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((Short description|Method that extracts features from radiographic medical images))
((Multiple issues|((technical|date=April 2016))((more citations needed|date=April 2016))((essay-like|date=April 2016))))

In the field of medicine, '''radiomics''' is a method that extracts a large number of features from [[medical image]]s using data-characterisation algorithms.<ref name="Lambin-2012">((cite journal | vauthors = Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ | display-authors = 6 | title = Radiomics: extracting more information from medical images using advanced feature analysis | journal = European Journal of Cancer | volume = 48 | issue = 4 | pages = 441–6 | date = March 2012 | pmid = 22257792 | pmc = 4533986 | doi = 10.1016/j.ejca.2011.11.036 ))</ref><ref name="ReferenceA">((cite journal | vauthors = Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ | display-authors = 6 | title = Radiomics: the process and the challenges | journal = Magnetic Resonance Imaging | volume = 30 | issue = 9 | pages = 1234–48 | date = November 2012 | pmid = 22898692 | pmc = 3563280 | doi = 10.1016/j.mri.2012.06.010 ))</ref><ref name="Gillies-2016">((cite journal | vauthors = Gillies RJ, Kinahan PE, Hricak H | title = Radiomics: Images Are More than Pictures, They Are Data | journal = Radiology | volume = 278 | issue = 2 | pages = 563–77 | date = February 2016 | pmid = 26579733 | pmc = 4734157 | doi = 10.1148/radiol.2015151169 ))</ref><ref name="Parekh-2016">((cite journal | vauthors = Parekh V, Jacobs MA | title = Radiomics: a new application from established techniques | journal = Expert Review of Precision Medicine and Drug Development | volume = 1 | issue = 2 | pages = 207–226 | year = 2016 | pmid = 28042608 | pmc = 5193485 | doi = 10.1080/23808993.2016.1164013 ))</ref><ref>((cite journal | vauthors = Yip SS, Aerts HJ | title = Applications and limitations of radiomics | journal = Physics in Medicine and Biology | volume = 61 | issue = 13 | pages = R150-66 | date = July 2016 | pmid = 27269645 | pmc = 4927328 | doi = 10.1088/0031-9155/61/13/R150 | bibcode = 2016PMB....61R.150Y ))</ref> These features, termed radiomic features, have the potential to uncover tumoral patterns and characteristics that fail to be appreciated by the naked eye.<ref>((cite journal | vauthors = Yip SS, Liu Y, Parmar C, Li Q, Liu S, Qu F, Ye Z, Gillies RJ, Aerts HJ | display-authors = 6 | title = Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer | journal = Scientific Reports | volume = 7 | issue = 1 | pages = 3519 | date = June 2017 | pmid = 28615677 | pmc = 5471260 | doi = 10.1038/s41598-017-02425-5 | bibcode = 2017NatSR...7.3519Y ))</ref> The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various cancer types, thus providing valuable information for personalized therapy.<ref name="Lambin-2012"/><ref>((cite journal | vauthors = Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ | title = Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis | journal = European Journal of Nuclear Medicine and Molecular Imaging | volume = 40 | issue = 1 | pages = 133–40 | date = January 2013 | pmid = 23064544 | doi = 10.1007/s00259-012-2247-0 | s2cid = 24695383 ))</ref><ref>((cite journal | vauthors = Cook GJ, Siddique M, Taylor BP, Yip C, Chicklore S, Goh V |doi=10.1007/s40336-014-0064-0 |title=Radiomics in PET: Principles and applications |journal=Clinical and Translational Imaging |volume=2 |issue=3 |pages=269–276 |year=2014 |doi-access=free ))</ref> Radiomics emerged from the medical fields of [[radiology]] and [[oncology]]<ref name="Gillies-2016" /><ref>((cite journal | vauthors = Parekh VS, Jacobs MA | title = Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI | journal = npj Breast Cancer | volume = 3 | issue = 1 | pages = 43 | date = 2017-11-14 | pmid = 29152563 | pmc = 5686135 | doi = 10.1038/s41523-017-0045-3 ))</ref><ref>((cite journal | vauthors = Parekh VS, Jacobs MA | title = Deep learning and radiomics in precision medicine | journal = Expert Review of Precision Medicine and Drug Development | volume = 4 | issue = 2 | pages = 59–72 | date = 2019-03-04 | pmid = 31080889 | pmc = 6508888 | doi = 10.1080/23808993.2019.1585805 ))</ref> and is the most advanced in applications within these fields. However, the technique can be applied to any medical study where a pathological process can be imaged.

