FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4319346673> ?p ?o ?g. }

• W4319346673 abstract "HomeRadiologyVol. 306, No. 3 PreviousNext Reviews and CommentaryEditorial–Centennial ContentWhat the Future Holds for the Screening, Diagnosis, and Treatment of Breast CancerChristiane K. Kuhl Christiane K. Kuhl Author AffiliationsFrom the Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Pauwelsstr 30, 52074 Aachen, RWTH, Germany.Address correspondence to the author (email: [email protected]).Christiane K. Kuhl Published Online:Feb 7 2023https://doi.org/10.1148/radiol.223338MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. American Cancer Society. Cancer Facts and Figures 2022. Atlanta, Ga: American Cancer Society; 2022. https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html. Accessed November 30, 2022. Google Scholar2. Breast cancer burden in EU-27. ECIS – European Cancer Information System. https://ecis.jrc.ec.europa.eu/pdf/Breast_cancer_factsheet-Oct_2020.pdf. Accessed November 30, 2022. Google Scholar3. Adami HO, Kalager M, Valdimarsdottir U, Bretthauer M, Ioannidis JPA. Time to abandon early detection cancer screening. Eur J Clin Invest 2019;49(3):e13062. Crossref, Medline, Google Scholar4. Greaves M, Maley CC. Clonal evolution in cancer. Nature 2012;481(7381):306–313. Crossref, Medline, Google Scholar5. Wanders AJT, Mees W, Bun PAM, et al. Interval cancer detection using a neural network and breast density in women with negative screening mammograms. Radiology 2022;303(2):269–275. Link, Google Scholar6. Yala A, Mikhael PG, Strand F, et al. Multi-institutional validation of a mammography-based breast cancer risk model. J Clin Oncol 2022;40(16):1732–1740. Crossref, Medline, Google Scholar7. Lehman CD, Mercaldo S, Lamb LR, et al. Deep learning vs traditional breast cancer risk models to support risk-based mammography screening. J Natl Cancer Inst 2022;114(10):1355–1363. Crossref, Medline, Google Scholar8. Arasu VA, Miglioretti DL, Sprague BL, et al. Population-based assessment of the association between magnetic resonance imaging background parenchymal enhancement and future primary breast cancer risk. J Clin Oncol 2019;37(12):954–963. Crossref, Medline, Google Scholar9. Schrag D, McDonnell CH 3rd, Nadauld L, et al. A prospective study of a multi-cancer early detection blood test. ESMO Abstract 9030. Ann Oncol 2022;33(suppl_7):S417–S426. https://oncologypro.esmo.org/meeting-resources/esmo-congress/a-prospective-study-of-a-multi-cancer-early-detection-blood-test. Google Scholar10. Swanton C, Neal RD, Johnson PWM, et al. NHS-Galleri trial design: equitable study recruitment tactics for targeted population-level screening with a multi-cancer early detection (MCED) test. 2022 ASCO Annual Meeting, Abstract TPS6606. J Clin Oncol 2022;40:(16_suppl):TPS6606. Google Scholar11. Jochelson MS, Lobbes MBI. Contrast-enhanced mammography: state of the art. Radiology 2021;299(1):36–48. Link, Google Scholar12. Kuhl CK. Abbreviated magnetic resonance imaging (MRI) for breast cancer screening: rationale, concept, and transfer to clinical practice. Annu Rev Med 2019;70(1):501–519. Crossref, Medline, Google Scholar13. Bakker MF, de Lange SV, Pijnappel RM, et al. Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 2019;381(22):2091–2102. Crossref, Medline, Google Scholar14. Lawson M. Comparative Performance of Contrast Enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening. 108th Scientific Assembly and Annual Meeting of the Radiological Society of North America, Abstract S2-SSBR01-3. https://eppro02.ativ.me/web/page.php?page=IntHtml&project=RSNA22&id=314. Google Scholar15. Mann RM, Veldhuis WB. Contrast-enhanced mammography: moving ahead with perfusion imaging. Radiology 2022;305(1):104–106. Link, Google Scholar16. Kuhl C. Efficacy and Safety of Gadopiclenol for Body Magnetic Resonance Imaging (MRI): The PROMISE Trial. Abstract S5-SSMS01-6; Sun, Nov 27,108th Scientific Assembly and Annual Meeting of the Radiological Society of North America, 2022. https://eppro02.ativ.me/web/page.php?page=IntHtml&project=RSNA22&id=1973. Google Scholar17. Boehm-Sturm P, Haeckel A, Hauptmann R, Mueller S, Kuhl CK, Schellenberger EA. Low-molecular-weight iron chelates may be an alternative to gadolinium-based contrast agents for T1-weighted contrast-enhanced MR imaging. Radiology 2018;286(2):537–546. Link, Google Scholar18. Luo H, Zhang T, Gong NJ, et al. Deep learning-based methods may minimize GBCA dosage in brain MRI. Eur Radiol 2021;31(9):6419–6428. Crossref, Medline, Google Scholar19. Chen Y, Panda A, Pahwa S, et al. Three-dimensional MR fingerprinting for quantitative breast imaging. Radiology 2019;290(1):33–40. Link, Google Scholar20. Baltzer P, Mann RM, Iima M, et al. Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2020;30(3):1436–1450. Crossref, Medline, Google Scholar21. Fisher B, Anderson S, Bryant J, et al. Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med 2002;347(16):1233–1241. Crossref, Medline, Google Scholar22. Williams L, Taylor K, Cameron DA, et al. Randomised controlled trial of breast conserving therapy: 30 year analysis of the Scottish breast conservation trial. Abstract at the 13th European Breast Cancer Conference EBCC, 16-November 18, 2022, Barcelona, Spain, Book of Abstracts Published in European Journal of Cancer, 175, S1. https://www.ejcancer.com/article/S0959-8049(22)01352-1/fulltext. Accessed November 30, 2022. Google Scholar23. Mann B, Rose A, Hughes J, et al. Primary results of ANZ 1002: Post-operative radiotherapy omission in selected patients with early breast cancer trial (PROSPECT) following pre-operative breast MRI. Abstract 572, Poster 52, ASCO Annual Meeting 2022. https://ascopubs.org/doi/pdf/10.1200/JCO.2022.40.16_suppl.572?role=tab.Accessed November 30, 2022. Google Scholar24. Kuhl CK, Lehman C, Bedrosian I. Imaging in locoregional management of breast cancer. J Clin Oncol 2020;38(20):2351–2361. Crossref, Medline, Google Scholar25. Schrading S, Strobel K, Keulers A, Dirrichs T, Kuhl CK. Safety and efficacy of magnetic resonance-guided vacuum-assisted large-volume breast biopsy (MR-guided VALB). Invest Radiol 2017;52(3):186–193. Crossref, Medline, Google Scholar26. Roknsharifi S, Wattamwar K, Fishman MDC, et al. Image-guided microinvasive percutaneous treatment of breast lesions: where do we stand? RadioGraphics 2021;41(4):945–966. Link, Google Scholar27. Soysal SD, Tzankov A, Muenst SE. Role of the tumor microenvironment in breast cancer. Pathobiology 2015;82(3-4):142–152. Crossref, Medline, Google Scholar28. Pinker K, Helbich TH, Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol 2017;90(1069):20160715. [Published correction appears in Br J Radiol 2017;90(1072):20160715e.] Crossref, Medline, Google Scholar29. The HYPMED project. https://www.eibir.org/projects/hypmed/.Accessed November 30, 2022. Google Scholar30. Ashraf AB, Daye D, Gavenonis S, et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 2014;272(2):374–384. Link, Google Scholar31. Nadig V, Herrmann K, Mottaghy FM, Schulz V. Hybrid total-body pet scanners-current status and future perspectives. Eur J Nucl Med Mol Imaging 2022;49(2):445–459. Crossref, Medline, Google Scholar32. Tranberg KG. Local destruction of tumors and systemic immune effects. Front Oncol 2021;11:708810. Crossref, Medline, Google Scholar33. Beck JD, Reidenbach D, Salomon N, et al. mRNA therapeutics in cancer immunotherapy. Mol Cancer 2021;20(1):69. Crossref, Medline, Google Scholar34. Andrews M. Supplemental breast cancer screening isn’t right for all women, experts say. Cable News Network. https://edition.cnn.com/2022/10/27/health/breast-cancer-screening-khn-partner/index.html. Accessed November 30, 2022. Google Scholar35. van Zelst JCM, Vreemann S, Witt HJ, et al. Multireader study on the diagnostic accuracy of ultrafast breast magnetic resonance imaging for breast cancer screening. Invest Radiol 2018;53(10):579–586. Crossref, Medline, Google Scholar36. Moy L. Change Is Good: The Evolution and Future of Breast Imaging. Radiology 2023. https://doi.org/10.1148/radiol.230018. Published online February 7, 2023. Link, Google ScholarArticle HistoryReceived: Jan 2 2023Revision requested: Jan 6 2023Revision received: Jan 9 2023Accepted: Jan 11 2023Published online: Feb 07 2023 FiguresReferencesRelatedDetailsCited ByChange Is Good: The Evolution and Future of Breast ImagingLinda Moy, 7 February 2023 | Radiology, Vol. 306, No. 3Accompanying This ArticleThe Future of Breast imagingApr 25 2023Default Digital Object SeriesRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 306, No. 3 PodcastMetrics Altmetric Score PDF download" @default.
