Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment

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 · see citing works

Initial sequencing and analysis of the human genome

International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216 · see citing works

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 · see citing works

Molecular portraits of human breast tumours

Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551 · see citing works

Comprehensive molecular portraits of human breast tumours

The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129 · see citing works

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 · see citing works

The Sequence of the Human Genome

Venter, J., Adams, M., Myers, E., Li, P. W., Mural, R., Sutton, G., Smith, H., Yandell, M., Evans, C., Holt, R., et al. (2001) · Science · doi:10.1373/clinchem.2014.237016 · Cited by 6,779 · see citing works

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 · see citing works

Trastuzumab plus Adjuvant Chemotherapy for Operable HER2-Positive Breast Cancer

Romond, E. H., Perez, E. A., Bryant, J., Suman, V. J., Geyer, C. E., Davidson, N. E., Tan-Chiu, E., Martino, S., Paik, S., Kaufman, P. A., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052122 · Cited by 5,355 · see citing works

A New Initiative on Precision Medicine

Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202 · see citing works

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599 · see citing works

Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011

Goldhirsch, A., Wood, W. C., Coates, A. S., Gelber, R. D., Thurlimann, B. J. K., & Senn, H.-J. (2011) · Annals of Oncology · doi:10.1093/annonc/mdr304 · Cited by 3,839 · see citing works

Race, Breast Cancer Subtypes, and Survival in the Carolina Breast Cancer Study

Carey, L., Perou, C. M., Livasy, C., Dressler, L. G., Cowan, D., Conway, K., Karaca, G., Troester, M. A., Tse, C. K., Edmiston, S. N., et al. (2006) · JAMA · doi:10.1001/jama.295.21.2492 · Cited by 3,810 · see citing works

Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013

Goldhirsch, A., Winer, E. P., Coates, A. S., Gelber, R. D., Piccart, M. J., Thurlimann, B. J. K., Senn, H.-J., Albain, K. S., André, F., Bergh, J. C. S., et al. (2013) · Annals of Oncology · doi:10.1093/annonc/mdt303 · Cited by 3,759 · see citing works

International network of cancer genome projects

The International Cancer Genome Consortium (2010) · Nature · doi:10.1038/nature08987 · Cited by 2,299 · see citing works

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 · see citing works

Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015

Coates, A. S., Winer, E. P., Goldhirsch, A., Gelber, R. D., Gnant, M. I., Piccart, M. J., Thürlimann, B., Senn, H.-J., André, F., Baselga, J. M., et al. (2015) · Annals of Oncology · doi:10.1093/annonc/mdv221 · Cited by 1,828 · see citing works

Identifying differentially expressed genes using false discovery rate controlling procedures

Reiner, A., Yekutieli, D., & Benjamini, Y. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btf877 · Cited by 1,765 · see citing works

The landscape of cancer genes and mutational processes in breast cancer

Stephens, P. J., Tarpey, P. S., Davies, H. R., Loo, P. V., Greenman, C., Wedge, D. C., Nik‐Zainal, S., Martin, S., Varela, I., Bignell, G. R., et al. (2012) · Nature · doi:10.1038/nature11017 · Cited by 1,686 · see citing works

Proteogenomics connects somatic mutations to signalling in breast cancer

CPTAC, N., Mertins, P., Mani, D. R., Ruggles, K. V., Gillette, M. A., Clauser, K. R., Wang, P., Wang, X., Qiao, J. W., Cao, S., et al. (2016) · Nature · doi:10.1038/nature18003 · Cited by 1,676 · see citing works

The molecular portraits of breast tumors are conserved across microarray platforms

Hu, Z., Fan, C., Oh, D. S., Marron, J. S., He, X., Qaqish, B. F., Livasy, C., Carey, L., Reynolds, E. R., Dressler, L. G., et al. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-96 · Cited by 1,492 · see citing works

Targeting cancer metabolism: a therapeutic window opens

Heiden, M. G. V. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3504 · Cited by 1,409 · see citing works

Transcriptomics technologies

Lowe, R. G. T., Shirley, N. J., Bleackley, M. R., Dolan, S., & Shafee, T. M. A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005457 · Cited by 1,186 · see citing works

Methods of integrating data to uncover genotype-phenotype interactions

Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085 · see citing works

Locoregional Relapse and Distant Metastasis in Conservatively Managed Triple Negative Early-Stage Breast Cancer

Haffty, B. G., Yang, Q., Reiß, M., Kearney, T. J., Higgins, S. A., Weidhaas, J. B., Harris, L. N., Hait, W., & Toppmeyer, D. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2006.06.5664 · Cited by 1,085 · see citing works

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

Blenkiron, C., Goldstein, L. D., Thorne, N., Spiteri, I., Chin, S., Dunning, M., Barbosa‐Morais, N. L., Teschendorff, A. E., Green, A. R., Ellis, I. O., et al. (2007) · Genome biology · doi:10.1186/gb-2007-8-10-r214 · Cited by 955 · see citing works

What is next generation sequencing?

Behjati, S., & Tarpey, P. S. (2013) · Archives of Disease in Childhood Education & Practice · doi:10.1136/archdischild-2013-304340 · Cited by 829 · see citing works

MicroRNAs: New Biomarkers for Diagnosis, Prognosis, Therapy Prediction and Therapeutic Tools for Breast Cancer

Bertoli, G. R., Cava, C., & Castiglioni, I. (2015) · Theranostics · doi:10.7150/thno.11543 · Cited by 818 · see citing works

Subclonal diversification of primary breast cancer revealed by multiregion sequencing

Yates, L. R., Gerstung, M., Knappskog, S., Desmedt, C., Gundem, G., Loo, P. V., Aas, T., Alexandrov, L. B., Larsimont, D., Davies, H., et al. (2015) · Nature Medicine · doi:10.1038/nm.3886 · Cited by 812 · see citing works

Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline

Harris, L. N., Ismaila, N., McShane, L. M., André, F., Collyar, D. E., Gonzalez-Angulo, A. M., Hammond, E. H., Kuderer, N. M., Liu, M., Mennel, R. G., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.65.2289 · Cited by 812 · see citing works

Gene expression profiling in breast cancer: classification, prognostication, and prediction

Reis-Filho, J. S., & Pusztai, L. (2011) · The Lancet · doi:10.1016/s0140-6736(11)61539-0 · Cited by 729 · see citing works

Systems Biology, Proteomics, and the Future of Health Care: Toward Predictive, Preventative, and Personalized Medicine

Weston, A. D., & Hood, L. (2004) · Journal of Proteome Research · doi:10.1021/pr0499693 · Cited by 727 · see citing works

MaZda—A software package for image texture analysis

Szczypiński, P. M., Strzelecki, M., Materka, A., & Klepaczko, A. (2008) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2008.08.005 · Cited by 712 · see citing works

Visualization of omics data for systems biology

Gehlenborg, N., O’Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs, M., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1436 · Cited by 622 · see citing works

The evolution of molecular biology into systems biology

Westerhoff, H. V., & Palsson, B. Ø. (2004) · Nature Biotechnology · doi:10.1038/nbt1020 · Cited by 566 · see citing works

