The DOI matches its Crossref record; 99 of the 104 references it cites were found in a registry or an index.
Radiomics: the process and the challenges
Open accessAbstract
"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The first such study used 182 texture features in combination with 22 semantic features to describe CT images of lung cancer (24).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 1×
“This multiple testing problem has been addressed in statistics in many ways; however, the most familiar, and conservative, Bonferroni corrections severely limit the power of the test in the 50,000-test experiments [81].”
BackgroundTextural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · cited 1×
“Second-order statistics or co-occurrence matrix features can be used for texture classification [64–66] and are widely applied in medical pattern recognition tasks [67–71].”
MethodBootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
Osher, S., & Sethian, J. A. (1988) · Journal of Computational Physics · doi:10.1016/0021-9991(88)90002-2 · Cited by 14,196 · cited 1×
“The level set method was initially proposed by Osher and Sethian in 1988 to track moving interfaces, and it was subsequently applied across various imaging applications in the late 1990s [38].”
MethodGolub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516 · cited 1×
“The field of high-dimensional biomarker discovery and validation has evolved rapidly over the past decade since some of the earliest microarray-based results were reported [80].”
BackgroundIn silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Fundamentals of digital image processing
Jain, A. K. (1988) · Cited by 7,000
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Flagged first, then by citations
5 of the 104 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Jackson E, Ashton E, Evelhoch J, Buonocore M, Karczmar G, Rosen M, et al. Multivendor, multisite DCE-MRI phantom validation study. 2009. In: Proceedings of the 95th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA '10); December 2009; Chicago, IL, USA.
Cited by
Sorted by citations
Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421
Self-citationRadiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
Self-citationDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
Self-citationZwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177
“5 iv Chapter 1 On this document While analysis of medical images has practically taken place since the first image was recorded, high throughput analysis of medical images is a more recent phenomenon (Kumar et al., 2012; Lambin et al., 2012; Gillies et al., 2015). The aim of such a radiomics process is to provide decis…”
BackgroundSelf-citationArtificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
Self-citationBakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
“A subset of the pre-operative scans included in the generated data [Data Citation 3 and Data Citation 4] was also part of the BraTS ’ 15 dataset (Table 4 (available online only)), which were skull-stripped, co-registered to the same anatomical template and resampled to 1 mm 3 voxel resolution by the challenge organize…”
InfluentialMayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., & Cook, G. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.222893 · Cited by 1,777
“, interpatient variability) are preferable (39).”
Radiomics: the facts and the challenges of image analysis
Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Fanciullo, C., MORGANTI, A. G., & Bellomi, M. (2018) · European Radiology Experimental · doi:10.1186/s41747-018-0068-z · Cited by 1,048
“4) and not always feasible for radiomics ’ analysis, requiring very large data sets [46].”
BackgroundLiu, Z., Wang, S., Dong, D., Wei, J., Fang, C., Zhou, X., Sun, K., Li, L., Li, B., Wang, M., et al. (2019) · Theranostics · doi:10.7150/thno.30309 · Cited by 983
“The concept of radiomics was first proposed in 2012 [1, 2], and has since attracted the attention of researchers throughout the world (Figure 1).”
BackgroundMethodRepeatability and Reproducibility of Radiomic Features: A Systematic Review
Traverso, A., Wee, L., Dekker, A. L. A. J., & Gillies, R. J. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.05.053 · Cited by 966
““Reproducibility” refers to features that remain the same when imaged using different equipment, different software, different image acquisition settings, or different operators (eg, other clinics), be that in the same subject or in different subjects (14, 20).”
Self-citationVallières, M., Freeman, C. R., Skamene, S., & Naqa, I. E. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/14/5471 · Cited by 892
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819
Limkin, E. J., Sun, R., Dercle, L., Zacharaki, E. I., Robert, C., Reuzé, S., Schernberg, A., Nikos Paragios, A., Deutsch, É., & Ferté, C. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx034 · Cited by 756
Huang, Y., Liu, Z., He, L., Chen, X., Pan, D., Ma, Z., Liang, C., Tian, J., & Liang, C. (2016) · Radiology · doi:10.1148/radiol.2016152234 · Cited by 741
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.02.015 · Cited by 695
“Radiomics [7–9] aims to provide a comprehensive quantification of the tumor phenotype by analyzing robustly [10–12] a large set of quantitative data characterization algorithms.”
