors, which are called “volumes of interest”.
Because of the large image data that needs to be processed, it would be too much work to perform the segmentation manually for every single image if a radiomics database with lots of data is created. Instead of manual segmentation, an automated process has to be used. A possible solution are automatic and semiautomatic segmentation algorithms. Before it can be applied on a big scale an algorithm must score as high as possible in the following four tasks:
- First, it must be reproducible, which means that when it is used on the same data the outcome will not change.
- Another important factor is the consistency. The algorithm does solve the problem at hand and performs the task rather than doing something that is not important. In this case, it is necessary that the algorithm can detect the diseased part in all different scans.
- The algorithm also needs to be accurate. It is very important that the algorithm detects the diseased part in the most precise way possible. Only with accurate data, accurate results can be achieved.
- A minor but still important point is the time efficiency. The results should be generated as fast as possible so that the whole process of radiomics can also be accelerated. A minor point means in this case that, if it is in a certain frame, it is not as important as the others.
Features Extraction and Qualification
After the segmentation, many features of the tumor may be computed. They stretch from volume, shape, surface to density and intensity as well as texture, tumor location, relations with the surrounding tissues and a lot of others.
Due to its massive variety, the data needs to be qualified as well, so that we can eliminate redundant information. Hundreds of different features need to be evaluated and so we need feature selection algorithms to accelerate this process.
After the selection of features that are important for our task it is crucial to analyze the chosen data. Before the actual analysis, the clinical and molecular (sometimes even the genetic) data needs to be integrated because it has a big impact on what can be deducted from the analysis. There are different methods to finally analyze the data. First, the different features are compared to one another to find out whether they have any information in common and to reveal what it means when they all occur at the same time.
Another way is Supervised or Unsupervised Analysis. Supervised Analysis uses an outcome variable to be able to create prediction models. Unsupervised Analysis summarizes the information we have and can be represented graphically. So that the conclusion of our results is clearly visible.
Several steps are necessary to create an integrated radiomics database. The imaging data needs to be exported from the clinics. This is already a very challenging step because the patient information is very sensitive and governed by Privacy laws, such as HIPAA. At the same time the exported data must not lose any of its integrity when compressed so that the database only incorporates data of the same quality. The integration of clinical and molecular data is important as well and a large image storage location is needed.
The goal of radiomics is to be able to use this database for new patients. This means that we need algorithms that run new input data through the database which return a result with information about what the course of the patients’ disease might look like. For example, how fast the tumor will grow or how good the chances are that the patient survives for a certain time, whether distant metastases are possible and where. This determines how the further treatment (like surgery, chemotherapy, radiotherapy or targeted drugs etc.) and the best solution which maximizes survival or improvement is selected. The algorithm has to recognize correlations between the images and the features, so that it is possible to extrapolate from the data base material to the input data.
Radiomic features can be divided into five groups: size and shape based–features, descriptors of the image intensity histogram, descriptors of the relationships between image voxels (e.g. gray-level co-occurrence matrix (GLCM), run length matrix (RLM), size zone matrix (SZM), and neighborhood gray tone difference matrix (NGTDM) derived textures, textures extracted from filtered images, and fractal features. The mathematical definitions of these features are independent of imaging modality and can be found in the literature. A detailed description of texture features for radiomics can be found in Parekh, et al.,(2016)  and Depeursinge et al. (2017).
Multiparametric radiological imaging is vital for detection, characterization and diagnosis of many different diseases. However, current methods in radiomics are limited to using single images for the extraction of these textural features and may limit the applicable scope of radiomics in different clinical settings. Thus, in the current form, they are not capable of capturing the true underlying tissue characteristics in high dimensional multiparametric imaging space.
Recently, a Multiparametric imaging radiomic framework termed MPRAD for extraction of radiomic features from high dimensional datasets was developed. The Multiparametric Radiomics was tested on two different organs and diseases; breast cancer and cerebrovascular accidents in brain, commonly referred to as stroke.
Breast Cancer - Multiparametric radiomics
In breast cancer, The MPRAD framework classified malignant from benign breast lesions with excellent sensitivity and specificity of 87% and 80.5% respectively with an AUC of 0.88. MPRAD provided a 9%-28% increase in AUC over single radiomic parameters. More importantly, in breast, normal glandular tissue MPRAD were similar between each group with no significance differences.