From 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 557 · see citing works

Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis‐driven science in the post‐genomic era

Kell, D. B., & Oliver, S. G. (2004) · BioEssays · doi:10.1002/bies.10385 · Cited by 535 · see citing works

An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer

Teschendorff, A. E., Miremadi, A., Pinder, S. E., Ellis, I. O., & Caldas, C. (2007) · Genome biology · doi:10.1186/gb-2007-8-8-r157 · Cited by 492 · see citing works

The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade

Weigelt, B., Baehner, F. L., & Reis-Filho, J. S. (2009) · The Journal of Pathology · doi:10.1002/path.2648 · Cited by 482 · see citing works

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

Li, H., Zhu, Y., Burnside, E. S., Drukker, K., Hoadley, K. A., Fan, C., Conzen, S. D., Whitman, G. J., Sutton, E. J., Net, J., et al. (2016) · Radiology · doi:10.1148/radiol.2016152110 · Cited by 463 · see citing works

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

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 · see citing works

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Li, H., Zhu, Y., Burnside, E. S., Huang, E. P., Drukker, K., Hoadley, K. A., Fan, C., Conzen, S. D., Zuley, M. L., Net, J., et al. (2016) · npj Breast Cancer · doi:10.1038/npjbcancer.2016.12 · Cited by 367 · see citing works

Patterns of Recurrence and Outcome According to Breast Cancer Subtypes in Lymph Node–Negative Disease: Results From International Breast Cancer Study Group Trials VIII and IX

Metzger, O., Sun, Z., Viale, G., Price, K. N., Crivellari, D., Snyder, R. D., Gelber, R. D., Castiglione‐Gertsch, M., Coates, A. S., Goldhirsch, A., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.46.1574 · Cited by 344 · see citing works

A Decade of Systems Biology

Chuang, H., Hofree, M., & Ideker, T. (2010) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100109-104122 · Cited by 316 · see citing works

Triple-Modality Screening Trial for Familial Breast Cancer Underlines the Importance of Magnetic Resonance Imaging and Questions the Role of Mammography and Ultrasound Regardless of Patient Mutation Status, Age, and Breast Density

Riedl, C. C., Luft, N., Bernhart, C., Weber, M., Bernathova, M., Tea, M.-K. M., Rudas, M., Singer, C. F., & Helbich, T. H. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.56.8626 · Cited by 299 · see citing works

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 · see citing works

A Review of Applications of Metabolomics in Cancer

Beger, R. D. (2013) · Metabolites · doi:10.3390/metabo3030552 · Cited by 289 · see citing works

Transcriptome Profiling in Human Diseases: New Advances and Perspectives

Casamassimi, A., Federico, A., Rienzo, M., Esposito, S., & Ciccodicola, A. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18081652 · Cited by 288 · see citing works

Next-generation sequencing

Reis-Filho, J. S. (2009) · Breast Cancer Research · doi:10.1186/bcr2431 · Cited by 281 · see citing works

Serum Circulating microRNA Profiling for Identification of Potential Breast Cancer Biomarkers

Mar‐Aguilar, F., Mendoza-Ramírez, J. A., Malagón‐Santiago, G. I., Espino-Silva, P. K., Santuario-Facio, S. K., Ruíz-Flores, P., Rodrı́guez-Padilla, C., & Reséndez‐Pérez, D. (2013) · Disease Markers · doi:10.1155/2013/259454 · Cited by 280 · see citing works

Identification of non-invasive miRNAs biomarkers for prostate cancer by deep sequencing analysis of urinary exosomes

Rodríguez, M., Bajo-Santos, C., Hessvik, N. P., Lorenz, S., Fromm, B., Berge, V., Sandvig, K., Linē, A., & Llorente, A. (2017) · Molecular Cancer · doi:10.1186/s12943-017-0726-4 · Cited by 253 · see citing works

Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging

Mazurowski, M. A., Zhang, J., Grimm, L. J., Yoon, S. C., & Silber, J. I. (2014) · Radiology · doi:10.1148/radiol.14132641 · Cited by 241 · see citing works

Behind the Numbers: Decoding Molecular Phenotypes with Radiogenomics—Guiding Principles and Technical Considerations

Kuo, M. D., & Jamshidi, N. (2014) · Radiology · doi:10.1148/radiol.13132195 · Cited by 238 · see citing works

The topography of mutational processes in breast cancer genomes

Morganella, S., Alexandrov, L. B., Głodzik, D., Zou, X., Davies, H. R., Staaf, J., Sieuwerts, A., Brinkman, A., Martin, S., Ramakrishna, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11383 · Cited by 236 · see citing works

PI3K-AKT-mTOR inhibitors in breast cancers: From tumor cell signaling to clinical trials

Dey, N., De, P. K., & Leyland-Jones, B. R. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.02.037 · Cited by 232 · see citing works

Mapping the Cancer Genome

Collins, F. S., & Barker, A. D. (2007) · Scientific American · doi:10.1038/scientificamerican0307-50 · Cited by 231 · see citing works

Identification of prostate cancer biomarkers in urinary exosomes

Øverbye, A., Skotland, T., Koehler, C. J., Thiede, B., Seierstad, T., Berge, V., Sandvig, K., & Llorente, A. (2015) · Oncotarget · doi:10.18632/oncotarget.4851 · Cited by 230 · see citing works

Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in a Clinical Cancer Genetics service setting: Risks of breast/ovarian cancer quoted should reflect the cancer burden in the family

Evans, D. G., Shenton, A., Woodward, E., Lalloo, F., Howell, A., & Maher, E. (2008) · BMC Cancer · doi:10.1186/1471-2407-8-155 · Cited by 226 · see citing works

Magnetic resonance imaging texture analysis classification of primary breast cancer

Waugh, S. A., Purdie, C. A., Jordan, L. B., Vinnicombe, S., Lerski, R. A., Martin, P., & Thompson, A. M. (2015) · European Radiology · doi:10.1007/s00330-015-3845-6 · Cited by 203 · see citing works

Big Data and Biomedical Informatics: A Challenging Opportunity

Bellazzi, R. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0024 · Cited by 184 · see citing works

Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR)

European Society of Radiology (ESR) (2015) · Insights into Imaging · doi:10.1007/s13244-015-0394-0 · Cited by 178 · see citing works

Mapping the cancer genome. Pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies.