Self-citationThe Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review
Aerts, H. J. W. L. (2016) · JAMA Oncology · doi:10.1001/jamaoncol.2016.2631 · Cited by 669
Self-citationNon‐small cell lung cancer in China
Chen, P., Liu, Y., Wen, Y., & Zhou, C. (2022) · 癌症：英文版 · doi:10.1002/cac2.12359 · Cited by 682
“Radiomics is a novel technology developed in recent years that might improve the specificity and accuracy of lung cancer diagnosis [114].”
BackgroundMeasuring Computed Tomography Scanner Variability of Radiomics Features
Mackin, D., Fave, X. J., Zhang, L., Fried, D., Yang, J., Taylor, B. A., Rodriguez-Rivera, E., Dodge, C. T., Jones, A. K., & Court, L. E. (2015) · Investigative Radiology · doi:10.1097/rli.0000000000000180 · Cited by 649
Berenguer, R., Pastor-Juan, M. D. R., Canales‐Vázquez, J., Castro‐García, M., Villas, M. V., Legorburo, F. M., & Sabater, S. (2018) · Radiology · doi:10.1148/radiol.2018172361 · Cited by 622
Machine and deep learning methods for radiomics
Avanzo, M., Wei, L., Stancanello, J., Vallières, M., Rao, A. U. K., Morin, O., Mattonen, S. A., & Naqa, I. E. (2020) · Medical Physics · doi:10.1002/mp.13678 · Cited by 641
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Parmar, C., Velazquez, E. R., Leijenaar, R. T. H., Jermoumi, M., Carvalho, S., Mak, R. H., Mitra, S., Shankar, B. U., Kikinis, R., Haibe‐Kains, B., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102107 · Cited by 611
“Radiomics addresses this issue, and refers to the high throughput extraction of a large number of quantitative and minable imaging features, assuming that these features convey prognostic and predictive information [3,4].”
BackgroundMethodInfluentialSelf-citationSurgical data science for next-generation interventions
Maier‐Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feußner, H., Forestier, G., Giannarou, S., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0132-7 · Cited by 655
Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels
Shafiq‐ul‐Hassan, M., Zhang, G. G., Latifi, K., Ullah, G., Hunt, D., Balagurunathan, Y., Abdalah, M. A., Schabath, M. B., Goldgof, D. B., Mackin, D., et al. (2017) · Medical Physics · doi:10.1002/mp.12123 · Cited by 587
Self-citationFrom big data analysis to personalized medicine for all: challenges and opportunities
Alyass, A., Turcotte, M., & Meyre, D. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0108-y · Cited by 575
“integrate, analyze, and interpret omics data [10–12].”
Liu, Z., Zhang, X.-Y., Shi, Y., Wang, L., Zhu, H., Tang, Z., Wang, S., Li, X.-T., Tian, J., & Sun, Y. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-1038 · Cited by 548
“Radiomics, which is based on advanced pattern recognition tools, involves the extraction of a large number of quantitative features from digital images to determine relationships between such features and the underlying pathophysiology (17, 18).”
BackgroundApplications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Vallières, M., Kay‐Rivest, E., Perrin, L. J., Liem, X., Furstoss, C., Aerts, H. J. W. L., Khaouam, N., Nguyen‐Tan, P. F., Wang, C.-S., Sultanem, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10371-5 · Cited by 569
Self-citationReproducibility of radiomics for deciphering tumor phenotype with imaging
Zhao, B., Tan, Y., Tsai, W., Qi, J., Xie, C., Lü, L., & Schwartz, L. H. (2016) · Scientific Reports · doi:10.1038/srep23428 · Cited by 509
Beyond imaging: The promise of radiomics
Avanzo, M., Stancanello, J., & Naqa, I. E. (2017) · Physica Medica · doi:10.1016/j.ejmp.2017.05.071 · Cited by 499
“The fundamental hypothesis of radiomics is that quantitative analysis of tumor through a large amount of radiomic features can provide valuable diagnostic, prognostic or predictive information [19,20].”