== Process ==

=== Image acquisition ===
The image data is provided by radiological modalities as CT,<ref>((Cite journal|last1=Kumar|first1=Virendra|last2=Gu|first2=Yuhua|last3=Basu|first3=Satrajit|last4=Berglund|first4=Anders|last5=Eschrich|first5=Steven A.|last6=Schabath|first6=Matthew B.|last7=Forster|first7=Kenneth|last8=Aerts|first8=Hugo J.W.L.|last9=Dekker|first9=Andre|last10=Fenstermacher|first10=David|last11=Goldgof|first11=Dmitry B.|date=November 2012|title=Radiomics: the process and the challenges|journal=Magnetic Resonance Imaging|language=en|volume=30|issue=9|pages=1234–1248|doi=10.1016/j.mri.2012.06.010|pmc=3563280|pmid=22898692))</ref> MRI,<ref>((Cite journal|last1=Schick|first1=Ulrike|last2=Lucia|first2=François|last3=Dissaux|first3=Gurvan|last4=Visvikis|first4=Dimitris|last5=Badic|first5=Bogdan|last6=Masson|first6=Ingrid|last7=Pradier|first7=Olivier|last8=Bourbonne|first8=Vincent|last9=Hatt|first9=Mathieu|date=December 2019|title=MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology|journal=The British Journal of Radiology|language=en|volume=92|issue=1104|pages=20190105|doi=10.1259/bjr.20190105|issn=0007-1285|pmc=6913356|pmid=31538516))</ref> PET/CT or even PET/MR.<ref>((Cite journal|last1=Amorim|first1=Barbara Juarez|last2=Torrado-Carvajal|first2=Angel|last3=Esfahani|first3=Shadi A|last4=Marcos|first4=Sara S|last5=Vangel|first5=Mark|last6=Stein|first6=Dan|last7=Groshar|first7=David|last8=Catalano|first8=Onofrio A|date=October 2020|title=PET/MRI Radiomics in Rectal Cancer: a Pilot Study on the Correlation Between PET- and MRI-Derived Image Features with a Clinical Interpretation|url=http://link.springer.com/10.1007/s11307-020-01484-x|journal=Molecular Imaging and Biology|language=en|volume=22|issue=5|pages=1438–1445|doi=10.1007/s11307-020-01484-x|pmid=32270337 |s2cid=215411812 |issn=1536-1632))</ref> The produced raw data volumes are used to find different pixel/voxel characteristics through extraction tools.<ref name="ReferenceA"/>

The extracted features are saved in large databases where clinics have access so as to enable broadly collaborative and cumulative work in which all can benefit from growing amounts of data, ideally enabling a more precise workflow.

=== Image segmentation ===
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”.<ref name="ReferenceA"/>

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 ===
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. [[Co-occurrence matrix|gray-level co-occurrence matrix]] (GLCM), run length matrix (RLM), [[Gray level size zone matrix|size zone matrix]] (SZM), and neighborhood gray tone difference matrix (NGTDM) derived textures), [[Image texture|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.<ref>((cite journal |doi=10.1016/S0146-664X(75)80008-6 |title=Texture analysis using gray level run lengths |journal=Computer Graphics and Image Processing |volume=4 |issue=2 |pages=172–179 |year=1975 |last1=Galloway |first1=Mary M ))</ref><ref>((cite journal | vauthors = Pentland AP | title = Fractal-based description of natural scenes | journal = IEEE Transactions on Pattern Analysis and Machine Intelligence | volume = 6 | issue = 6 | pages = 661–74 | date = June 1984 | pmid = 22499648 | doi = 10.1109/TPAMI.1984.4767591 | s2cid = 17415943 ))</ref><ref>((cite journal  | vauthors = Amadasun M, King R |doi=10.1109/21.44046 |title=Textural features corresponding to textural properties |journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=19 |issue=5 |pages=1264–1274 |year=1989 ))</ref><ref>((cite journal | vauthors = Thibault G, Angulo J, Meyer F | title = Advanced statistical matrices for texture characterization: application to cell classification | journal = IEEE Transactions on Bio-Medical Engineering | volume = 61 | issue = 3 | pages = 630–7 | date = March 2014 | pmid = 24108747 | doi = 10.1109/TBME.2013.2284600 | s2cid = 11319154 ))</ref>
A detailed description of texture features for radiomics can be found in Parekh et al. (2016) <ref name="Parekh-2016" /> and Depeursinge et al. (2017).<ref>((Cite book | vauthors = Ranjbar S, Mitchell JR|doi=10.1016/B978-0-12-812133-7.00008-9 |chapter=An Introduction to Radiomics: An Evolving Cornerstone of Precision Medicine |title=Biomedical Texture Analysis |pages=223–245 |year=2017 |isbn=9780128121337 ))</ref>