• W4319346673 created "2023-02-08" @default.
• W4319346673 creator A5070612619 @default.
• W4319346673 date "2023-03-01" @default.
• W4319346673 modified "2023-10-01" @default.
• W4319346673 title "What the Future Holds for the Screening, Diagnosis, and Treatment of Breast Cancer" @default.
• W4319346673 cites W1488814076 @default.
• W4319346673 cites W2023560061 @default.
• W4319346673 cites W2067420088 @default.
• W4319346673 cites W2123932528 @default.
• W4319346673 cites W2548436343 @default.
• W4319346673 cites W2556605429 @default.
• W4319346673 cites W2753466980 @default.
• W4319346673 cites W2810654885 @default.
• W4319346673 cites W2899387377 @default.
• W4319346673 cites W2904258135 @default.
• W4319346673 cites W2911194665 @default.
• W4319346673 cites W2913819571 @default.
• W4319346673 cites W2991278755 @default.
• W4319346673 cites W2991330623 @default.
• W4319346673 cites W3027338859 @default.
• W4319346673 cites W3134701770 @default.
• W4319346673 cites W3137651818 @default.
• W4319346673 cites W3156114568 @default.
• W4319346673 cites W3174307091 @default.
• W4319346673 cites W3181947413 @default.
• W4319346673 cites W3206336448 @default.
• W4319346673 cites W3213232584 @default.
• W4319346673 cites W4210867352 @default.
• W4319346673 cites W4281771310 @default.
• W4319346673 cites W4287836345 @default.
• W4319346673 cites W4319347486 @default.
• W4319346673 doi "https://doi.org/10.1148/radiol.223338" @default.
• W4319346673 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36802999" @default.
• W4319346673 hasPublicationYear "2023" @default.
• W4319346673 type Work @default.
• W4319346673 citedByCount "3" @default.
• W4319346673 countsByYear W43193466732023 @default.
• W4319346673 crossrefType "journal-article" @default.
• W4319346673 hasAuthorship W4319346673A5070612619 @default.
• W4319346673 hasConcept C121608353 @default.
• W4319346673 hasConcept C126322002 @default.
• W4319346673 hasConcept C143998085 @default.
• W4319346673 hasConcept C19527891 @default.
• W4319346673 hasConcept C29456083 @default.
• W4319346673 hasConcept C530470458 @default.
• W4319346673 hasConcept C71924100 @default.
• W4319346673 hasConceptScore W4319346673C121608353 @default.
• W4319346673 hasConceptScore W4319346673C126322002 @default.
• W4319346673 hasConceptScore W4319346673C143998085 @default.
• W4319346673 hasConceptScore W4319346673C19527891 @default.
• W4319346673 hasConceptScore W4319346673C29456083 @default.
• W4319346673 hasConceptScore W4319346673C530470458 @default.
• W4319346673 hasConceptScore W4319346673C71924100 @default.
• W4319346673 hasIssue "3" @default.
• W4319346673 hasLocation W43193466731 @default.
• W4319346673 hasLocation W43193466732 @default.
• W4319346673 hasOpenAccess W4319346673 @default.
• W4319346673 hasPrimaryLocation W43193466731 @default.
• W4319346673 hasRelatedWork W1595863544 @default.
• W4319346673 hasRelatedWork W2014447844 @default.
• W4319346673 hasRelatedWork W2113427382 @default.
• W4319346673 hasRelatedWork W2224319365 @default.
• W4319346673 hasRelatedWork W2297592050 @default.
• W4319346673 hasRelatedWork W2989121736 @default.
• W4319346673 hasRelatedWork W3130591221 @default.
• W4319346673 hasRelatedWork W3196332669 @default.
• W4319346673 hasRelatedWork W4286448049 @default.
• W4319346673 hasRelatedWork W4365129602 @default.
• W4319346673 hasVolume "306" @default.
• W4319346673 isParatext "false" @default.
• W4319346673 isRetracted "false" @default.
• W4319346673 workType "article" @default.