Collins, F. S., & Barker, A. D. (2007) · PubMed · Cited by 176 · see citing works

Radiogenomic Analysis of Breast Cancer Using MRI: A Preliminary Study to Define the Landscape

Yamamoto, S., Maki, D. D., Korn, R. L., & Kuo, M. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.11.7824 · Cited by 174 · see citing works

St. Gallen 2013: Brief Preliminary Summary of the Consensus Discussion

Harbeck, N., Thomssen, C., & Gnant, M. (2013) · Breast Care · doi:10.1159/000351193 · Cited by 174 · see citing works

Breast Cancer Adjuvant Therapy: Time to Consider Its Time-Dependent Effects

Jatoi, I., Anderson, W. F., Jeong, J., & Redmond, C. K. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2010.32.3550 · Cited by 172 · see citing works

Cancer Metabolism and Drug Resistance

Rahman, M., & Hasan, M. R. (2015) · Metabolites · doi:10.3390/metabo5040571 · Cited by 168 · see citing works

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 · see citing works

Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma

Zhu, Y., Li, H., Guo, W., Drukker, K., Lan, L., Giger, M. L., & Ji, Y. (2015) · Scientific Reports · doi:10.1038/srep17787 · Cited by 165 · see citing works

Human genomics projects and precision medicine

Carrasco-Ramiro, F., Peiró‐Pastor, R., & Aguado, B. (2017) · Gene Therapy · doi:10.1038/gt.2017.77 · Cited by 164 · see citing works

Breast cancer classification by proteomic technologies: Current state of knowledge

Lam, S. W., Jiménez, C. R., & Boven, E. (2013) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2013.06.006 · Cited by 162 · see citing works

Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms

Grimm, L. J., Zhang, J., & Mazurowski, M. A. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24879 · Cited by 153 · see citing works

Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles

Ashraf, A. B., Daye, D., Gavenonis, S. C., Mies, C., Feldman, M. D., Rosen, M. A., & Despina Kontos, B. (2014) · Radiology · doi:10.1148/radiol.14131375 · Cited by 151 · see citing works

New frontiers in metabolomics: from measurement to insight

Riekeberg, E., & Powers, R. (2017) · F1000Research · doi:10.12688/f1000research.11495.1 · Cited by 149 · see citing works

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

Volinia, S., & Croce, C. M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1304977110 · Cited by 149 · see citing works

Methods, Tools and Current Perspectives in Proteogenomics

Ruggles, K. V., Krug, K., Wang, X., Clauser, K. R., Wang, J., Payne, S., Fenyö, D., Zhang, B., & Mani, D. R. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.mr117.000024 · Cited by 148 · see citing works

Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay

Sutton, E. J., Oh, J. H., Dashevsky, B. Z., Veeraraghavan, H., Apte, A., Thakur, S. B., Deasy, J. O., & Morris, E. A. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24890 · Cited by 138 · see citing works

Breast cancer molecular subtype classifier that incorporates MRI features

Sutton, E. J., Dashevsky, B. Z., Oh, J. H., Veeraraghavan, H., Apte, A., Thakur, S. B., Morris, E. A., & Deasy, J. O. (2016) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25119 · Cited by 138 · see citing works

Magnetic resonance image–guided proteomics of human glioblastoma multiforme

Hobbs, S. K., Shi, G., Homer, R., Harsh, G., Atlas, S. W., & Bednarski, M. D. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10395 · Cited by 135 · see citing works

Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Kamel, H. F. M., & Al-Amodi, H. S. A. B. (2017) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2016.11.005 · Cited by 134 · see citing works

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Kim, Y., Jeon, J., Mejia, S., Yao, C. Q., Ignatchenko, V., Nyalwidhe, J. O., Gramolini, A. O., Lance, R. S., Troyer, D. A., Drake, R. R., et al. (2016) · Nature Communications · doi:10.1038/ncomms11906 · Cited by 125 · see citing works

Breast Cancer Molecular Subtypes in Patients With Locally Advanced Disease: Impact on Prognosis, Patterns of Recurrence, and Response to Therapy

Huber, K. E., Carey, L. A., & Wazer, D. E. (2009) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2009.05.004 · Cited by 123 · see citing works

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis

Yamamoto, S., Han, W., Kim, Y., Du, L., Jamshidi, N., Huang, D., Kim, J. H., & Kuo, M. (2015) · Radiology · doi:10.1148/radiol.15142698 · Cited by 120 · see citing works

Predicting long‐term survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling

Cao, M. D., Sitter, B., Bathen, T. F., Bofin, A. M., Lønning, P. E., Lundgren, S., & Gribbestad, I. S. (2011) · NMR in Biomedicine · doi:10.1002/nbm.1762 · Cited by 120 · see citing works

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Ruggles, K. V., Tang, Z., Wang, X., Grover, H., Askenazi, M., Teubl, J., Cao, S., McLellan, M. D., Clauser, K. R., Tabb, D. L., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.056226 · Cited by 118 · see citing works

Imaging genomics in cancer research: limitations and promises

Bai, H. X., Lee, A. M., Yang, L., Zhang, P. J., Davatzikos, C. A., Maris, J. M., & Diskin, S. J. (2016) · British Journal of Radiology · doi:10.1259/bjr.20151030 · Cited by 112 · see citing works

Texture analysis methodologies for magnetic resonance imaging

Materka, A. (2004) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2004.6.2/amaterka · Cited by 112 · see citing works

Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer

Chung, L., Moore, K., Phillips, L., Boyle, F. M., Marsh, D. J., & Baxter, R. C. (2014) · Breast Cancer Research · doi:10.1186/bcr3676 · Cited by 108 · see citing works

Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

Gonzalez-Angulo, A. M., Hennessy, B. T., Meric‐Bernstam, F., Şahin, A. A., Liu, W., Ju, Z., Carey, M. S., Myhre, S., Speers, C. W., Deng, L., et al. (2011) · Clinical Proteomics · doi:10.1186/1559-0275-8-11 · Cited by 102 · see citing works

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review

Li, B.-B., He, X., Jia, W., & Li, H. (2017) · Molecules · doi:10.3390/molecules22071173 · Cited by 94 · see citing works

Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways

Tang, J. P., Karhinen, L., Xu, T., Szwajda, A., Yadav, B., Wennerberg, K., & Aittokallio, T. A. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003226 · Cited by 91 · see citing works

Immunotherapy Combined or Sequenced With Targeted Therapy in the Treatment of Solid Tumors: Current Perspectives

Atkins, M. B., & Larkin, J. (2016) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv414 · Cited by 91 · see citing works

Improved Differentiation of Benign and Malignant Breast Tumors with Multiparametric 18Fluorodeoxyglucose Positron Emission Tomography Magnetic Resonance Imaging: A Feasibility Study

Pinker, K., Bogner, W., Baltzer, P., Karanikas, G., Magometschnigg, H. F., Brader, P., Gruber, S., Bickel, H., Dubsky, P. C., Bagó-Horváth, Z., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-2810 · Cited by 87 · see citing works

Quantitative Sodium MR Imaging at 7 T: Initial Results and Comparison with Diffusion-weighted Imaging in Patients with Breast Tumors

Zaric, O., Pinker, K., Zbýň, Š., Strasser, B., Robinson, S., Minarikova, L., Gruber, S., Farr, A., Singer, C., Helbich, T. H., et al. (2016) · Radiology · doi:10.1148/radiol.2016151304 · Cited by 86 · see citing works

Comprehensive proteomics

Beck, M., Claassen, M., & Aebersold, R. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.09.002 · Cited by 83 · see citing works

Metabolomics Biomarkers for Breast Cancer

Günther, U. L. (2015) · Pathobiology · doi:10.1159/000430844 · Cited by 80 · see citing works

Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features

Woodard, G. A., Ray, K. M., Joe, B. N., & Price, E. R. (2017) · Radiology · doi:10.1148/radiol.2017162333 · Cited by 77 · see citing works

Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies

Brown, M. V., McDunn, J. E., Gunst, P. R., Smith, E. M., Milburn, M. V., Troyer, D. A., & Lawton, K. A. (2012) · Genome Medicine · doi:10.1186/gm332 · Cited by 73 · see citing works

Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis.