BackgroundMethodInfluentialRadiomics and radiogenomics in lung cancer: A review for the clinician
Thawani, R., McLane, M. J., Beig, N. G., Ghose, S., Prasanna, P., Velcheti, V., & Madabhushi, A. (2017) · Lung Cancer · doi:10.1016/j.lungcan.2017.10.015 · Cited by 459
Val‐Laillet, D., Aarts, E., Weber, B., Ferrari, M., Quaresima, V., Stoeckel, L. E., Alonso‐Alonso, M., Audette, M. A., Malbert, C., & Stice, E. M. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.03.016 · Cited by 466
Scapicchio, C., Gabelloni, M., Barucci, A., Cioni, D., Saba, L., & Neri, E. (2021) · La radiologia medica · doi:10.1007/s11547-021-01389-x · Cited by 435
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Parmar, C., Leijenaar, R. T. H., Großmann, P., Velazquez, E. R., Bussink, J., Rietveld, D. H. F., Rietbergen, M. M., Haibe‐Kains, B., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep11044 · Cited by 433
Self-citationLeijenaar, R. T. H., Carvalho, S., Velazquez, E. R., van Elmpt, W., Parmar, C., Hoekstra, O. S., Hoekstra, C. J., Boellaard, R., Dekker, A. L. A. J., Gillies, R. J., et al. (2013) · Acta Oncologica · doi:10.3109/0284186x.2013.812798 · Cited by 428
Self-citationZhou, M., Scott, J. G., Chaudhury, B., Hall, L., Goldgof, D. B., Yeom, K. W., Iv, M., Ou, Y., Kalpathy–Cramer, J., Napel, S. A., et al. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5391 · Cited by 425
“to deepen our understanding of imaging characteristics in cancer.(1,2) The identified quantitative features can be used to alert”
BackgroundSelf-citationRadiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Parmar, C., Großmann, P., Rietveld, D. H. F., Rietbergen, M. M., Lambin, P., & Aerts, H. J. W. L. (2015) · Frontiers in Oncology · doi:10.3389/fonc.2015.00272 · Cited by 410
“Predictive and prognostic models are an important part of radiomics (27).”
BackgroundSelf-citationBiobanking in health care: evolution and future directions
Coppola, L., Cianflone, A., Grimaldi, A., Incoronato, M., Bevilacqua, P., Messina, F., Baselice, S., Soricelli, A., Mirabelli, P., & Salvatore, M. (2019) · Journal of Translational Medicine · doi:10.1186/s12967-019-1922-3 · Cited by 429
Park, J. E., Park, S. Y., Kim, H. J., & Kim, H. S. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0070 · Cited by 388
“The signal intensities on MRI result from a complex interplay of tissue relaxation time and imaging acquisition (9), with information on MRI not based solely on tissue properties.”
BackgroundMethodInfluentialRadiomics in Oncology: A Practical Guide
Shur, J. D., Doran, S., Kumar, S., Dafydd, D. A., Downey, K., O’Connor, J. P. B., Papanikolaou, N. K., Messiou, C., Koh, D., & Orton, M. R. (2021) · Radiographics · doi:10.1148/rg.2021210037 · Cited by 388
Defining the biological basis of radiomic phenotypes in lung cancer
Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife · doi:10.7554/elife.23421 · Cited by 365
“…quantitative imaging features that objectively define tumor intensity, shape, size, and texture (Gillies et al., 2016; Aerts, 2016; Lambin et al., 2012; Kumar et al., 2012) in a robust and reproducible way (Zhao et al., 2016; Fried et al., 2014; Balagurunathan et al., 2014; Leijenaar et al., 2013).”