Brain - Stroke - Multiparametric radiomics
Similarly, the MPRAD features in brain stroke demonstrated increased performance in distinguishing the perfusion-diffusion mismatch compared to single parameter radiomics and there were no differences within the white and gray matter tissue. The majority of the single radiomic second order features (GLCM) did not show any significant textural difference between infarcted tissue and tissue at risk on the ADC map. Whereas the same second order multiparametric radiomic features (TSPM) were significantly different for the DWI dataset. Similarly, multiparametric radiomic values for the TTP and PWI dataset demonstrated excellent results for the MPRAD. The MPRAD TSPM Entropy exhibited significant difference between infarcted tissue and potential tissue-at-risk: (6.6±0.5 vs 8.4±0.3, p=0.01).
Prediction of clinical outcomes
Aerts et al. (2014) performed the first large-scale radiomic study that included three lung and two head-and-neck cancer cohorts, consisting of over 1000 patients. They assessed the prognostic values of over 400 textural and shape- and intensity-based features extracted from the computed tomography (CT) images acquired before any treatment. Tumor volumes were defined either by expert radiation oncologists or using semiautomatic segmentation methods. Their results identified a subset of radiomic features that may be useful for predicting patient survival and describing intratumoural heterogeneity. They also confirmed that the prognostic ability of these radiomics features may be transferred from lung to head-and-neck cancer. However, Parmar et al. (2015) demonstrated that prognostic value of some radiomic features may be cancer type dependent. Particularly, they observed that not every radiomic feature that significantly predicted the survival of lung cancer patients could also predict the survival of head-and-neck cancer patients and vice versa.
Several studies have also shown radiomic features are better at predicting treatment response than conventional measures, such as tumor volume and diameter, and the maximum radiotracer uptake on positron emission tomography (PET) imaging. Using this technique an algorythm has been developed, after initial training based on intra tumor lymphocyte density, to predict the probability of tumor response to immunotherapy, providing a demonstration of the clinical potential of radiomics as a powerful to for personnalized therapy in the emerging field of immunooncology.
Prediction risk of distant metastasis
Metastatic potential of tumors may also be predicted by radiomic features. For example, thirty-five CT-based radiomic features were identified to be predictive of distant metastasis of lung cancer in a study by Coroller et al. in 2015. They thus concluded that radiomic features can be useful to identify patients with high risk of developing distant metastasis, guiding physicians to select the effective treatment for individual patients.
Assessment of cancer genetics
Lung tumor biological mechanisms were found[by whom?] to demonstrate distinct and complex imaging patterns. In particular, Aerts et al. (2014) showed that radiomic features were associated with biological gene sets, such as cell cycle phase, DNA recombination, regulation of immune system process, etc. Moreover, various mutations of glioblastoma (GBM), such as 1p/19q deletion, MGMT methylation, TP53, EGFR, and NF1, have been shown to be significantly predicted by magnetic resonance imaging (MRI) volumetric measures, including tumor volume, necrosis volume, and contrast enhancing volume.
Image Guided Radiotherapy
Radiomics offers the advantage to be non invasive and can therefore be repeated propsectively for a given patient more easily than invasive tumor biopsies. It has been suggested that radiomics could be a mean to monitor tumor dynamic changes along the course of radiotherapy and to define sub volumes at risk for which dose escalation could be beneficial.
Distinguishing True Progression From Radionecrosis in Radiation Therapy after stereotactic radiosurgery (SRS) for brain metastases
Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics demonstrated significant differences in a set of 82 treated lesions in 66 patients with pathological outcomes. Top-ranked Radiomic features feed into an optimized IsoSVM classifier resulted ina sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%. These results show that radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS.
Prediction of physiological events
Radiomics can also be used to identify challenging physiological events such as brain activity, which is usually studied with imaging techniques such as functional MRI "fMRI". FMRI raw images can undergo radiomic analysis to generate imaging features that can be later correlated with meaningful brain activity.
- Lambin, Philippe; Rios-Velazquez, Emmanuel; Leijenaar, Ralph; Carvalho, Sara; Van Stiphout, Ruud G.P.M; Granton, Patrick; Zegers, Catharina M.L; Gillies, Robert; Boellard, Ronald; Dekker, André; Aerts, Hugo J.W.L (2012). "Radiomics: Extracting more information from medical images using advanced feature analysis". European Journal of Cancer. 48 (4): 441–446. doi:10.1016/j.ejca.2011.11.036. PMC 4533986. PMID 22257792.
- 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). "Radiomics: The process and the challenges". Magnetic Resonance Imaging. 30 (9): 1234–1248. doi:10.1016/j.mri.2012.06.010. PMC 3563280. PMID 22898692.