Ghasemi, M., Nabipour, I., Omrani, A., Alipour, Z., & Assadi, M. (2016) · PubMed Central · Cited by 72 · see citing works

MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes

Blaschke, E. M., & Abé, H. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24884 · Cited by 71 · see citing works

Precision medicine in breast cancer: reality or utopia?

Bettaı̈eb, A., Paul, C., Plenchette, S., Shan, J., Chouchane, L., & Ghiringhelli, F. (2017) · Journal of Translational Medicine · doi:10.1186/s12967-017-1239-z · Cited by 69 · see citing works

White paper on imaging biomarkers

European Society of Radiology (ESR) (2010) · Insights into Imaging · doi:10.1007/s13244-010-0025-8 · Cited by 67 · see citing works

Molecular signatures in breast cancer

Lal, S., Reed, A. E. M., Luca, X. M. D., & Simpson, P. T. (2017) · Methods · doi:10.1016/j.ymeth.2017.06.032 · Cited by 65 · see citing works

Current FDA-approved treatments for non-small cell lung cancer and potential biomarkers for its detection

Ruiz-Ceja, K. A., & Chirino, Y. I. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.03.018 · Cited by 65 · see citing works

Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer

Yamaguchi, K., Abé, H., Newstead, G. M., Egashira, R., Nakazono, T., Imaizumi, T., & Irié, H. (2014) · Breast Cancer · doi:10.1007/s12282-013-0512-0 · Cited by 63 · see citing works

Many Phenotypes Without Many False Discoveries: Error Controlling Strategies for Multitrait Association Studies

Peterson, C. B., Bogomolov, M., Benjamini, Y., & Sabatti, C. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21942 · Cited by 62 · see citing works

Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk

Mahrooghy, M., Ashraf, A. B., Daye, D., McDonald, E. S., Rosen, M. A., Mies, C., Feldman, M. D., & Despina Kontos, B. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2395812 · Cited by 62 · see citing works

Imaging in the Age of Precision Medicine: Summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology

Herold, C., Lewin, J. S., Wibmer, A. G., Thrall, J. H., Krestin, G. P., Dixon, A. K., Schoenberg, S. O., Geckle, R. J., Muellner, A., & Hricak, H. (2015) · Radiology · doi:10.1148/radiol.2015150709 · Cited by 61 · see citing works

Cholangiocarcinoma: Correlation between Molecular Profiling and Imaging Phenotypes

Sadot, E., Simpson, A. L., Gian, R. K., Gönen, M., Shia, J., Allen, P. J., D'Angelica, M. I., DeMatteo, R. P., Kingham, T. P., & Jarnagin, W. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132953 · Cited by 59 · see citing works

Big Data: the challenge for small research groups in the era of cancer genomics

Noor, A. M., Holmberg, L., Gillett, C., & Grigoriadis, A. E. (2015) · British Journal of Cancer · doi:10.1038/bjc.2015.341 · Cited by 59 · see citing works

How could breast cancer molecular features contribute to locoregional treatment decision making?

Tsoutsou, P. P., Vozenin, M., Durham, A., & Bourhis, J. (2016) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2016.12.006 · Cited by 57 · see citing works

Breast MRI radiogenomics: Current status and research implications

Grimm, L. J. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25116 · Cited by 53 · see citing works

Translating cancer epigenomics into the clinic: focus on lung cancer

Marí‐Alexandre, J., Díaz‐Lagares, Á., Villalba-Esparza, M., Juan, Ó., Crujeiras, A. B., Calvo, A., & Sandoval, J. (2017) · Translational research · doi:10.1016/j.trsl.2017.05.008 · Cited by 50 · see citing works

A New Contrast in MR Mammography by Means of Chemical Exchange Saturation Transfer (CEST) Imaging at 3 Tesla: Preliminary Results

Schmitt, B., Zámecnik, P., Zaiß, M., Rerich, E., Schuster, L., Bachert, P., & Schlemmer, H. (2011) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/s-0031-1281764 · Cited by 47 · see citing works

Detecting blood oxygen level‐dependent (BOLD) contrast in the breast

Rakow‐Penner, R., Daniel, B. L., & Glover, G. H. (2010) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22227 · Cited by 46 · see citing works

Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype

Grimm, L. J., Zhang, J., Baker, J. A., Soo, M. S., Johnson, K. S., & Mazurowski, M. A. (2017) · The Breast Journal · doi:10.1111/tbj.12799 · Cited by 46 · see citing works

Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer

Al‐Ejeh, F., Miranda, M. S., Shi, W., Simpson, P. T., Song, S., Vargas, A. C., Saunus, J. M., Smart, C. E., Mariasegaram, M., Wiegmans, A. P., et al. (2014) · Oncotarget · doi:10.18632/oncotarget.1865 · Cited by 44 · see citing works

Mutation matters in precision medicine: A future to believe in

Dey, N., Williams, C. B., Leyland-Jones, B. R., & De, P. K. (2017) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2017.03.002 · Cited by 41 · see citing works

Next-Generation Proteomics and Its Application to Clinical Breast Cancer Research

Mardamshina, M., & Geiger, T. (2017) · American Journal Of Pathology · doi:10.1016/j.ajpath.2017.07.003 · Cited by 41 · see citing works

Qualitative Age Interactions in Breast Cancer Studies: Mind the Gap

Anderson, W. F., Jatoi, I., & Sherman, M. E. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2009.22.9450 · Cited by 38 · see citing works

Personalized Medicine in the U.S. and Germany: Awareness, Acceptance, Use and Preconditions for the Wide Implementation into the Medical Standard

Kichko, K., Marschall, P., & Flessa, S. (2016) · Journal of Personalized Medicine · doi:10.3390/jpm6020015 · Cited by 36 · see citing works

Introduction of an Automated User–Independent Quantitative Volumetric Magnetic Resonance Imaging Breast Density Measurement System Using the Dixon Sequence

Wengert, G., Helbich, T. H., Vogl, W., Baltzer, P., Langs, G., Weber, M., Bogner, W., Gruber, S., Trattnig, S., & Pinker, K. (2014) · Investigative Radiology · doi:10.1097/rli.0000000000000102 · Cited by 34 · see citing works

Opportunities for Nanotechnology-Based Innovation in Tissue Proteomics

Geho, D. H., Lahar, N., Mauro Ferrari, B., Petricoin, E. F., & Liotta, L. A. (2004) · Biomedical Microdevices · doi:10.1023/b:bmmd.0000042053.51016.b4 · Cited by 32 · see citing works

Moreton Lecture: Imaging in the Age of Precision Medicine

Thrall, J. H. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.06.003 · Cited by 28 · see citing works