BackgroundSelf-citationPrinciples and methods of integrative genomic analyses in cancer
Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K. M., Frigessi, A., & Børresen‐Dale, A. (2014) · Nature reviews. Cancer · doi:10.1038/nrc3721 · Cited by 376
Radiomics: a new application from established techniques
Parekh, V. S., & Jacobs, M. A. (2016) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2016.1164013 · Cited by 391
Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J. D., Snyder, A., Weigelt, B., & Vargas, H. A. (2016) · Clinical Radiology · doi:10.1016/j.crad.2016.09.013 · Cited by 369
“First, it requires standardisation of imaging protocols, including image acquisition and post-processing, as well as robust segmentation algorithms that require minimal operator input (13).”
BackgroundMethodInfluentialLarue, R. T. H. M., Defraene, G., Ruysscher, D. D., Lambin, P., & van Elmpt, W. (2016) · British Journal of Radiology · doi:10.1259/bjr.20160665 · Cited by 361
“lung lobe, distance to the organ at risk) have been used.(72)”
BackgroundInfluentialSelf-citationDelta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer
Fave, X. J., Zhang, L., Yang, J., Mackin, D., Balter, P. A., Gomez, D. R., Followill, D. S., Jones, A. K., Stingo, F. C., Liao, Z. X., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-00665-z · Cited by 353
A review in radiomics: Making personalized medicine a reality via routine imaging
Guiot, J., Vaidyanathan, A., Deprez, L., Zerka, F., Danthine, D., Frix, A.-N., Lambin, P., Bottari, F., Tsoutzidis, N., Miraglio, B., et al. (2021) · Medicinal Research Reviews · doi:10.1002/med.21846 · Cited by 356
“The hypothesis is that quantitative analysis of medical image data can provide complementary information to aid physicians in the decision‐making process, aided by automatic or semiautomatic software, in a fast and reproducible way.(5) Radiomics is the result of several decades of computer‐aided diagnosis, prognosis,…”
BackgroundSelf-citationFrom Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
Afshar, P., Mohammadi, A., Plataniotis, K. N., Oikonomou, A., & Benali, H. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2900993 · Cited by 334
“GLRLM A matrix that presents the length of consecutive pixels having the same intensity [62] Short-run emphasis; long-run emphasis; gray-level nonuniformity; run-length nonuniformity; run percentage; low gray-level run emphasis; high gray-level run emphasis [2]”
BackgroundMethodInfluentialLi, Z., Wang, Y., Yu, J., Guo, Y., & Cao, W. (2017) · Scientific Reports · doi:10.1038/s41598-017-05848-2 · Cited by 346
“Normally, the process of radiomics analysis includes image acquisition, image segmentation, feature extraction, feature selection and informatics analyses 3 .”
MethodRadiomic phenotype features predict pathological response in non-small cell lung cancer
Coroller, T. P., Agrawal, V., Narayan, V., Hou, Y., Großmann, P., Lee, S. W., Mak, R. H., & Aerts, H. J. W. L. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.04.004 · Cited by 327
Self-citationRadiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas
Liu, Y., Kim, J., Balagurunathan, Y., Li, Q., García, A. L., Stringfield, O., Ye, Z., & Gillies, R. J. (2016) · Clinical Lung Cancer · doi:10.1016/j.cllc.2016.02.001 · Cited by 314
Self-citationibex : An open infrastructure software platform to facilitate collaborative work in radiomics
Zhang, L., Fried, D., Fave, X. J., Hunter, L. A., Yang, J., & Court, L. E. (2015) · Medical Physics · doi:10.1118/1.4908210 · Cited by 316
Radiogenomics: What It Is and Why It Is Important
Mazurowski, M. A. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.04.019 · Cited by 289
“Finally, ‘ radiogenomics’ has been equated with another approach called ‘ radiomics’ [5], [6], [7].”