- Gillies, Robert J; Kinahan, Paul E; Hricak, Hedvig (2016). "Radiomics: Images Are More than Pictures, They Are Data". Radiology. 278 (2): 563–577. doi:10.1148/radiol.2015151169. PMC 4734157. PMID 26579733.
- Parekh, Vishwa; Jacobs, Michael A (2016). "Radiomics: A new application from established techniques". Expert Review of Precision Medicine and Drug Development. 1 (2): 207–226. doi:10.1080/23808993.2016.1164013. PMC 5193485. PMID 28042608.
- Yip, Stephen S F; Aerts, Hugo J W L (7 July 2016). "Applications and limitations of radiomics". Physics in Medicine and Biology. 61 (13): R150–R166. Bibcode:2016PMB....61R.150Y. doi:10.1088/0031-9155/61/13/R150. PMC 4927328. PMID 27269645.
- Yip, Stephen S. F.; Liu, Ying; Parmar, Chintan; Li, Qian; Liu, Shichang; Qu, Fangyuan; Ye, Zhaoxiang; Gillies, Robert J.; Aerts, Hugo J. W. L. (14 June 2017). "Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer". Scientific Reports. 7 (1): 3519. Bibcode:2017NatSR...7.3519Y. doi:10.1038/s41598-017-02425-5. PMC 5471260. PMID 28615677.
- Chicklore, Sugama; Goh, Vicky; Siddique, Musib; Roy, Arunabha; Marsden, Paul K; Cook, Gary J. R (2012). "Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis". European Journal of Nuclear Medicine and Molecular Imaging. 40 (1): 133–140. doi:10.1007/s00259-012-2247-0. PMID 23064544.
- Cook, Gary J. R; Siddique, Musib; Taylor, Benjamin P; Yip, Connie; Chicklore, Sugama; Goh, Vicky (2014). "Radiomics in PET: Principles and applications". Clinical and Translational Imaging. 2 (3): 269–276. doi:10.1007/s40336-014-0064-0.
- Parekh, Vishwa S.; Jacobs, Michael A. (2017-11-14). "Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI". Npj Breast Cancer. 3 (1): 43. doi:10.1038/s41523-017-0045-3. ISSN 2374-4677. PMC 5686135. PMID 29152563.
- Galloway, Mary M (1975). "Texture analysis using gray level run lengths". Computer Graphics and Image Processing. 4 (2): 172–179. doi:10.1016/S0146-664X(75)80008-6.
- Pentland, Alex P (1984). "Fractal-Based Description of Natural Scenes". IEEE Transactions on Pattern Analysis and Machine Intelligence (6): 661–674. doi:10.1109/TPAMI.1984.4767591.
- Amadasun, M; King, R (1989). "Textural features corresponding to textural properties". IEEE Transactions on Systems, Man, and Cybernetics. 19 (5): 1264–1274. doi:10.1109/21.44046.
- Thibault, Guillaume; Angulo, Jesus; Meyer, Fernand (2014). "Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification". IEEE Transactions on Biomedical Engineering. 61 (3): 630–637. doi:10.1109/TBME.2013.2284600. PMID 24108747.
- Ranjbar, Sara; Ross Mitchell, J (2017). "An Introduction to Radiomics: An Evolving Cornerstone of Precision Medicine". Biomedical Texture Analysis. pp. 223–245. doi:10.1016/B978-0-12-812133-7.00008-9. ISBN 9780128121337.
- Parekh, Vishwa S.; Jacobs, Michael A. (2018-09-25). "MPRAD: A Multiparametric Radiomics Framework". arXiv:1809.09973 [cs.CV].
- Gu, Yuhua; Kumar, Virendra; Hall, Lawrence O; Goldgof, Dmitry B; Li, Ching-Yen; Korn, René; Bendtsen, Claus; Velazquez, Emmanuel Rios; Dekker, Andre; Aerts, Hugo; Lambin, Philippe; Li, Xiuli; Tian, Jie; Gatenby, Robert A; Gillies, Robert J (2013). "Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach". Pattern Recognition. 46 (3): 692–702. doi:10.1016/j.patcog.2012.10.005. PMC 3580869. PMID 23459617.