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.<ref>((cite journal |last1=Tunali |first1=Ilke |last2=Hall |first2=Lawrence O. |last3=Napel |first3=Sandy |last4=Cherezov |first4=Dmitry |last5=Guvenis |first5=Albert |last6=Gillies |first6=Robert J. |last7=Schabath |first7=Matthew B. |title=Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions |journal=Medical Physics |date=23 September 2019 |volume=46 |issue=11 |pages=5075–5085 |doi=10.1002/mp.13808 |pmid=31494946 |pmc=6842054 |bibcode=2019MedPh..46.5075T ))</ref><ref name=pmid31602401>((cite journal |last1=Nasief |first1=Haidy |last2=Zheng |first2=Cheng |last3=Schott |first3=Diane |last4=Hall |first4=William |last5=Tsai |first5=Susan |last6=Erickson |first6=Beth |last7=Allen Li |first7=X. |title=A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer |journal= npj Precision Oncology|date=4 October 2019 |volume=3 |issue=1 |pages=25 |doi=10.1038/s41698-019-0096-z |pmid=31602401 |pmc=6778189 ))</ref>

=== Analysis ===
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 ===

==== Creation ====
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 [[Health Insurance Portability and Accountability Act|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 ====
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==
=== Prediction of clinical outcomes ===
Aerts et al. (2014)<ref name="Decoding tumour phenotype by noninv">((cite journal | vauthors = Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D et al. | title = Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. | journal = Nat Commun | volume = 5 | pages = 4006 |  date = Jun 2014| pmid = 24892406 | doi = 10.1038/ncomms5006| pmc = 4059926 | bibcode = 2014NatCo...5.4006A ))</ref> 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 [[CT scan|computed tomography]] (CT) images acquired before any treatment. Tumor volumes were defined either by expert radiation oncologists or using semiautomatic segmentation methods.<ref>((cite journal | vauthors = Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, Bendtsen C, Velazquez ER, Dekker A, Aerts H, Lambin P, Li X, Tian J, Gatenby RA, Gillies RJ | display-authors = 6 | title = Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach | journal = Pattern Recognition | volume = 46 | issue = 3 | pages = 692–702 | date = March 2013 | pmid = 23459617 | pmc = 3580869 | doi = 10.1016/j.patcog.2012.10.005 | bibcode = 2013PatRe..46..692G ))</ref><ref>((cite journal | vauthors = Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, Lewis JH, De Ruysscher D, Kikinis R, Lambin P, Aerts HJ | display-authors = 6 | title = Volumetric CT-based segmentation of NSCLC using 3D-Slicer | journal = Scientific Reports | volume = 3 | pages = 3529 | date = December 2013 | pmid = 24346241 | pmc = 3866632 | doi = 10.1038/srep03529 | bibcode = 2013NatSR...3E3529V ))</ref> 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)<ref>((cite journal | vauthors = Parmar C, Leijenaar RT, Grossmann P, Rios Velazquez E, Bussink J, Rietveld D, Rietbergen MM, Haibe-Kains B, Lambin P, Aerts HJ | display-authors = 6 | title = Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer | journal = Scientific Reports | volume = 5 | pages = 11044 | date = June 2015 | pmid = 26251068 | pmc = 4937496 | doi = 10.1038/srep11044 | bibcode = 2015NatSR...511044P ))</ref> 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|head-and-neck cancer]] patients and vice versa.

Nasief et al. (2019)<ref name=pmid31602401/> 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 .<ref>((cite journal |last1=Nasief |first1=Haidy |last2=Hall |first2=William |last3=Zheng |first3=Cheng |last4=Tsai |first4=Susan |last5=Wang |first5=Liang |last6=Erickson |first6=Beth |last7=Li |first7=X. Allen |title=Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9 |journal=Frontiers in Oncology |date=8 January 2020 |volume=9 |pages=1464 |doi=10.3389/fonc.2019.01464 |pmid=31970088 |pmc=6960122 |doi-access=free ))</ref>