BackgroundFeAture Explorer (FAE): A tool for developing and comparing radiomics models
Song, Y. S., Zhang, J., Zhang, Y., Hou, Y., Yan, X., Wang, Y., Zhou, M., Yao, Y., & Yang, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237587 · Cited by 289
“Radiomics extracts a large number of quantitative features from the regions of interest (ROIs) in medical images and maps them to clinical conclusions with machine learning (ML) [1, 2].”
BackgroundPrecision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment
Pinker, K., Chin, J., Melsaether, A. N., Morris, E. A., & Moy, L. (2018) · Radiology · doi:10.1148/radiol.2018172171 · Cited by 275
Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer
Zhang, Y., Oikonomou, A., Wong, A. M., Haider, M. A., & Khalvati, F. (2017) · Scientific Reports · doi:10.1038/srep46349 · Cited by 272
Feng, X., Tustison, N. J., Patel, S. H., & Meyer, C. H. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00025 · Cited by 268
“…segmentation of the tumor region has shown advantages in image-based tumor phenotyping over traditionally used clinical measures such as largest anterior-posterior, transverse, and inferior-superior tumor dimensions on a subjectively-chosen slice (Kumar et al., 2012; Gillies et al., 2016).”
MethodPeerlings, J., Woodruff, H. C., Winfield, J. M., Ibrahim, A., BEERS, B. E. V., Heerschap, A., Jackson, A., Wildberger, J. E., Mottaghy, F. M., deSouza, N. M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-41344-5 · Cited by 267
Self-citationGray-level invariant Haralick texture features
Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., & Garpebring, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212110 · Cited by 264
Assessing robustness of radiomic features by image perturbation
Zwanenburg, A., Leger, S., Agolli, L., Pilz, K., Troost, E. G. C., Richter, C., & Löck, S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36938-4 · Cited by 258
“4 All intensity values outside range r are replaced by the nearest valid intensity: I j = 8 >> < >> : r 1; I j<r 1 r 2; I >r I j; otherwise (b) Intensities are then mapped to the [0;1] range by a simple transformation: I j;s= I j r 1 r 2 r 1 3. The number of supervoxels is estimated so that on ave…”
BackgroundMethodInfluentialHe, L., Huang, Y., Ma, Z., Liang, C., Liang, C., & Liu, Z. (2016) · Scientific Reports · doi:10.1038/srep34921 · Cited by 253
Image-Based Cardiac Diagnosis With Machine Learning: A Review
Martín-Isla, C., Campello, V. M., Izquierdo, C., Raisi‐Estabragh, Z., Baeßler, B., Petersen, S. E., & Lekadir, K. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.00001 · Cited by 250
“Introduced in 2012 (11, 12), the radiomics paradigm was, for a long time, mostly exploited in oncology (13).”
BackgroundRadiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma
Zhang, B., He, X. J., Ouyang, F., Gu, D., Dong, Y., Zhang, L., Mo, X., Huang, W., Tian, J., & Zhang, S. (2017) · Cancer Letters · doi:10.1016/j.canlet.2017.06.004 · Cited by 246
Deep learning and radiomics in precision medicine
Parekh, V. S., & Jacobs, M. A. (2019) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2019.1585805 · Cited by 255
“In radiology, Radiomics are textural mathematical constructs that capture the spatial appearance of the tissue of interest (shape and texture) on different types of images using texture [10–13 , 50– 53].”
BackgroundMethodTexture Analysis of Imaging: What Radiologists Need to Know
Varghese, B. A., Cen, S., Hwang, D., & Duddalwar, V. A. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20624 · Cited by 253
“, their value remains the same when repeated multiple times on a single scanner]) need to be identified [42, 43] (Fig.”
BackgroundPark, H., Lim, Y., Ko, E. S., Cho, H.-H., Lee, J. E., Han, B., Ko, E. Y., Choi, J. S., & Park, K. W. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3783 · Cited by 243
“Other pathologic diagnoses in the 17 patients were as follows:mucinous carcinoma (7), medullary carcinoma (2), invasive micropapillary carcinoma (3), tubular carcinoma (1), invasive apocrine carcinoma (3), and adenoid cystic carcinoma (1).”