Gene-Expression-Based Predictors for Breast Cancer

Gupta, A., Mutebi, M. C., & Bardia, A. (2015) · Annals of Surgical Oncology · doi:10.1245/s10434-015-4703-0 · Cited by 27 · see citing works

Prognostic and predictive biomarkers in melanoma: an update

Foth, M., Wouters, J., de Chaumont, C., Dynoodt, P., & Gallagher, W. M. (2015) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2016.1126511 · Cited by 25 · see citing works

The Evolving Role of Companion Diagnostics for Breast Cancer in an Era of Next-Generation Omics

Rosenbaum, J. N., & Weisman, P. S. (2017) · American Journal Of Pathology · doi:10.1016/j.ajpath.2017.04.021 · Cited by 22 · see citing works

Genomic profiling of breast cancers

Curtis, C. N. (2014) · Current Opinion in Obstetrics & Gynecology · doi:10.1097/gco.0000000000000145 · Cited by 20 · see citing works

Optimizing targeted cancer therapy: Towards clinical application of systems biology approaches

Sikkema, A. H., den Dunnen, W. F. A., Diks, S. H., Peppelenbosch, M. P., & de Bont, E. S. J. M. (2011) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2011.05.002 · Cited by 19 · see citing works

‘Omics Approaches in Breast Cancer Research and Clinical Practice

Croft, P. K., Al‐Ejeh, F., Reed, A. E. M., Saunus, J. M., & Lakhani, S. R. (2016) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000128 · Cited by 18 · see citing works

CEST-imaging: A new contrast in MR-mammography by means of chemical exchange saturation transfer

Schmitt, B., Trattnig, S., & Schlemmer, H.-P. (2012) · European Journal of Radiology · doi:10.1016/s0720-048x(12)70060-8 · Cited by 18 · see citing works

Metabolomics and Cardiology: Toward the Path of Perinatal Programming and Personalized Medicine

Pintus, R., Bassareo, P. P., Dessì, A., Deidda, M., Mercuro, G. G., & Fanos, V. (2017) · BioMed Research International · doi:10.1155/2017/6970631 · Cited by 17 · see citing works

Transcriptomics Using Next Generation Sequencing Technologies

Lee‐Liu, D., Almonacid, L. I., Faunes, F., Melo, F., & Larraı́n, J. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-992-1_18 · Cited by 16 · see citing works

Driving Toward Precision Medicine for Acute Leukemias: Are We There Yet?

Chung, C., & Ma, H. (2017) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.1977 · Cited by 15 · see citing works

Precision Military Medicine: Conducting a multi-site clinical utility study of genomic and lifestyle risk factors in the United States Air Force

Delaney, S., Brenner, R., Schmidlen, T., Dempsey, M. P., London, K. E., Gordon, E. S., Bellafante, M., Nasuti, A., Scheinfeldt, L., Rajula, K. D., et al. (2017) · npj Genomic Medicine · doi:10.1038/s41525-016-0004-1 · Cited by 14 · see citing works

Immunotherapy in glioblastoma: emerging options in precision medicine

Hodges, T. R., Ferguson, S. D., & Heimberger, A. B. (2016) · CNS Oncology · doi:10.2217/cns-2016-0009 · Cited by 12 · see citing works

A decade of Systems Biology: where are we and where are we going to?

Peitsch, M. C., & de Graaf, D. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.06.002 · Cited by 11 · see citing works

HER2-Orientated Therapy in Early and Metastatic Breast Cancer

Iborra, S., & Stickeler, E. (2016) · Breast Care · doi:10.1159/000453603 · Cited by 11 · see citing works

Radiogenomics and Radioproteomics

Djekidel, M. (2012) · OMICS Journal of Radiology · doi:10.4172/2167-7964.1000115 · Cited by 7 · see citing works

Characterization of enzymatic micromachining for construction of variable cross-section microchannel topologies

Huang, J., Han, D., Ruggles, M., Jayaraman, A., & Ugaz, V. M. (2016) · Biomicrofluidics · doi:10.1063/1.4948508 · Cited by 4 · see citing works

Meeting Report: EMBL Conference – Omics and Personalized Medicine

Peng, J. (2012) · Biotechnology Journal · doi:10.1002/biot.201200122 · Cited by 2 · see citing works

Secure, privacy-preserving and federated machine learning in medical imaging

Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,501 · see citing works

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15027-z · Cited by 607 · see citing works

Multimodal data fusion for cancer biomarker discovery with deep learning

Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00633-5 · Cited by 402 · see citing works

Biobanking 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 408 · see citing works

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Geras, K. J., Mann, R. M., & Moy, L. P. (2019) · Radiology · doi:10.1148/radiol.2019182627 · Cited by 298 · see citing works

Radiomics in breast cancer: Current advances and future directions

Qi, Y.-J., Su, G.-H., You, C., Zhang, X., Xiao, Y., Jiang, Y.-Z., & Shao, Z. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101719 · Cited by 195 · see citing works

Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer

Jiang, Y., Chen, C., Xie, J., Wang, W., Zha, X., Lv, W., Chen, H., Hu, Y., Li, T., Yu, J., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.09.007 · Cited by 184 · see citing works

Novel Approaches to Screening for Breast Cancer

Mann, R. M., Hooley, R. J., Barr, R. G., & Moy, L. P. (2020) · Radiology · doi:10.1148/radiol.2020200172 · Cited by 169 · see citing works

Harnessing non-destructive 3D pathology

Liu, J. T. C., Glaser, A. K., Bera, K., True, L. D., Reder, N. P., Eliceiri, K. W., & Madabhushi, A. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00681-x · Cited by 158 · see citing works

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Zhang, J., Wu, J., Zhou, X. S., Shi, F., & Shen, D. G. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.001 · Cited by 145 · see citing works

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Saxena, S., Jena, B., Gupta, N., Das, S., Sarmah, D., Bhattacharya, P., Nath, T., Paul, S., Fouda, M. M., Kalra, M. K., et al. (2022) · Cancers · doi:10.3390/cancers14122860 · Cited by 138 · see citing works

Application of radiomics and machine learning in head and neck cancers

Peng, Z., Wang, Y.-M., Wang, Y.-X., Jiang, S., Fan, R.-H., Zhang, H., & Jiang, W.-H. (2021) · International Journal on Biological Sciences · doi:10.7150/ijbs.55716 · Cited by 91 · see citing works

A review of radiomics and genomics applications in cancers: the way towards precision medicine

Li, S., & Zhou, B. (2022) · Radiation Oncology · doi:10.1186/s13014-022-02192-2 · Cited by 100 · see citing works

Ovarian cancer: An update on imaging in the era of radiomics

Nougaret, S., Tardieu, M., Vargas, H. A., Reinhold, C., Perre, S. V., Bonanno, N., Sala, E., & Thomassin‐Naggara, I. (2018) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2018.11.007 · Cited by 94 · see citing works

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Tran, W. T., Jerzak, K. J., Lu, F.-I., Klein, J., Tabbarah, S., Lagree, A., Wu, T., Rosado-Méndez, I. M., Law, E., Saednia, K., et al. (2019) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2019.07.010 · Cited by 93 · see citing works

Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence

Satake, H., Ishigaki, S., Ito, R., & Naganawa, S. (2021) · La radiologia medica · doi:10.1007/s11547-021-01423-y · Cited by 88 · see citing works

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy

Wu, J., Mayer, A. T., & Li, R. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.12.005 · Cited by 89 · see citing works

The Application of Radiomics in Breast MRI: A Review

Ye, D., Wang, H., & Yu, T. (2020) · Technology in Cancer Research & Treatment · doi:10.1177/1533033820916191 · Cited by 84 · see citing works

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

Jiang, M., Li, C.-L., Luo, X., Chuan, Z.-R., Chen, R., Tang, S.-C., Lv, W., Cui, X., & Dietrich, C. F. (2021) · European Radiology · doi:10.1007/s00330-021-08330-w · Cited by 84 · see citing works

Imaging diagnosis of metastatic breast cancer

Pesapane, F., Downey, K., Rotili, A., Cassano, E., & Koh, D. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00885-4 · Cited by 83 · see citing works

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future

Pesapane, F., Rotili, A., Agazzi, G. M., Botta, F., Raimondi, S., Penco, S., Dominelli, V., Cremonesi, M., Jereczek‐Fossa, B. A., Carrafiello, G., et al. (2021) · Current Oncology · doi:10.3390/curroncol28040217 · Cited by 78 · see citing works

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma

Wang, H., Song, B., Ye, N., Ren, J., Sun, X., Dai, Z., Zhang, Y., & Chen, B. T. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108755 · Cited by 77 · see citing works

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Shao, J., Ma, J., Zhang, Q., Li, W., & Wang, C. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.006 · Cited by 81 · see citing works

PET/CT radiomics in breast cancer: Mind the step

Sollini, M., Cozzi, L., Ninatti, G., Antunovic, L., Cavinato, L., Chiti, A., & Kirienko, M. (2020) · Methods · doi:10.1016/j.ymeth.2020.01.007 · Cited by 75 · see citing works

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

Zanfardino, M., Pane, K., Mirabelli, P., Salvatore, M., & Franzese, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20236033 · Cited by 74 · see citing works

Radiomics and radiogenomics in ovarian cancer: a literature review

Nougaret, S., McCague, C., Tibermacine, H., Vargas, H. A., Rizzo, S., & Sala, E. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02820-z · Cited by 73 · see citing works

Comprehensive Analysis of Advancement in Optical Biosensing Techniques for Early Detection of Cancerous Cells

Ramola, A., Shakya, A. K., & Bergman, A. (2025) · Biosensors · doi:10.3390/bios15050292 · Cited by 73 · see citing works

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Li, S., Deng, Y., Zhu, Z., Hua, H., & Tao, Z. (2021) · Diagnostics · doi:10.3390/diagnostics11091523 · Cited by 71 · see citing works

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Zhong, N., Wang, H., Huang, X., Li, Z., Cao, L., Huo, F., Liu, B., & Bu, L. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.07.002 · Cited by 71 · see citing works

Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer

Jiang, Y., Wang, W., Chen, C., Zhang, X., Zha, X., Lv, W., Xie, J., Huang, W., Sun, Z., Hu, Y., et al. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00340 · Cited by 67 · see citing works

Radiogenomics for Precision Medicine With a Big Data Analytics Perspective

Panayides, A. S., Pattichis, M. S., Leandrou, S., Pitris, C., Constantinidou, A., & Pattichis, C. S. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2879381 · Cited by 66 · see citing works

Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation?

Pesapane, F., Suter, M. B., Rotili, A., Penco, S., Nigro, O., Cremonesi, M., Bellomi, M., Jereczek‐Fossa, B. A., Pinotti, G., & Cassano, E. (2020) · Medical Oncology · doi:10.1007/s12032-020-01353-1 · Cited by 67 · see citing works

AI-enhanced breast imaging: Where are we and where are we heading?

Bitencourt, A. G. V., Naranjo, I. D., Gullo, R. L., Saccarelli, C. R., & Pinker, K. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109882 · Cited by 65 · see citing works

Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression

Bismeijer, T., van der Velden, B. H. M., Canisius, S., Lips, E. H., Loo, C. E., Viergever, M. A., Wesseling, J., Gilhuijs, K. G. A., & Wessels, L. F. A. (2020) · Radiology · doi:10.1148/radiol.2020191453 · Cited by 62 · see citing works

A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules

Weng, Q., Zhou, L., Wang, H., Hui, J., chen, M., Pang, P., Zheng, L., Xu, M., Wang, Z., & Ji, J. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.07.026 · Cited by 59 · see citing works

The application of radiomics in predicting gene mutations in cancer

Qi, Y., Zhao, T., & Han, M. (2022) · European Radiology · doi:10.1007/s00330-021-08520-6 · Cited by 58 · see citing works

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Philip, A. K., Samuel, B., Bhatia, S., Khalifa, S. A. M., & El-Seedi, H. R. (2022) · Life · doi:10.3390/life13010024 · Cited by 67 · see citing works

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

He, W., Huang, W., Zhang, L., Wu, X.-W., Zhang, S.-X., & Zhang, B. (2024) · MedComm · doi:10.1002/mco2.722 · Cited by 65 · see citing works

Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study

Filograna, L., Lenkowicz, J., Cellini, F., Dinapoli, N., Manfrida, S., Magarelli, N., Leone, A., Colosimo, C., & Valentini, V. (2018) · La radiologia medica · doi:10.1007/s11547-018-0935-y · Cited by 57 · see citing works

‘Omics Approaches to Explore the Breast Cancer Landscape

Parsons, J., & Francavilla, C. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00395 · Cited by 57 · see citing works

CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma

Attiyeh, M. A., Chakraborty, J., McIntyre, C. A., Kappagantula, R. L., Chou, Y., Aşkan, G., Seier, K., Gönen, M., Baştürk, O., Balachandran, V. P., et al. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-02112-1 · Cited by 55 · see citing works

Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis

Pesapane, F., Rotili, A., Botta, F., Raimondi, S., Bianchini, L., Corso, F., Ferrari, F., Penco, S., Nicosia, L., Bozzini, A. C., et al. (2021) · Cancers · doi:10.3390/cancers13174271 · Cited by 54 · see citing works

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., & Li, R. (2018) · Breast Cancer Research · doi:10.1186/s13058-018-1039-2 · Cited by 53 · see citing works

A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke

Wang, H., Sun, Y., Ge, Y., Wu, P., Lin, J., Zhao, J., & Song, B. (2021) · Neurology and Therapy · doi:10.1007/s40120-021-00263-2 · Cited by 51 · see citing works

Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions

Cui, H., Sun, Y., Zhao, D., Zhang, X., Kong, H., Hu, N., Wang, P., Zuo, X., Fan, W., Yao, Y., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-022-03840-7 · Cited by 50 · see citing works

Dynamic Contrast-Enhanced (DCE) MRI

Li, X., Huang, W., & Holmes, J. H. (2023) · Magnetic Resonance Imaging Clinics of North America · doi:10.1016/j.mric.2023.09.001 · Cited by 50 · see citing works