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.<ref>((cite journal | vauthors = Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, Corcos L, Visvikis D | display-authors = 6 | title = Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer | journal = Journal of Nuclear Medicine | volume = 52 | issue = 3 | pages = 369–78 | date = March 2011 | pmid = 21321270 | pmc = 3789272 | doi = 10.2967/jnumed.110.082404 ))</ref><ref>((cite journal | vauthors = Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, Hindié E, Martineau A, Pradier O, Hustinx R, Perdrisot R, Guillevin R, El Naqa I, Visvikis D | display-authors = 6 | title = 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 = Journal of Nuclear Medicine | volume = 56 | issue = 1 | pages = 38–44 | date = January 2015 | pmid = 25500829 | doi = 10.2967/jnumed.114.144055 | doi-access = free ))</ref><ref>((cite journal | vauthors = van Rossum PS, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ, Court LE, Lin SH | display-authors = 6 | title = 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 = Journal of Nuclear Medicine | volume = 57 | issue = 5 | pages = 691–700 | date = May 2016 | pmid = 26795288 | doi = 10.2967/jnumed.115.163766 | doi-access = free ))</ref><ref>((cite journal | vauthors = Yip SS, Coroller TP, Sanford NN, Mamon H, Aerts HJ, Berbeco RI | title = Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients | journal = Frontiers in Oncology | volume = 6 | pages = 72 | year = 2016 | pmid = 27066454 | pmc = 4810033 | doi = 10.3389/fonc.2016.00072 | doi-access = free ))</ref><ref>((cite journal | vauthors = Zhang H, Tan S, Chen W, Kligerman S, Kim G, D'Souza WD, Suntharalingam M, Lu W | display-authors = 6 | title = Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics | journal = International Journal of Radiation Oncology, Biology, Physics | volume = 88 | issue = 1 | pages = 195–203 | date = January 2014 | pmid = 24189128 | pmc = 3875172 | doi = 10.1016/j.ijrobp.2013.09.037 ))</ref><ref>((cite journal | vauthors = Cheng NM, Fang YH, Lee LY, Chang JT, Tsan DL, Ng SH, Wang HM, Liao CT, Yang LY, Hsu CH, Yen TC | display-authors = 6 | title = Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer | journal = European Journal of Nuclear Medicine and Molecular Imaging | volume = 42 | issue = 3 | pages = 419–28 | date = March 2015 | pmid = 25339524 | doi = 10.1007/s00259-014-2933-1 | s2cid = 6167165 ))</ref><ref>((cite journal | vauthors = Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, Marsden P, Ahmad S, Landau D | display-authors = 6 | title = Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? | journal = Journal of Nuclear Medicine | volume = 54 | issue = 1 | pages = 19–26 | date = January 2013 | pmid = 23204495 | doi = 10.2967/jnumed.112.107375 | doi-access = free ))</ref> 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 tool for personalized therapy in the emerging field of immunooncology.<ref>((cite journal | vauthors = Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S, Hollebecque A, Scoazec JY, Marabelle A, Massard C, Soria JC, Robert C, Paragios N, Deutsch E, Ferté C | display-authors = 6 | title = 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 | journal = The Lancet. Oncology | volume = 19 | issue = 9 | pages = 1180–1191 | date = September 2018 | pmid = 30120041 | doi = 10.1016/S1470-2045(18)30413-3 | s2cid = 52032552 ))</ref> Other studies have also demonstrated the utility of radiomics for predicting [[immunotherapy]] response of [[NSCLC]] patients using pre-treatment CT<ref>((cite journal | vauthors = Tunali I, Gray JE, Qi J, Abdallah M, Jeong DK, Guvenis A, Gillies RJ, Gillies RJ | title = Novel Clinical and Radiomic Predictors of Rapid Disease Progression Phenotypes among Lung Cancer Patients Treated with Immunotherapy: An Early Report | journal = Lung Cancer | volume = 129 | pages = 75–79 | date = Jan 2019 | pmid = 30797495| doi = 10.1016/j.lungcan.2019.01.010| pmc = 6450086 ))</ref> and [[PET/CT]] images.<ref>((cite journal | vauthors = Mu W, Tunali I, Gray JE, Qi J, Gillies RJ, Gillies RJ | title = Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy | journal = Eur J Nucl Med Mol Imaging | date = Dec 2019 | volume = 47 | issue = 5 | pages = 1168–1182 | pmid = 31807885| doi = 10.1007/s00259-019-04625-9| pmc = 8663718 | s2cid = 208650067 ))</ref>