BackgroundVolumetric CT-based segmentation of NSCLC using 3D-Slicer
Velazquez, E. R., Parmar, C., Jermoumi, M., Mak, R. H., van Baardwijk, A. A. W., Fennessy, F. M., Lewis, J. H., Ruysscher, D. D., Kikinis, R., Lambin, P., et al. (2013) · Scientific Reports · doi:10.1038/srep03529 · Cited by 240
Self-citationRepeatability of Multiparametric Prostate MRI Radiomics Features
Schwier, M., van Griethuysen, J. J. M., Vangel, M. G., Pieper, S., Peled, S., Tempany, C. M., Aerts, H. J. W. L., Kikinis, R., Fennessy, F. M., & Fedorov, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-45766-z · Cited by 232
“Since there is no systematic difference between the first and second scans, we model both between-subject and within-subject sources of variability as independent random effects and ICC(1,1) is therefore appropriate.”
MethodSelf-citationCriteria for the translation of radiomics into clinically useful tests
Huang, E. P., O’Connor, J. P. B., McShane, L. M., Giger, M. L., Lambin, P., Kinahan, P. E., Siegel, E. L., & Shankar, L. K. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00707-0 · Cited by 246
Self-citationVirtual clinical trials in medical imaging: a review
Abadi, E., Segars, W. P., Tsui, B. M. W., Kinahan, P. E., Bottenus, N. B., Frangi, A. F., Maidment, A. D. A., Lo, J. Y.-C., & Samei, E. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.4.042805 · Cited by 230
“267 – 269.”
Bibault, J., Giraud, P., Housset, M., Durdux, C., Taı̈eb, J., Berger, A., Coriat, R., Chaussade, S., Dousset, B. E., Nordlinger, B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30657-6 · Cited by 229
Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats
Napel, S. A., Mu, W., Jardim‐Perassi, B. V., Aerts, H. J. W. L., & Gillies, R. J. (2018) · Cancer · doi:10.1002/cncr.31630 · Cited by 232
Self-citationThe crucial role of multiomic approach in cancer research and clinically relevant outcomes
Lu, M., & Zhan, X. (2018) · The EPMA Journal · doi:10.1007/s13167-018-0128-8 · Cited by 235
“This type of information is crucial for the diagnosis of tumors, but it does not accurately reflect the morphological and behavioral complexities of a tumor, with limited benefits in the judgment of treatment sensitivity and prognosis [255].”
BackgroundPapadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226
“Shape features [19]: provide quantitative description of geometric properties of the ROIs/VOIs, such as surface area, total volume, diameter, sphericity or surface-to-volume ratio.”
BackgroundCunliffe, A. R., Armato, S. G., Castillo, R., Pham, N., Guerrero, T. M., & Al‐Hallaq, H. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2014.11.030 · Cited by 225
“0-10 Gy 503 (165-776) 532 (300-936) 10-30 Gy 162 (17-398) 118 (22-294) 30 Gy 49 (1-154) 27 (0-90)”
Feng, Z., Rong, P., Cao, P., Zhou, Q. S., Zhu, W., Yan, Z., Liu, Q., & Wang, W. (2017) · European Radiology · doi:10.1007/s00330-017-5118-z · Cited by 230
Kim, J. Y., Park, J. E., Jo, Y., Shim, W. H., Nam, S. J., Kim, J. H., Yoo, R., Choi, S. H., & Kim, H. S. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy133 · Cited by 222
Sun, W., Zheng, B., & Qian, W. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.04.006 · Cited by 227
Leijenaar, R. T. H., Carvalho, S., Hoebers, F. J. P., Aerts, H. J. W. L., van Elmpt, W., Huang, S. H., Chan, H. B., Waldron, J. N., O’Sullivan, B., & Lambin, P. (2015) · Acta Oncologica · doi:10.3109/0284186x.2015.1061214 · Cited by 216
Self-citationIntegrative methods for analyzing big data in precision medicine
Gligorijević, V., Malod‐Dognin, N., & Pržulj, N. (2015) · PROTEOMICS · doi:10.1002/pmic.201500396 · Cited by 226
“Even medical imaging, which was traditionally manually investigated, is now a subject of high-throughput capturing [14, 15].”
