Radiomics

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In the field of medicine, radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms.[1][2][3][4][5] These features, termed radiomic features, have the potential to uncover disease characteristics that fail to be appreciated by the naked eye.[6] 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.[1][7][8] Radiomics emerged from the medical field of oncology[3][9][10] 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 imaged.

Process[edit]

Image acquisition[edit]

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.[2]

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.

Image segmentation[edit]

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”.[2]

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.

Feature extraction and qualification[edit]

After the segmentation, many features can be extracted and the relative net change from longitudinal images (delta-radiomics) can be computed. 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.[11][12][13][14] A detailed description of texture features for radiomics can be found in Parekh, et al.,(2016) [4] and Depeursinge et al. (2017).[15]

Due to its massive variety, feature reductions need to be implemented to eliminate redundant information. Hundreds of different features need to be evaluated with a selection algorithms to accelerate this process. Additionally, features that are unstable and non-reproducible should be eliminated since features with low-fidelity will likely lead to spurious findings and unrepeatable models.[16][17]

Analysis[edit]

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.

Databases[edit]

Creation[edit]

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.

Use[edit]

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.

Applications[edit]

Prediction of clinical outcomes[edit]

Aerts et al. (2014)[18] 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.[19][20] 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)[21] 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.

Nasief et al. (2019)[17] showed that changes of radiomic features over time in longitudinal images (delta-radiomic features, DRFs) can potentially be used as a biomarker to predict treatment response for pancreatic cancer. Their results showed that a Bayesian regularization neural network can be used to identify a subset of DRFs that demonstrated significant changes between good- and bad- responders following 2-4 weeks of treatment with an AUC = 0.94. They also showed (Nasief et al., 2020) that DRFs are independent predictor of survival and if combined with the clinical biomarker CA19-9 can improve treatment response prediction and increase the possibility for response-based treatment adaptation .[22]

Several studies have also showed that 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.[23][24][25][26][27][28][29] Using this technique an algorithm 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 personalized therapy in the emerging field of immunooncology.[30] Other studies have also demonstrated the utility of radiomics for predicting immunotherapy response of NSCLC patients using pre-treatment CT[31] and PET/CT images.[32]

Radiomics remains inferior to conventional techniques in some applications, suggesting the necessity of continued improvement and manipulation of Radiomics features to different clinical scenarios. For instance, Ludwig et. al (2020)[33] demonstrated that morphological Radiomics features were inferior to previously established features in the discrimination of intracranial aneurysm rupture status from 3-dimensional rotational angiography.

Prognostication[edit]

Radiomic studies have shown that image-based markers have the potential to provide information orthogonal to staging and biomarkers and improve prognostication.[34][35][36]

Prediction risk of distant metastasis[edit]

Metastatic potential of tumors may also be predicted by radiomic features.[37][38] 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.[37] 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[edit]

Lung tumor biological mechanisms may demonstrate distinct and complex imaging patterns.[39][40][1] In particular, Aerts et al. (2014)[1] 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.[41][42][43]

Image guided radiotherapy[edit]

Radiomics offers the advantage to be non invasive and can therefore be repeated prospectively 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.[44][45]

Distinguishing true progression from radionecrosis[edit]

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 in a 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.[46]

Prediction of physiological events[edit]

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.[47]

Multiparametric radiomics[edit]

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.[48] 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[edit]

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.[48]

Stroke[edit]

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.[48] 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).

See also[edit]

References[edit]