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)<ref>((cite journal|last=Ludwig |first=CG|author2=Lauric, A |author3=Malek, JA|author4=Mulligan, R|author5=Malek, AM|title=Performance of Radiomics derived morphological features for prediction of aneurysm rupture status.|journal=Journal of NeuroInterventional Surgery|date=2020|volume=13|issue=8|pages=<!-- -->|pmid=33158993 |doi=10.1136/neurintsurg-2020-016808|s2cid=226274492))</ref> 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===
Radiomic studies have shown that image-based markers have the potential to provide information orthogonal to staging and biomarkers and improve prognostication.<ref name="Decoding tumour phenotype by noninv"/><ref>((cite journal | vauthors = Tunali I, Stringfield O, Guvenis A, Wang H, Liu Y, et al. | title = Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients | journal = Oncotarget | volume = 8 | issue = 56 | pages = 96013–96026 |  date = Aug 2017 | pmid = 29221183| doi = 10.18632/oncotarget.21629| pmc = 5707077 ))</ref><ref>((cite journal | vauthors = Huang P, Park S, Yan R, Lee J, Chu LC, Lin CT, Hussien A, Rathmell J, Thomas B, Chen C et al. | title = Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study | journal = Radiology| volume = 286 | issue = 1 | pages = 286–295 |  date = Sep 2018 | pmid = 28872442| doi = 10.1148/radiol.2017162725| pmc = 5779085 ))</ref>

=== Prediction risk of distant metastasis ===
Metastatic potential of tumors may also be predicted by radiomic features.<ref name="Coroller-2015">((cite journal | vauthors = Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJ | display-authors = 6 | title = CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma | journal = Radiotherapy and Oncology | volume = 114 | issue = 3 | pages = 345–50 | date = March 2015 | pmid = 25746350 | pmc = 4400248 | doi = 10.1016/j.radonc.2015.02.015 ))</ref><ref>((cite journal | vauthors = Vallières M, Freeman CR, Skamene SR, El Naqa I | title = A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities | journal = Physics in Medicine and Biology | volume = 60 | issue = 14 | pages = 5471–96 | date = July 2015 | pmid = 26119045 | doi = 10.1088/0031-9155/60/14/5471 | bibcode = 2015PMB....60.5471V | s2cid = 4709680 ))</ref> 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.<ref name="Coroller-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 cancer|Lung tumor]] biological mechanisms may demonstrate distinct and complex imaging patterns.<ref>((cite journal | vauthors = Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, Ye Z, Makrigiorgos M, Fennessy F, Mak RH, Gillies R, Quackenbush J, Aerts HJ | display-authors = 6 | title = Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer | journal = Cancer Research | volume = 77 | issue = 14 | pages = 3922–3930 | date = July 2017 | pmid = 28566328 | pmc = 5528160 | doi = 10.1158/0008-5472.CAN-17-0122 ))</ref><ref>((cite journal | vauthors = Yip SS, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E, Mak RH, Aerts HJ | display-authors = 6 | title = Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer | journal = Journal of Nuclear Medicine | volume = 58 | issue = 4 | pages = 569–576 | date = April 2017 | pmid = 27688480 | doi = 10.2967/jnumed.116.181826 | pmc = 5373502 ))</ref><ref name="Lambin-2012"/> In particular, Aerts et al. (2014)<ref name="Lambin-2012"/> 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.<ref>((cite journal | vauthors = 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 | display-authors = 6 | title = The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendroglioma | journal = Clinical Cancer Research | volume = 14 | issue = 8 | pages = 2357–62 | date = April 2008 | pmid = 18413825 | doi = 10.1158/1078-0432.CCR-07-1964 | doi-access = free ))</ref><ref>((cite journal | vauthors = Drabycz S, Roldán G, de Robles P, Adler D, McIntyre JB, Magliocco AM, Cairncross JG, Mitchell JR | display-authors = 6 | title = An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging | journal = NeuroImage | volume = 49 | issue = 2 | pages = 1398–405 | date = January 2010 | pmid = 19796694 | doi = 10.1016/j.neuroimage.2009.09.049 | s2cid = 18857965 ))</ref><ref>((cite journal | vauthors = Gutman DA, Dunn WD, Grossmann P, Cooper LA, Holder CA, Ligon KL, Alexander BM, Aerts HJ | display-authors = 6 | title = Somatic mutations associated with MRI-derived volumetric features in glioblastoma | journal = Neuroradiology | volume = 57 | issue = 12 | pages = 1227–37 | date = December 2015 | pmid = 26337765 | pmc = 4648958 | doi = 10.1007/s00234-015-1576-7 ))</ref>