BackgroundCT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer
Huynh, E., Coroller, T. P., Narayan, V., Agrawal, V., Hou, Y., Romano, J., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.05.024 · Cited by 215
Self-citationVoxel size and gray level normalization of CT radiomic features in lung cancer
Shafiq-ul-Hassan, M., Latifi, K., Zhang, G. G., Ullah, G., Gillies, R. J., & Moros, E. G. (2018) · Scientific Reports · doi:10.1038/s41598-018-28895-9 · Cited by 215
Self-citationRadiomics and radiogenomics in gliomas: a contemporary update
Singh, G., Manjila, S. V., Sakla, N. M., True, A., Wardeh, A. H., Beig, N. G., Vaysberg, A., Matthews, J., Prasanna, P., & Spektor, V. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01387-w · Cited by 226
Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
Aerts, H. J. W. L., Großmann, P., Tan, Y., Oxnard, G. R., Rizvi, N. A., Schwartz, L. H., & Zhao, B. (2016) · Scientific Reports · doi:10.1038/srep33860 · Cited by 213
Self-citationZhu, X., Dong, D., Chen, Z., Fang, M., Zhang, L., Song, J., Yu, D., Zang, Y., Liu, Z., Shi, J., et al. (2018) · European Radiology · doi:10.1007/s00330-017-5221-1 · Cited by 211
Mali, S. A., Ibrahim, A., Woodruff, H. C., Andrearczyk, V., Müller, H., Primakov, S. P., Salahuddin, Z., Chatterjee, A., & Lambin, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090842 · Cited by 228
“The radiomic hypothesis postulates that the quantitative study of medical image data can provide complementary knowledge in a quick and reproducible manner to support clinicians in their decision-making process, assisted by automated or semi-automated software [5,6].”
BackgroundSelf-citationBladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning
Kenny, H., Hadjiiski, L. M., Chan, H., Weizer, A. Z., Alva, A. S., Cohan, R. H., Caoili, E. M., Paramagul, C., & Samala, R. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-09315-w · Cited by 208
Vial, A., Stirling, D. A., Field, M., Ros, M., Ritz, C. H., Carolan, M., Holloway, L. C., & Miller, A. (2018) · Translational Cancer Research · doi:10.21037/tcr.2018.05.02 · Cited by 207
“It has been found that texture features are the one of the most important imaging features for the field of radiomics (1-5, Kumar et al. (36) describe that there are a vast number of extractible radiomic features for each patient analysed, hence the quantity of features needs to be reduced to avoid…”
BackgroundInfluentialShiri, I., Rahmim, A., Ghafarian, P., Geramifar, P., Abdollahi, H., & Bitarafan‐Rajabi, A. (2017) · European Radiology · doi:10.1007/s00330-017-4859-z · Cited by 205
Da‐ano, R., Masson, I., Lucia, F., Dore, M., Robin, P., Alfieri, J., Rousseau, C., Mervoyer, A., Reinhold, C., Castelli, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66110-w · Cited by 211
Neisius, U., El-Rewaidy, H., Nakamori, S., Rodriguez, J., Manning, W. J., & Nezafat, R. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.11.024 · Cited by 196
“The use of quantitative mapping data compared with qualitative CMR improved standardization and reflection of physical tissue properties (37).”
Zhang, Y., Zhang, X., Cheng, Y., Li, B., Teng, X.-Z., Zhang, J., Lam, S., Zhou, T., Ma, Z., Sheng, J., et al. (2023) · Military Medical Research · doi:10.1186/s40779-023-00458-8 · Cited by 212
“The reproducibility of segmentation may be more important than its Acc, as was noted by Kumar et al. [188].”