  1. ^ a b c d Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. (March 2012). "Radiomics: extracting more information from medical images using advanced feature analysis". European Journal of Cancer. 48 (4): 441–6. doi:10.1016/j.ejca.2011.11.036. PMC 4533986. PMID 22257792.
  2. ^ a b c Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. (November 2012). "Radiomics: the process and the challenges". Magnetic Resonance Imaging. 30 (9): 1234–48. doi:10.1016/j.mri.2012.06.010. PMC 3563280. PMID 22898692.
  3. ^ a b Gillies RJ, Kinahan PE, Hricak H (February 2016). "Radiomics: Images Are More than Pictures, They Are Data". Radiology. 278 (2): 563–77. doi:10.1148/radiol.2015151169. PMC 4734157. PMID 26579733.
  4. ^ a b Parekh V, Jacobs MA (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.
  5. ^ Yip SS, Aerts HJ (July 2016). "Applications and limitations of radiomics". Physics in Medicine and Biology. 61 (13): R150-66. Bibcode:2016PMB....61R.150Y. doi:10.1088/0031-9155/61/13/R150. PMC 4927328. PMID 27269645.
  6. ^ Yip SS, Liu Y, Parmar C, Li Q, Liu S, Qu F, et al. (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.
  7. ^ Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ (January 2013). "Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis". European Journal of Nuclear Medicine and Molecular Imaging. 40 (1): 133–40. doi:10.1007/s00259-012-2247-0. PMID 23064544. S2CID 24695383.
  8. ^ Cook GJ, Siddique M, Taylor BP, Yip C, Chicklore S, Goh V (2014). "Radiomics in PET: Principles and applications". Clinical and Translational Imaging. 2 (3): 269–276. doi:10.1007/s40336-014-0064-0.
  9. ^ Parekh VS, Jacobs MA (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. PMC 5686135. PMID 29152563.
  10. ^ Parekh VS, Jacobs MA (2019-03-04). "Deep learning and radiomics in precision medicine". Expert Review of Precision Medicine and Drug Development. 4 (2): 59–72. doi:10.1080/23808993.2019.1585805. PMC 6508888. PMID 31080889.
  11. ^ 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.
  12. ^ Pentland AP (June 1984). "Fractal-based description of natural scenes". IEEE Transactions on Pattern Analysis and Machine Intelligence. 6 (6): 661–74. doi:10.1109/TPAMI.1984.4767591. PMID 22499648. S2CID 17415943.
  13. ^ 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.
  14. ^ Thibault G, Angulo J, Meyer F (March 2014). "Advanced statistical matrices for texture characterization: application to cell classification". IEEE Transactions on Bio-Medical Engineering. 61 (3): 630–7. doi:10.1109/TBME.2013.2284600. PMID 24108747. S2CID 11319154.
  15. ^ Ranjbar S, Mitchell JR (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.
  16. ^ Tunali, Ilke; Hall, Lawrence O.; Napel, Sandy; Cherezov, Dmitry; Guvenis, Albert; Gillies, Robert J.; Schabath, Matthew B. (23 September 2019). "Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions". Medical Physics. 46 (11): 5075–5085. doi:10.1002/mp.13808. PMC 6842054. PMID 31494946.
  17. ^ a b Nasief, Haidy; Zheng, Cheng; Schott, Diane; Hall, William; Tsai, Susan; Erickson, Beth; Allen Li, X. (4 October 2019). "A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer". NPJ Precision Oncology. 3 (1): 25. doi:10.1038/s41698-019-0096-z. PMC 6778189. PMID 31602401.
  18. ^ Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al. (Jun 2014). "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach". Nat Commun. 5: 4006. Bibcode:2014NatCo...5.4006A. doi:10.1038/ncomms5006. PMC 4059926. PMID 24892406.
  19. ^ Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, et al. (March 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.
  20. ^ Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, et al. (December 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.
  21. ^ Parmar C, Leijenaar RT, Grossmann P, Rios Velazquez E, Bussink J, Rietveld D, et al. (June 2015). "Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer". Scientific Reports. 5: 11044. Bibcode:2015NatSR...511044P. doi:10.1038/srep11044. PMC 4937496. PMID 26251068.
  22. ^ Nasief, Haidy; Hall, William; Zheng, Cheng; Tsai, Susan; Wang, Liang; Erickson, Beth; Li, X. Allen (8 January 2020). "Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9". Frontiers in Oncology. 9: 1464. doi:10.3389/fonc.2019.01464. PMC 6960122. PMID 31970088.
  23. ^ Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, et al. (March 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): 369–78. doi:10.2967/jnumed.110.082404. PMC 3789272. PMID 21321270.
  24. ^ Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, et al. (January 2015). "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.
  25. ^ van Rossum PS, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ, et al. (May 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.
  26. ^ Yip SS, Coroller TP, Sanford NN, Mamon H, Aerts HJ, Berbeco RI (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.