=== Image guided radiotherapy ===
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.<ref>((cite journal | vauthors = Sun R, Orlhac F, Robert C, Reuzé S, Schernberg A, Buvat I, Deutsch E, Ferté C | display-authors = 6 | title = In Regard to Mattonen et al | journal = International Journal of Radiation Oncology, Biology, Physics | volume = 95 | issue = 5 | pages = 1544–1545 | date = August 2016 | pmid = 27479727 | doi = 10.1016/j.ijrobp.2016.03.038 ))</ref><ref>((cite journal | vauthors = Yip SS, Coroller TP, Sanford NN, Huynh E, Mamon H, Aerts HJ, Berbeco RI | title = Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction | journal = Physics in Medicine and Biology | volume = 61 | issue = 2 | pages = 906–22 | date = January 2016 | pmid = 26738433 | doi = 10.1088/0031-9155/61/2/906 | bibcode = 2016PMB....61..906Y | s2cid = 21799373 ))</ref>

=== Distinguishing true progression from radionecrosis ===
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.<ref>((cite journal | vauthors = Peng L, Parekh V, Huang P, Lin DD, Sheikh K, Baker B, Kirschbaum T, Silvestri F, Son J, Robinson A, Huang E, Ames H, Grimm J, Chen L, Shen C, Soike M, McTyre E, Redmond K, Lim M, Lee J, Jacobs MA, Kleinberg L | display-authors = 6 | title = Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics | language = English | journal = International Journal of Radiation Oncology, Biology, Physics | volume = 102 | issue = 4 | pages = 1236–1243 | date = November 2018 | pmid = 30353872 | pmc = 6746307 | doi = 10.1016/j.ijrobp.2018.05.041 ))</ref>

===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.<ref>((cite journal | vauthors = Hassan I, Kotrotsou A, Bakhtiari AS, Thomas GA, Weinberg JS, Kumar AJ, Sawaya R, Luedi MM, Zinn PO, Colen RR | display-authors = 6 | title = Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity | journal = Scientific Reports | volume = 6 | pages = 25295 | date = May 2016 | pmid = 27151623 | pmc = 4858648 | doi = 10.1038/srep25295 | bibcode = 2016NatSR...625295H ))</ref>

===Imaging genomics===

In imaging genomics, [[radiogenomics]] can be used to create imaging [[biomarkers]] that can identify the genomics of a disease, especially [[cancer]] without the use of a [[biopsy]].  Various techniques for dealing with high-dimensional data are used to find statistically significant correlations between [[MRI]], [[X-ray computed tomography|CT]], and [[Positron emission tomography|PET]] imaging features and the genomics of disease, including [[Significance analysis of microarrays|SAM]], [[VAMPIRE]], and [[GSEA]].