BackgroundFeng, S., Jia, Y., Liao, B., Huang, B., Zhou, Q., Li, X., Wei, K., Chen, L., Li, B., Wang, W., et al. (2019) · European Radiology · doi:10.1007/s00330-018-5935-8 · Cited by 199
Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology
Forghani, R., Savadjiev, P., Chatterjee, A., Muthukrishnan, N., Reinhold, C., & Forghani, B. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.07.001 · Cited by 206
“The fi rst published occurrence of the term radiomics dates back to 2012 [27,28].”
BackgroundChu, L. C., Park, S., Kawamoto, S., Fouladi, D. F., Shayesteh, S., Zinreich, E. S., Graves, J. S., Horton, K. M., Hruban, R. H., Yuille, A. L., et al. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20901 · Cited by 193
Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis
Thurzo, A., Urbanová, W., Novák, B., Czakó, L., Siebert, T., Stano, P., Mareková, S., Fountoulaki, G., Kosnáčová, H., & Varga, I. (2022) · Healthcare · doi:10.3390/healthcare10071269 · Cited by 221
Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma
Reuben, A., Spencer, C. N., Prieto, P. A., Gopalakrishnan, V., Reddy, S. M., Miller, J. P., Mao, X., De Macedo, M. P., Chen, J., Song, X., et al. (2017) · npj Genomic Medicine · doi:10.1038/s41525-017-0013-8 · Cited by 166
“16 The concept of textural analysis was first introduced in 1973 by Haralick and colleagues.”
Test–Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific?
van Timmeren, J. E., Leijenaar, R. T. H., van Elmpt, W., Wang, J., Zhang, Z., Dekker, A. L. A. J., & Lambin, P. (2016) · Tomography · doi:10.18383/j.tom.2016.00208 · Cited by 191
“We investigated the test–retest stability on both data sets of a total of 542 radiomic features, divided into the following 4 groups: (1) Tumor intensity (n 15), (2) shape (n 11), (3) texture (n 44), and (4) wavelet (n 472).”
MethodSelf-citationAssessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules
Massion, P. P., Antic, S., Ather, S., Arteta, C., Brabec, J., Chen, H., Declerck, J., Dufek, D., Hickes, W., Kadir, T., et al. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201903-0505oc · Cited by 190
How to develop a meaningful radiomic signature for clinical use in oncologic patients
Papanikolaou, N. K., Matos, C., & Koh, D. (2020) · Cancer Imaging · doi:10.1186/s40644-020-00311-4 · Cited by 186
Metadata
- Authors
- Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., Goldgof, D. B., Hall, L. O., Lambin, P., Balagurunathan, Y., Gatenby, R. A., & Gillies, R. J.
- Affiliations
- Moffitt Cancer Center (US)
- University of South Florida (US)
- Harvard University (US)
- Maastricht University Medical Centre (NL)
- Corresponding author
- Gillies, Robert J.
- ORCID
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 30
- Issue
- 9
- Pages
- 1234-1248
- ISSN
- 0730-725X (ISSN-L)
- PMC
- PMC3563280
- OpenAlex
- W2097475056
- Cited by
- 2,258
- References
- 104
- Citations by year
- 2026: 106 · 2025: 189 · 2024: 224 · 2023: 236 · 2022: 277 · 2021: 290 · 2020: 253 · 2019: 209 · 2018: 190 · 2017: 127 · 2016: 86 · 2015: 43 · 2014: 16 · 2013: 6 · 2012: 3
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Radiological Society of North America
- National Cancer Institute (U01CA143062, P30CA076292)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, quantitative imaging features, image segmentation, feature extraction, data sharing, non-small cell lung cancer, computed tomography, positron emission tomography, prediction model
- MeSH terms
- Algorithms, Carcinoma, Non-Small-Cell Lung, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Medical Informatics, Pattern Recognition, Automated, Risk Factors, Software, Tomography, X-Ray Computed, Reproducibility of Results, Multivariate Analysis, Radiation Oncology, Phantoms, Imaging, Positron-Emission Tomography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