- Velazquez, Emmanuel Rios; Parmar, Chintan; Jermoumi, Mohammed; Mak, Raymond H; Van Baardwijk, Angela; Fennessy, Fiona M; Lewis, John H; De Ruysscher, Dirk; Kikinis, Ron; Lambin, Philippe; Aerts, Hugo J. W. L (2013). "Volumetric CT-based segmentation of NSCLC using 3D-Slicer". Scientific Reports. 3: 3529. Bibcode:2013NatSR...3E3529V. doi:10.1038/srep03529. PMC 3866632. PMID 24346241.
- Parmar, Chintan; Leijenaar, Ralph T. H; Grossmann, Patrick; Rios Velazquez, Emmanuel; Bussink, Johan; Rietveld, Derek; Rietbergen, Michelle M; Haibe-Kains, Benjamin; Lambin, Philippe; Aerts, Hugo J.W.L (2015). "Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer". Scientific Reports. 5: 11044. doi:10.1038/srep11044. PMC 4937496. PMID 26251068.
- Tixier, F; Le Rest, C. C; Hatt, M; Albarghach, N; Pradier, O; Metges, J.-P; Corcos, L; Visvikis, D (2011). "Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer". Journal of Nuclear Medicine. 52 (3): 369–378. doi:10.2967/jnumed.110.082404. PMC 3789272. PMID 21321270.
- Hatt, M; Majdoub, M; Vallieres, M; Tixier, F; Le Rest, C. C; Groheux, D; Hindie, E; Martineau, A; Pradier, O; Hustinx, R; Perdrisot, R; Guillevin, R; El Naqa, I; Visvikis, D (2014). "18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi-Cancer Site Patient Cohort". Journal of Nuclear Medicine. 56 (1): 38–44. doi:10.2967/jnumed.114.144055. PMID 25500829.
- Van Rossum, P. S. N; Fried, D. V; Zhang, L; Hofstetter, W. L; Van Vulpen, M; Meijer, G. J; Court, L. E; Lin, S. H (2016). "The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer". Journal of Nuclear Medicine. 57 (5): 691–700. doi:10.2967/jnumed.115.163766. PMID 26795288.
- Yip, Stephen S. F; Coroller, Thibaud P; Sanford, Nina N; Mamon, Harvey; Aerts, Hugo J. W. L; Berbeco, Ross I (2016). "Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients". Frontiers in Oncology. 6: 72. doi:10.3389/fonc.2016.00072. PMC 4810033. PMID 27066454.
- Zhang, Hao; Tan, Shan; Chen, Wengen; Kligerman, Seth; Kim, Grace; d'Souza, Warren D; Suntharalingam, Mohan; Lu, Wei (2014). "Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal 18F-FDG PET Features, Clinical Parameters, and Demographics". International Journal of Radiation Oncology*biology*physics. 88 (1): 195–203. doi:10.1016/j.ijrobp.2013.09.037. PMC 3875172. PMID 24189128.
- Cheng, Nai-Ming; Fang, Yu-Hua Dean; Lee, Li-yu; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Liao, Chun-Ta; Yang, Lan-Yan; Hsu, Ching-Han; Yen, Tzu-Chen (2014). "Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer". European Journal of Nuclear Medicine and Molecular Imaging. 42 (3): 419–428. doi:10.1007/s00259-014-2933-1. PMID 25339524.
- Cook, G. J. R; Yip, C; Siddique, M; Goh, V; Chicklore, S; Roy, A; Marsden, P; Ahmad, S; Landau, D (2012). "Are Pretreatment 18F-FDG PET Tumor Textural Features in Non-Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?". Journal of Nuclear Medicine. 54 (1): 19–26. doi:10.2967/jnumed.112.107375. PMID 23204495.
- Sun, Roger; Limkin, Elaine Johanna; Vakalopoulou, Maria; Dercle, Laurent; Champiat, Stéphane; Han, Shan Rong; Verlingue, Loïc; Brandao, David; Lancia, Andrea; Ammari, Samy; Hollebecque, Antoine; Scoazec, Jean-Yves; Marabelle, Aurélien; Massard, Christophe; Soria, Jean-Charles; Robert, Charlotte; Paragios, Nikos; Deutsch, Eric; Ferté, Charles (2018). "A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: An imaging biomarker, retrospective multicohort study". The Lancet Oncology. 19 (9): 1180–1191. doi:10.1016/S1470-2045(18)30413-3. PMID 30120041.
- Coroller, Thibaud P; Grossmann, Patrick; Hou, Ying; Rios Velazquez, Emmanuel; Leijenaar, Ralph T.H; Hermann, Gretchen; Lambin, Philippe; Haibe-Kains, Benjamin; Mak, Raymond H; Aerts, Hugo J.W.L (2015). "CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma". Radiotherapy and Oncology. 114 (3): 345–350. doi:10.1016/j.radonc.2015.02.015. PMC 4400248. PMID 25746350.