The imaging radiogenomic approach has proven successful<ref>((cite journal |doi=10.1073/pnas.0801279105 |title=Identification of noninvasive imaging surrogates for brain tumor gene-expression modules |year=2008 |last1=Diehn |first1=Maximilian |last2=Nardini |first2=Christine |last3=Wang |first3=David S. |last4=McGovern |first4=Susan |last5=Jayaraman |first5=Mahesh |last6=Liang |first6=Yu |last7=Aldape |first7=Kenneth |last8=Cha |first8=Soonmee |last9=Kuo |first9=Michael D. |journal=Proceedings of the National Academy of Sciences |volume=105 |issue=13 |pmid=18362333 |pages=5213–8 |pmc=2278224 |doi-access=free ))</ref> in determining the MRI phenotype associated genetics of [[glioblastoma]], a highly aggressive type of brain tumor with low prognosis. The first large-scale MR-imaging microRNA-mRNA correlative study in GBM was published by Zinn et al. in 2011<ref>((cite journal |doi=10.1371/journal.pone.0025451 |title=Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme |year=2011 |editor1-last=Deutsch |editor1-first=Eric |last1=Zinn |first1=Pascal O. |last2=Mahajan |first2=Bhanu |last3=Sathyan |first3=Pratheesh |last4=Singh |first4=Sanjay K. |last5=Majumder |first5=Sadhan |last6=Jolesz |first6=Ferenc A. |last7=Colen |first7=Rivka R. |journal=PLOS ONE |volume=6 |issue=10 |pages=e25451 |pmid=21998659 |pmc=3187774|bibcode=2011PLoSO...625451Z |doi-access=free ))</ref>  Similar studies in [[liver cancer]] have successfully determined much of the liver cancer genome from non-invasive imaging features.<ref>((cite journal |doi=10.1016/j.ejrad.2009.01.050 |title=Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging |year=2009 |last1=Rutman |first1=Aaron M. |last2=Kuo |first2=Michael D. |journal=European Journal of Radiology |volume=70 |issue=2 |pages=232–41 |pmid=19303233))</ref> Gevaert et al. at [[Stanford University]] have shown the potential to link image features of non-small cell lung nodules in CT scans to predict survival by leveraging publicly available gene expression data.<ref>((cite journal | doi= 10.1148/radiol.12111607 |title=Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results |year=2012 |last1=Gevaert |first1=O. |last2=Xu |first2=J. |last3=Hoang |first3=C. D. |last4=Leung |first4=A.N. |last5=Xu |first5=Y. |last6=Quon |first6=A. |last7=Rubin |first7=D.L. |last8=Napel |first8=S. |last9=Plevritis |first9=S.K. |journal=Radiology |volume=264 |issue=2 |pages=387–96 |pmid=22723499 |pmc=3401348 ))</ref> This publication was accompanied by an editorial discussing the synergy between imaging and genomics.<ref>((cite journal | doi= 10.1148/radiol.12120871 |title=Imaging and genomics: is there a synergy? |year=2012 |last1=Jaffe |first1=C. |journal=Radiology |volume=264 |issue=2 |pages=329–31 |pmid=22821693|doi-access=free ))</ref> More recently, Mu Zhou et al.  at [[Stanford University]] have showed that multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of non-small cell lung cancer.<ref>((cite journal |doi= 10.1148/radiol.2017161845 |title=Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications |year=2017 |last1=Zhou |first1=M. |last2=Leung |first2=A. |last3= Echegaray |first3=S. |last4=Gentles |first4=A. |last5= Shrager |first5=J. |last6= Plevritis |first6=S. |last7=Rubin |first7=D.L. |last8=Napel |first8=S. |last9=Gevaert |first9= O. |journal=Radiology |volume=286 |issue=1 |pages=307–15 |pmid=28727543 |pmc=5749594 ))</ref>