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

Marino, M. A., Leithner, D., Sung, J. S., Avendaño, D., Morris, E. A., Pinker, K., & Jochelson, M. S. (2020) · Diagnostics · doi:10.3390/diagnostics10070492 · Cited by 50 · see citing works

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Saleh, G. A., Batouty, N. M., Gamal, A., Elnakib, A. A., Hamdy, O., Sharafeldeen, A., Mahmoud, A. H., Ghazal, M. A., Yousaf, J., Alhalabi, M. T., et al. (2023) · Cancers · doi:10.3390/cancers15215216 · Cited by 50 · see citing works

Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes

Park, E. K., Lee, K., Seo, B. K., Cho, K. R., Woo, O. H., Son, G. S., Lee, H. Y., & Chang, Y. W. (2019) · Scientific Reports · doi:10.1038/s41598-019-54371-z · Cited by 49 · see citing works

Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

Wu, L., Zhao, Y., Lin, P., Qin, H., Liu, Y., Wan, D., Li, X., He, Y., & Yang, H. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00610-7 · Cited by 45 · see citing works

Challenges and opportunities for artificial intelligence in oncological imaging

Cheung, H., & Rubin, D. L. (2021) · Clinical Radiology · doi:10.1016/j.crad.2021.03.009 · Cited by 45 · see citing works

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients

Guo, Y., Li, T., Gong, B., Hu, Y., Wang, S., Yang, L., & Zheng, C. (2024) · Advanced Science · doi:10.1002/advs.202408069 · Cited by 47 · see citing works

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Beer, L., Şahin, H., Bateman, N. W., Blazic, I. M., Vargas, H. A., Veeraraghavan, H., Kirby, J., Fevrier-Sullivan, B., Freymann, J., Jaffe, C. C., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06755-3 · Cited by 43 · see citing works

Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

Zhuang, X., Chen, C., Liu, Z., Zhang, L., Zhou, X., Cheng, M., Ji, F., Zhu, T., Lei, C., Zhang, J., et al. (2020) · Translational Oncology · doi:10.1016/j.tranon.2020.100831 · Cited by 40 · see citing works

Radiomics signature for the preoperative assessment of stage in advanced colon cancer.

Li, Y., Eresen, A., Lu, Y., Yang, J. Y., Shangguan, J., Velichko, Y., Yaghmai, V., & Zhang, Z. (2019) · PubMed Central · Cited by 39 · see citing works

Progressive Desmoid Tumor: Radiomics Compared With Conventional Response Criteria for Predicting Progression During Systemic Therapy—A Multicenter Study by the French Sarcoma Group

Crombé, A., Kind, M., Ray-Coquard, I. L., Isambert, N., Chevreau, C., André, T., Lebbé, C., Cesne, A. L., Bompas, E., Piperno-Neumann, S., et al. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.19.22635 · Cited by 39 · see citing works

Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs

Siviengphanom, S., Gandomkar, Z., Lewis, S. J., & Brennan, P. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.09.025 · Cited by 40 · see citing works

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Ong, W., Zhu, L., Zhang, W., Kuah, T., Lim, D. S. W., Low, X. Z., Thian, Y. L., Teo, E. C., Tan, J. H., Kumar, N., et al. (2022) · Cancers · doi:10.3390/cancers14164025 · Cited by 41 · see citing works

Radiomics: an Introductory Guide to What It May Foretell

Nougaret, S., Tibermacine, H., Tardieu, M., & Sala, E. (2019) · Current Oncology Reports · doi:10.1007/s11912-019-0815-1 · Cited by 37 · see citing works

Breast Ultrasound Microvascular Imaging and Radiogenomics

Park, A. Y., Seo, B. K., & Han, M.-R. (2021) · Korean Journal of Radiology · doi:10.3348/kjr.2020.1166 · Cited by 37 · see citing works

Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer

Xu, H., Liu, J., Chen, Z., Wang, C., Liu, Y., Wang, M., Zhou, P., Luo, H., & Ren, J. (2022) · European Radiology · doi:10.1007/s00330-022-08539-3 · Cited by 37 · see citing works

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype

Furtney, I., Bradley, R. M., & Kabuka, M. R. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3290394 · Cited by 36 · see citing works

Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

Wei, R., Wang, H., Wang, L., Hu, W., Sun, X., Dai, Z., Zhu, J., Li, H., Ge, Y., & Song, B. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00553-z · Cited by 36 · see citing works

Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance

Liu, J., Guo, W., Zeng, P., Geng, Y., Liu, Y., Ouyang, H., Lang, N., & Yuan, H. (2021) · European Radiology · doi:10.1007/s00330-021-08150-y · Cited by 36 · see citing works

MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review

Yin, X.-X., Hadjiloucas, S., Zhang, Y., & Tian, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106510 · Cited by 36 · see citing works

A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes

Rabah, C. B., Sattar, A., Ibrahim, A., & Serag, A. (2025) · Diagnostics · doi:10.3390/diagnostics15080995 · Cited by 35 · see citing works

An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer

Li, J., Zhou, Y., Wang, X., Zhou, M., Chen, X., & Luan, K. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02863-2 · Cited by 35 · see citing works

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Ni, M., Zhou, X., Liu, J., Yu, H., Gao, Y., Zhang, X., & Li, Z. (2020) · BMC Cancer · doi:10.1186/s12885-020-07557-y · Cited by 34 · see citing works

Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis

Gong, X., Li, Q., Gu, L., Chen, C., Liu, X., Zhang, X., Wang, B., Sun, C., Yang, D., Li, L., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1158736 · Cited by 33 · see citing works

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad, A., Kucharczyk, M. J., Cheddad, A., Clarke, S. E., Hassan, L., Ding, S., Rathore, S., Zhang, M., Katib, Y., Bahoric, B., et al. (2021) · Cancers · doi:10.3390/cancers13030552 · Cited by 33 · see citing works

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

Hussain, S., Lafarga-Osuna, Y., Ali, M., Naseem, U., Ahmed, M., & Tamez‐Peña, J. G. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05515-6 · Cited by 30 · see citing works

Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features

Cen, D., Xu, L., Zhang, S., Chen, Z., Huang, Y. X., Li, Z., & Liang, B. (2019) · European Radiology · doi:10.1007/s00330-019-06049-3 · Cited by 30 · see citing works

Role of mammogram and ultrasound imaging in predicting breast cancer subtypes in screening and symptomatic patients

Ian, T. W. M., Tan, E. Y., & Chotai, N. C. (2021) · World Journal of Clinical Oncology · doi:10.5306/wjco.v12.i9.808 · Cited by 30 · see citing works

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

López, O. V. G., Herraiz, J. L., Udías, J. M., Haug, A. R., Papp, L., Cioni, D., & Neri, E. (2022) · Cancers · doi:10.3390/cancers14122922 · Cited by 30 · see citing works

Breast cancer recurrence after immediate and delayed postmastectomy breast reconstruction—A systematic review and meta‐analysis