- Vallières, M; Freeman, C R; Skamene, S R; El Naqa, I (2015). "A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities". Physics in Medicine and Biology. 60 (14): 5471–5496. Bibcode:2015PMB....60.5471V. doi:10.1088/0031-9155/60/14/5471. PMID 26119045.
- Rios Velazquez, Emmanuel; Parmar, Chintan; Liu, Ying; Coroller, Thibaud P.; Cruz, Gisele; Stringfield, Olya; Ye, Zhaoxiang; Makrigiorgos, Mike; Fennessy, Fiona; Mak, Raymond H.; Gillies, Robert; Quackenbush, John; Aerts, Hugo J.W.L. (15 July 2017). "Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer". Cancer Research. 77 (14): 3922–3930. doi:10.1158/0008-5472.CAN-17-0122. PMC 5528160. PMID 28566328.
- Yip, Stephen S.F.; Kim, John; Coroller, Thibaud P.; Parmar, Chintan; Velazquez, Emmanuel Rios; Huynh, Elizabeth; Mak, Raymond H.; Aerts, Hugo J.W.L. (April 2017). "Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer". Journal of Nuclear Medicine. 58 (4): 569–576. doi:10.2967/jnumed.116.181826.
- Brown, R; Zlatescu, M; Sijben, A; Roldan, G; Easaw, J; Forsyth, P; Parney, I; Sevick, R; Yan, E; Demetrick, D; Schiff, D; Cairncross, G; Mitchell, R (2008). "The Use of Magnetic Resonance Imaging to Noninvasively Detect Genetic Signatures in Oligodendroglioma". Clinical Cancer Research. 14 (8): 2357–2362. doi:10.1158/1078-0432.CCR-07-1964. PMID 18413825.
- Drabycz, Sylvia; Roldán, Gloria; De Robles, Paula; Adler, Daniel; McIntyre, John B; Magliocco, Anthony M; Cairncross, J. Gregory; Mitchell, J. Ross (2010). "An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging". NeuroImage. 49 (2): 1398–1405. doi:10.1016/j.neuroimage.2009.09.049. PMID 19796694.
- Gutman, David A; Dunn, William D; Grossmann, Patrick; Cooper, Lee A. D; Holder, Chad A; Ligon, Keith L; Alexander, Brian M; Aerts, Hugo J. W. L (2015). "Somatic mutations associated with MRI-derived volumetric features in glioblastoma". Neuroradiology. 57 (12): 1227–1237. doi:10.1007/s00234-015-1576-7. PMC 4648958. PMID 26337765.
- Sun, Roger; Orlhac, Fanny; Robert, Charlotte; Reuzé, Sylvain; Schernberg, Antoine; Buvat, Irène; Deutsch, Eric; Ferté, Charles (2016). "In Regard to Mattonen et al". International Journal of Radiation Oncology*biology*physics. 95 (5): 1544–1545. doi:10.1016/j.ijrobp.2016.03.038. PMID 27479727.
- Yip, Stephen S F; Coroller, Thibaud P; Sanford, Nina N; Huynh, Elizabeth; Mamon, Harvey; Aerts, Hugo J W L; Berbeco, Ross I (21 January 2016). "Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction". Physics in Medicine and Biology. 61 (2): 906–922. Bibcode:2016PMB....61..906Y. doi:10.1088/0031-9155/61/2/906. PMID 26738433.
- Kleinberg, Lawrence; Jacobs, Michael A.; Lee, Junghoon; Lim, Michael; Redmond, Kristin; McTyre, Emory; Soike, Michael; Shen, Colette; Chen, Linda (2018-11-15). "Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics". International Journal of Radiation Oncology â€¢ Biology â€¢ Physics. 102 (4): 1236–1243. doi:10.1016/j.ijrobp.2018.05.041. ISSN 0360-3016. PMID 30353872.
- Hassan, I; Kotrotsou, A; Bakhtiari, A. S; Thomas, G. A; Weinberg, J. S; Kumar, A. J; Sawaya, R; Luedi, M. M; Zinn, P. O; Colen, R. R (2016). "Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity". Scientific Reports. 6: 25295. Bibcode:2016NatSR...625295H. doi:10.1038/srep25295. PMC 4858648. PMID 27151623.