Several radiogenomic studies have now been carried out in prostate cancer,<ref>((Cite journal|last1=Houlahan|first1=Kathleen E.|last2=Salmasi|first2=Amirali|last3=Sadun|first3=Taylor Y.|last4=Pooli|first4=Aydin|last5=Felker|first5=Ely R.|last6=Livingstone|first6=Julie|last7=Huang|first7=Vincent|last8=Raman|first8=Steven S.|last9=Ahuja|first9=Preeti|last10=Sisk|first10=Anthony E.|last11=Boutros|first11=Paul C.|date=July 2019|title=Molecular Hallmarks of Multiparametric Magnetic Resonance Imaging Visibility in Prostate Cancer|journal=European Urology|volume=76|issue=1|pages=18–23|doi=10.1016/j.eururo.2018.12.036|issn=1873-7560|pmid=30685078|pmc=10228592 |s2cid=59304426 ))</ref><ref>((Cite journal|last1=Li|first1=Ping|last2=You|first2=Sungyong|last3=Nguyen|first3=Christopher|last4=Wang|first4=Yanping|last5=Kim|first5=Jayoung|last6=Sirohi|first6=Deepika|last7=Ziembiec|first7=Asha|last8=Luthringer|first8=Daniel|last9=Lin|first9=Shih-Chieh|last10=Daskivich|first10=Timothy|last11=Wu|first11=Jonathan|date=2018|title=Genes involved in prostate cancer progression determine MRI visibility|journal=Theranostics|volume=8|issue=7|pages=1752–1765|doi=10.7150/thno.23180|issn=1838-7640|pmc=5858498|pmid=29556354))</ref><ref>((Cite journal|last1=Purysko|first1=Andrei S.|last2=Magi-Galluzzi|first2=Cristina|last3=Mian|first3=Omar Y.|last4=Sittenfeld|first4=Sarah|last5=Davicioni|first5=Elai|last6=du Plessis|first6=Marguerite|last7=Buerki|first7=Christine|last8=Bullen|first8=Jennifer|last9=Li|first9=Lin|last10=Madabhushi|first10=Anant|last11=Stephenson|first11=Andrew|date=September 2019|title=Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings|journal=European Radiology|volume=29|issue=9|pages=4861–4870|doi=10.1007/s00330-019-06114-x|issn=1432-1084|pmc=6684343|pmid=30847589))</ref> Some have noted that genetic features correlated with MRI signal are often also associated with more aggressive prostate cancer.<ref>((Cite journal|last1=Norris|first1=Joseph M.|last2=Simpson|first2=Benjamin S.|last3=Parry|first3=Marina A.|last4=Kasivisvanathan|first4=Veeru|last5=Allen|first5=Clare|last6=Ball|first6=Rhys|last7=Freeman|first7=Alex|last8=Kelly|first8=Daniel|last9=Kirkham|first9=Alex|last10=Whitaker|first10=Hayley C.|last11=Emberton|first11=Mark|date=March 2020|title=Genetic correlates of prostate cancer visibility (and invisibility) on multiparametric magnetic resonance imaging: it's time to take stock|journal=BJU International|volume=125|issue=3|pages=340–342|doi=10.1111/bju.14919|issn=1464-410X|pmid=31600865|doi-access=free))</ref> A systematic review of the genetic features found in more visible lesions on MRI identified multiple studies which had found loss of the tumour suppressor [[PTEN (gene)|PTEN]], increased [[gene expression]] linked to [[cell proliferation]] as well as cell-ECM interactions.<ref name="Norris-2020">((Cite journal|last1=Norris|first1=Joseph M.|last2=Simpson|first2=Benjamin S.|last3=Parry|first3=Marina A.|last4=Allen|first4=Clare|last5=Ball|first5=Rhys|last6=Freeman|first6=Alex|last7=Kelly|first7=Daniel|last8=Kim|first8=Hyung L.|last9=Kirkham|first9=Alex|last10=You|first10=Sungyong|last11=Kasivisvanathan|first11=Veeru|date=2020-07-01|title=Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis|journal=European Urology Open Science|language=en|volume=20|pages=37–47|doi=10.1016/j.euros.2020.06.006|pmid=33000006|pmc=7497895|issn=2666-1683|doi-access=free))</ref> This may indicate that certain genetic features drives cellular changes which ultimately effect fluid movement which can be seen on MRI and these features are predominantly associated with poor prognosis.<ref name="Norris-2020" /> The combination of more dangerous genetic alterations, [[histology]] and clinical outcomes for patients with prostate tumours which are visible on mpMRI, has led to suggestions that the definition of 'clinically significant cancer' should be at least in part based on mpMRI findings.<ref>((Cite journal|last1=Norris|first1=Joseph M.|last2=Simpson|first2=Benjamin S.|last3=Freeman|first3=Alex|last4=Kirkham|first4=Alex|last5=Whitaker|first5=Hayley C.|last6=Emberton|first6=Mark|title=Conspicuity of prostate cancer on multiparametric magnetic resonance imaging: A cross-disciplinary translational hypothesis|journal=The FASEB Journal|year=2020|volume=34|issue=11|language=en|pages=14150–14159|doi=10.1096/fj.202001466R|pmc=8436756|pmid=32920937|issn=1530-6860|doi-access=free))</ref>

The radiogenomic approach has been also successfully applied in breast cancer. In 2014, Mazurowski et al.<ref>((cite journal | doi= 10.1148/radiol.14132641 |title=Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging  |year=2014 |last1=Mazurowski |first1=M. A. |last2=Zhang |first2=J. |last3=Grimm |first3=L. J. |last4=Yoon |first4=S. C. |last5=Silber |first5=J. I. |journal=Radiology |volume=273 |issue=2 |pages=365–72|pmid=25028781|doi-access=free ))</ref> showed that enhancement dynamics in MRI, computed using computer vision algorithms, are associated with gene expression-based tumor molecular subtype in breast cancer patients.

Programs that study the connections between radiology and genomics are active at the [[University of Pennsylvania]], [[UCLA]], [[MD Anderson Cancer Center]], [[Stanford University]] and at [[Baylor College of Medicine]]  in Houston, Texas.

=== Multiparametric radiomics ===
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.<ref name="Parekh-2018">((cite journal|last1=Parekh|first1=Vishwa S.|last2=Jacobs|first2=Michael A. | name-list-style = vanc |date=2018-09-25|title=MPRAD: A Multiparametric Radiomics Framework|journal=Breast Cancer Research and Treatment|volume=180|issue=2|pages=407–421|arxiv=1809.09973|doi=10.1007/s10549-020-05533-5|pmid=32020435|pmc=7066290|bibcode=2018arXiv180909973P))</ref>  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 ====
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.<ref name="Parekh-2018" />

==== Stroke ====
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.<ref name="Parekh-2018" /> 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 ==
* [[Medical image computing]]
* [[Computational anatomy]]
* [[Artificial intelligence in healthcare]]
* [[omics]]

== References ==
((reflist))

[[Category:Computing in medical imaging]]

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