Bargon, C. A., Young‐Afat, D. A., Ikinci, M., Braakenburg, A., Rakhorst, H. A., Mureau, M. A. M., Verkooijen, H. M., & Doeksen, A. (2022) · Cancer · doi:10.1002/cncr.34393 · Cited by 30 · see citing works

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Tsougos, I., Vamvakas, A., Kappas, C., Fezoulidis, I. V., & Vassiou, K. G. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/7417126 · Cited by 29 · see citing works

BARD1 and Breast Cancer: The Possibility of Creating Screening Tests and New Preventive and Therapeutic Pathways for Predisposed Women

Śniadecki, M., Brzeziński, M., Darecka, K., Klasa-Mazurkiewicz, D., Poniewierza, P., Krzeszowiec, M., Kmieć, N., & Wydra, D. G. (2020) · Genes · doi:10.3390/genes11111251 · Cited by 29 · see citing works

Peritoneal Carcinosis: What the Radiologist Needs to Know

Reginelli, A., Giacobbe, G., Canto, M. T. D., Alessandrella, M., Balestrucci, G., Urraro, F., Russo, G. M., Gallo, L., Danti, G., Frittoli, B., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13111974 · Cited by 27 · see citing works

Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection

You, C., Su, G.-H., Zhang, X., Xiao, Y., Zheng, R., Sun, S., Zhou, J.-Y., Lin, L., Wang, Z., Wang, H., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00666-y · Cited by 27 · see citing works

Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing

Park, A. Y., Han, M.-R., Park, K. H., Kim, J., Son, G. S., Lee, H. Y., Chang, Y. W., Park, E. K., Cha, S. H., Cho, Y., et al. (2020) · Radiology · doi:10.1148/radiol.2020191368 · Cited by 27 · see citing works

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma

Lam, S., Zhang, J., Zhang, Y., Li, B., Ni, R., Zhou, T., Peng, T., Cheung, A. L.-Y., Chau, T., Lee, F. K.-H., et al. (2022) · Life · doi:10.3390/life12020241 · Cited by 27 · see citing works

Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography

Sansone, M., Fusco, R., Grassi, F., Gatta, G., Belfiore, M. P., Angelone, F., Ricciardi, C., Ponsiglione, A. M., Amato, F., Galdiero, R., et al. (2023) · Current Oncology · doi:10.3390/curroncol30010064 · Cited by 26 · see citing works

Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using machine learning radiomics based on CECT: A multi-institutional study

Chen, B., Mao, Y., Li, J., Zhao, Z., Chen, Q., Yu, Y., Yang, Y., Dong, Y., Lin, G., Yao, J., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107612 · Cited by 25 · see citing works

Spherical nucleic acids-based nanoplatforms for tumor precision medicine and immunotherapy

Liu, S., Yu, C., & Wei, H. (2023) · Materials Today Bio · doi:10.1016/j.mtbio.2023.100750 · Cited by 24 · see citing works

Towards Precision Medicine for Cancer Patient Stratification by Classifying Cancer by Using Machine Learning

Sarker, M. (2022) · Journal of Science & Technology · doi:10.55662/jst.2022.3301 · Cited by 23 · see citing works

Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

Özcan, İ., Aydin, H., & ÇETİNKAYA, A. (2022) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2022.23.10.3287 · Cited by 23 · see citing works

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

Mehmood, S., Faheem, M. Q., Ismail, H., Farhat, S. M., Ali, M., Younis, S., & Asghar, M. N. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.783494 · Cited by 22 · see citing works

Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation

Yue, W., Zhang, H., Gao, S., Li, G., Sun, Z., Tang, Z., Cai, J., Tian, N., Zhou, J., Dong, J., et al. (2023) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001474 · Cited by 22 · see citing works

Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses

Li, Y., Yang, Z., Lv, W., Qin, Y., Tang, C., Yan, X., Guo, Y., Xia, L., & Ai, T. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.738330 · Cited by 21 · see citing works

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

Ming, W., Zhu, Y., Bai, Y., Gu, W., Li, F., Hu, Z., Xia, T., Dai, Z., Yu, X., Li, H., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.943326 · Cited by 21 · see citing works

Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis

Arimura, H., Soufi, M., Ninomiya, K., Kamezawa, H., & Yamada, M. (2018) · Radiological Physics and Technology · doi:10.1007/s12194-018-0486-x · Cited by 25 · see citing works

Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer

Chhetri, A., Li, X., & Rispoli, J. V. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00175 · Cited by 21 · see citing works

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

Bevilacqua, V., Altini, N., Prencipe, B., Brunetti, A., Villani, L., Sacco, A., Morelli, C., Ciaccia, M., & Scardapane, A. (2021) · Electronics · doi:10.3390/electronics10202475 · Cited by 20 · see citing works

Artificial intelligence in breast imaging: potentials and challenges

Li, J.-W., Sheng, D., Chen, J., You, C., Liu, S., Xu, H., & Chang, C. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acfade · Cited by 22 · see citing works

Towards interpretable imaging genomics analysis: Methodological developments and applications

Cen, X., Dong, W., Lv, W., Zhao, Y. C., Dubee, F., Mentis, A. A., Jovic, D., Yang, H., & Li, Y. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102032 · Cited by 21 · see citing works

Conventional MR-based Preoperative Nomograms for Prediction of IDH/1p19q Subtype in Low-Grade Glioma

Liu, Z., Zhang, T., Jiang, H., Xu, W., & Zhang, J. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.09.022 · Cited by 20 · see citing works

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

Ho, L.-M., Lam, S., Zhang, J., Chiang, C., Chan, A. C. Y., & Cai, J. (2023) · Cancers · doi:10.3390/cancers15041105 · Cited by 19 · see citing works

Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1–2 breast cancer

Wei, W., Ma, Q., Feng, H., Wei, T., Jiang, F., Fan, L., Zhang, W., Xu, J., & Zhang, X. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-1257 · Cited by 19 · see citing works

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

Huang, Z. H., Chen, L., Sun, Y., Liu, Q., & Hu, P. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05018-9 · Cited by 21 · see citing works

Radiogenomics: a key component of precision cancer medicine

Liu, Z.-Q., Duan, T., Zhang, Y.-Y., Weng, S.-Y., Xu, H.-X., Ren, Y.-Q., Zhang, Z.-Y., & Han, X.-W. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02317-8 · Cited by 98 · see citing works

Radiogenomics in breast cancer: a review

Гаранина, А. Е., & Васильевич, Х. А. (2024) · Diagnostic radiology and radiotherapy · doi:10.22328/2079-5343-2024-15-1-15-21 · Cited by 1 · see citing works

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

He, W., Huang, W., Zhang, L., Wu, X.-W., Zhang, S.-X., & Zhang, B. (2024) · MedComm · doi:10.1002/mco2.722 · Cited by 65 · see citing works

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

(2023) · Data Science and Informetrics · doi:10.59494/dsi.2023.4.6

Combining molecular and imaging metrics in cancer: radiogenomics

Gullo, R. L., Naranjo, I. D., Morris, E. A., & Pinker, K. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0795-6 · Cited by 181 · see citing works