  27. ^ Zhang H, Tan S, Chen W, Kligerman S, Kim G, D'Souza WD, et al. (January 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.
  28. ^ Cheng NM, Fang YH, Lee LY, Chang JT, Tsan DL, Ng SH, et al. (March 2015). "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): 419–28. doi:10.1007/s00259-014-2933-1. PMID 25339524. S2CID 6167165.
  29. ^ Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. (January 2013). "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.
  30. ^ Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. (September 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.
  31. ^ Tunali I, Gray JE, Qi J, Abdallah M, Jeong DK, Guvenis A, Gillies RJ, Gillies RJ (Jan 2019). "Novel Clinical and Radiomic Predictors of Rapid Disease Progression Phenotypes among Lung Cancer Patients Treated with Immunotherapy: An Early Report". Lung Cancer. 129: 75–79. doi:10.1016/j.lungcan.2019.01.010. PMC 6450086. PMID 30797495.
  32. ^ Mu W, Tunali I, Gray JE, Qi J, Gillies RJ, Gillies RJ (Dec 2019). "Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy". Eur J Nucl Med Mol Imaging. 47 (5): 1168–1182. doi:10.1007/s00259-019-04625-9. PMID 31807885. S2CID 208650067.
  33. ^ Ludwig, CG; Lauric, A; Malek, JA; Mulligan, R; Malek, AM (2020). "Performance of Radiomics derived morphological features for prediction of aneurysm rupture status". Journal of NeuroInterventional Surgery. doi:10.1136/neurintsurg-2020-016808. PMID 33158993. S2CID 226274492.
  34. ^ Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al. (Jun 2014). "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach". Nat Commun. 5: 4006. Bibcode:2014NatCo...5.4006A. doi:10.1038/ncomms5006. PMC 4059926. PMID 24892406.
  35. ^ Tunali I, Stringfield O, Guvenis A, Wang H, Liu Y, et al. (Aug 2017). "Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients". Oncotarget. 8 (56): 96013–96026. doi:10.18632/oncotarget.21629. PMC 5707077. PMID 29221183.
  36. ^ Huang P, Park S, Yan R, Lee J, Chu LC, Lin CT, Hussien A, Rathmell J, Thomas B, Chen C, et al. (Sep 2018). "Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study". Radiology. 286 (1): 286–295. doi:10.1148/radiol.2017162725. PMC 5779085. PMID 28872442.
  37. ^ a b Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, et al. (March 2015). "CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma". Radiotherapy and Oncology. 114 (3): 345–50. doi:10.1016/j.radonc.2015.02.015. PMC 4400248. PMID 25746350.
  38. ^ Vallières M, Freeman CR, Skamene SR, El Naqa I (July 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): 5471–96. Bibcode:2015PMB....60.5471V. doi:10.1088/0031-9155/60/14/5471. PMID 26119045.
  39. ^ Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, et al. (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.
  40. ^ Yip SS, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E, et al. (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. PMC 5373502. PMID 27688480.
  41. ^ Brown R, Zlatescu M, Sijben A, Roldan G, Easaw J, Forsyth P, et al. (April 2008). "The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendroglioma". Clinical Cancer Research. 14 (8): 2357–62. doi:10.1158/1078-0432.CCR-07-1964. PMID 18413825.
  42. ^ Drabycz S, Roldán G, de Robles P, Adler D, McIntyre JB, Magliocco AM, et al. (January 2010). "An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging". NeuroImage. 49 (2): 1398–405. doi:10.1016/j.neuroimage.2009.09.049. PMID 19796694. S2CID 18857965.
  43. ^ Gutman DA, Dunn WD, Grossmann P, Cooper LA, Holder CA, Ligon KL, et al. (December 2015). "Somatic mutations associated with MRI-derived volumetric features in glioblastoma". Neuroradiology. 57 (12): 1227–37. doi:10.1007/s00234-015-1576-7. PMC 4648958. PMID 26337765.
  44. ^ Sun R, Orlhac F, Robert C, Reuzé S, Schernberg A, Buvat I, et al. (August 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.
  45. ^ Yip SS, Coroller TP, Sanford NN, Huynh E, Mamon H, Aerts HJ, Berbeco RI (January 2016). "Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction". Physics in Medicine and Biology. 61 (2): 906–22. Bibcode:2016PMB....61..906Y. doi:10.1088/0031-9155/61/2/906. PMID 26738433.
  46. ^ Peng L, Parekh V, Huang P, Lin DD, Sheikh K, Baker B, et al. (November 2018). "Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics". International Journal of Radiation Oncology, Biology, Physics. 102 (4): 1236–1243. doi:10.1016/j.ijrobp.2018.05.041. PMC 6746307. PMID 30353872.
  47. ^ Hassan I, Kotrotsou A, Bakhtiari AS, Thomas GA, Weinberg JS, Kumar AJ, et al. (May 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.
  48. ^ a b c Parekh VS, Jacobs MA (2018-09-25). "MPRAD: A Multiparametric Radiomics Framework". Breast Cancer Research and Treatment. 180 (2): 407–421. arXiv:1809.09973. Bibcode:2018arXiv180909973P. doi:10.1007/s10549-020-05533-5. PMC 7066290. PMID 32020435.