The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Open accessAbstract
Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head & Neck (H&N) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H&N cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H&N RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 ± 0.01, Prognosis H&N CI = 0.68 ± 0.01; Lung histology AUC = 0.56 ± 0.03, Lung stage AUC = 0.61 ± 0.01, H&N HPV AUC = 0.58 ± 0.03, H&N stage AUC = 0.77 ± 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.
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.
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
Textural 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
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Harrell, F. E. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,195
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Self-citationConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking
Wilkerson, M. D., & Hayes, D. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq170 · Cited by 5,669
Decoding 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-citationA Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
Hand, D. J., & Till, R. J. (2001) · Machine Learning · doi:10.1023/a:1010920819831 · Cited by 2,276
Radiomics: the process and the challenges
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., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258
Self-citationTexture analysis using gray level run lengths
Galloway, M. M. (1975) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(75)80008-6 · Cited by 2,141
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116
CERR: A computational environment for radiotherapy research
Deasy, J. O., Blanco, A. I., & Clark, V. H. (2003) · Medical Physics · doi:10.1118/1.1568978 · Cited by 872
Tixier, F., Le Rest, C. C., Hatt, M., Albarghach, N., Pradier, O., Metges, J.-P., Corcos, L., & Visvikis, D. (2011) · Journal of Nuclear Medicine · doi:10.2967/jnumed.110.082404 · Cited by 696
The VGAM Package for Categorical Data Analysis
Yee, T. W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v032.i10 · Cited by 651
Decoding global gene expression programs in liver cancer by noninvasive imaging
Segal, E., Sirlin, C. B., Ooi, C., Adler, A. S., Gollub, J., Chen, X., Chan, B. K., Matcuk, G. R., Barry, C. T., Chang, H. Y., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1306 · Cited by 629
Ganeshan, B., Panayiotou, E., Burnand, K., Dizdarevic, S., & Miles, K. (2012) · European Radiology · doi:10.1007/s00330-011-2319-8 · Cited by 505
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes
El Naqa, I., Grigsby, P. W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., Chaudhari, S., Yang, D., Schmitt, M., Laforest, R., et al. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2008.08.011 · Cited by 480
survcomp: an R/Bioconductor package for performance assessment and comparison of survival models
Schröder, M. S., Culhane, A. C., Quackenbush, J., & Haibe-Kains, B. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr511 · Cited by 470
Self-citationNon–Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT
Ganeshan, B., Goh, V., Mandeville, H. C., Ng, Q. S., Hoskin, P. J., & Miles, K. A. (2013) · Radiology · doi:10.1148/radiol.12112428 · Cited by 447
Predicting outcomes in radiation oncology—multifactorial decision support systems
Lambin, P., van Stiphout, R. G. P. M., Starmans, M. H. W., Rios-Velazquez, E., Nalbantov, G., Aerts, H. J. W. L., Roelofs, E., van Elmpt, W., Boutros, P. C., Granone, P., et al. (2013) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2012.196 · Cited by 402
Self-citationAerts, 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/ncomms5644 · Cited by 352
Self-citationGaneshan, B., Abaleke, S., Young, R. C. D., Chatwin, C. R., & Miles, K. A. (2010) · Cancer Imaging · doi:10.1102/1470-7330.2010.0021 · Cited by 324
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images
Balagurunathan, Y., Gu, Y., Wang, H., Kumar, V., Grove, O., Hawkins, S., Kim, J., Goldgof, D. B., Hall, L. O., Gatenby, R. A., et al. (2014) · Translational Oncology · doi:10.1593/tlo.13844 · Cited by 306
Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme
Zinn, P. O., Majadan, B., Sathyan, P., Singh, S. K., Majumder, S., Jolesz, F. A., & Colen, R. R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025451 · Cited by 265
Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer
Vaidya, M., Creach, K. M., Frye, J., Dehdashti, F., Bradley, J. D., & El Naqa, I. (2012) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2011.10.014 · Cited by 201
Buckler, A. J., Bresolin, L., Dunnick, N. R., Sullivan, D. C., & For the Group (2011) · Radiology · doi:10.1148/radiol.10100799 · Cited by 175
Nicolas‐Jilwan, M., Hu, Y., Yan, C., Meerzaman, D. M., Holder, C. A., Gutman, D. A., Jain, R., Colen, R. R., Rubin, D. L., Zinn, P. O., et al. (2014) · Journal of Neuroradiology · doi:10.1016/j.neurad.2014.02.006 · Cited by 133
Zhao, B., Oxnard, G. R., Moskowitz, C. S., Kris, M. G., Pao, W. J., Guo, P., Rusch, V. W., Ladanyi, M., Rizvi, N. A., & Schwartz, L. H. (2010) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-0125 · Cited by 112
Promise and pitfalls of quantitative imaging in oncology clinical trials
Kurland, B. F., Gerstner, E. R., Mountz, J. M., Schwartz, L. H., Ryan, C. W., Graham, M. M., Buatti, J. M., Fennessy, F. M., Eikman, E. A., Kumar, V., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.009 · Cited by 101
Quantitative Imaging Test Approval and Biomarker Qualification: Interrelated but Distinct Activities
Buckler, A. J., Bresolin, L. B., Dunnick, N. R., Sullivan, D. C., & For the Group (2011) · Radiology · doi:10.1148/radiol.10100800 · Cited by 94
Birchard, K. R., Hoang, J. K., Herndon, J. E., & Patz, E. F. (2009) · Cancer · doi:10.1002/cncr.24060 · Cited by 76
Genomics and proteomics: Emerging technologies in clinical cancer research
Chung, C. H., Levy, S., Chaurand, P., & Carbone, D. P. (2006) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2006.06.005 · Cited by 67
Cited by
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., Paragios, N., Deutsch, E., & Ferté, C. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx034 · Cited by 757
The 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-citationBraman, N. M., Etesami, M., Prasanna, P., Dubchuk, C., Gilmore, H., Tiwari, P., Plecha, D., & Madabhushi, A. (2017) · Breast Cancer Research · doi:10.1186/s13058-017-0846-1 · Cited by 632
Berenguer, R., Pastor-Juan, M. D. R., Canales-Vázquez, J., Castro-García, M., Villas, M. V., Mansilla Legorburo, F., & Sabater, S. (2018) · Radiology · doi:10.1148/radiol.2018172361 · Cited by 622
Metadata
- Authors
- Parmar, C., Leijenaar, R. T. H., Grossmann, P., Rios Velazquez, E., Bussink, J., Rietveld, D., Rietbergen, M. M., Haibe-Kains, B., Lambin, P., & Aerts, H. J. W. L.
- Venue
- Scientific Reports
- Published
- 2015-06-05
- Volume
- 5
- Issue
- 1
- Pages
- 11044-11044
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2253150690
- Cited by
- 434
- References
- 31
- FWCI
- 35.97
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
