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Zukünftige Entwicklungen in der Bildgebung

  • Gisela Anton
  • Pascal Baltzer
  • Julius Emons
  • Peter Andreas Fasching
  • Rüdiger Schulz-Wendtland
  • Christian Weismann

Zusammenfassung

Die Zukunft technischer Entwicklungen vorhersagen zu wollen ist immer auf einer subjektiven Einschätzung begründet und daher naturgemäß schwierig. Um die Zukunft der Bildgebung abschätzen zu können, wird jeweils die aktuelle Situation beleuchtet und davon ausgehend mögliche Entwicklungen dargestellt. Dabei ist ein zentraler Punkt die heute mithilfe von Computersystemen erhobene umfangreiche Menge von Patientendaten, die mit bestehenden Techniken neu verknüpft werden.

Literatur

Literatur zu Abschn. 10.1

  1. AWMF (2012) Interdisziplinäre S3-Leitlinie für die Diagnostik, herapie und Nachsorge des Mammakarzinoms. AWMF-Register Nr. 032-045 OLGoogle Scholar
  2. Baltzer P A T, Yang F, Dietzel M, Herzog A, Simon A, Vag T et al (2010) Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-Mammography considering 974 histologically verified lesions. Breast J 16(3):233–239Google Scholar
  3. Boyd D, (2015) Privacy and Publicity in the Context of Big Data [Online] Verfügbar unter: http://www.danah.org/papers/talks/2010/WWW2010.html [Zugegriffen: 30-Dez-2015]
  4. Dietzel M, Baltzer P A T, Vag T, Gröschel T, Gajda M, Camara O, Kaiser W A (2010) Application of breast MRI for prediction of lymph node metastases – systematic approach using 17 individual descriptors and a dedicated decision tree. Acta Radiol 51(8):885-894Google Scholar
  5. Dietzel M, Baltzer P A, Vag T, Zoubi R, Gröschel T, Burmeister H, Gajda M et al (2011) Potential of MR mammography to predict tumor grading of invasive breast cancer. RoFo 183( 9):826–833Google Scholar
  6. Eccles S A, Aboagye E O, Ali S, Anderson A S, Armes J, et al (2013) Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer, Breast Cancer Res BCR, 15(5)R92Google Scholar
  7. Guestini F, McNamara K M, Ishida T, Sasano H (2015) Triple Negative Breast Cancer Chemosensitivity and Chemoresistance: Current Advances in Biomarkers Indentification, Expert Opin Ther TargetsGoogle Scholar
  8. Hood L, Friend S H (2011) Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 8(3):184–187Google Scholar
  9. Leskovec J, Rajaraman A, Ullman J D (2014) Mining of Massive Datasets, 2 Aufl Cambridge, Cambridge University PressGoogle Scholar
  10. Nienhuis H H, Gaykema S B M, Timmer-Bosscha H, Jalving M, Brouwers A H et al (2015) Targeting breast cancer through its microenvironment: current status of preclinical and clinical research in finding relevant targets. Pharmacol Ther 147:63–79Google Scholar
  11. Pinker K, Helbich T, Magometschnigg H, Fueger B, Baltzer P (2014) Molecular breast imaging : An update. Radiol 54(3):241–253Google Scholar

Literatur zu Abschn. 10.2

  1. Baselga J et al (2015) PIK3CA Status in Circulating Tumor DNA Predicts Efficacy of Buparlisib Plus Fulvestrant in Postmenopausal Women With Endocrine-resistant HR+/HER2– Advanced Breast Cancer: First Results From the Randomized, Phase III BELLE-2 Trial. San Antonio Breast Cancer Symposium SS 6–01Google Scholar
  2. Bundesministerium für Bildung und Forschung (2015) Begleitende Evaluierung des Förderinstruments »Spitzencluster-Wettbewerb« des BMBF. http://www.rwi-essen.de/media/content/pages/publikationen/rwi-projektberichte/RWI-PB_Spitzencluster.pdf
  3. Bundesministerium für Wirtschaft und Energie (2014) »Smart Data« – Projekte des neuen BMWi-Technologieprogramms sind ausgewählt, http://www.bmwi.de/DE/Presse/pressemitteilungen,did=642872.html
  4. Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70Google Scholar
  5. Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474(7353):609–615Google Scholar
  6. Cancer Genome Atlas Research Network et al (2013) Integrated genomic characterization of endometrial carcinoma. Nature 497(7447):67–73Google Scholar
  7. Cheang MC et al (2009) Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101(10):736–750Google Scholar
  8. Cortazar P et al (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938):164–172Google Scholar
  9. Couch FJ et al (2015) Inherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer. J Clin Oncol 33(4):304–311Google Scholar
  10. Fasching PA et al (2012) The role of genetic breast cancer susceptibility variants as prognostic factors. Hum Mol Genet 21(17):3926–3939Google Scholar
  11. Fasching PA et al (2013) Breast Cancer Risk – From Genetics to Molecular Understanding of Pathogenesis. Geburtshilfe Frauenheilkd 73(12):1228–1235Google Scholar
  12. Google Inc. (2012) Introducing the Knowledge Graph: things, not strings. http://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html
  13. Google Inc. (2015) A remedy for your health-related questions: health info in the Knowledge Graph http://googleblog.blogspot.nl/2015/02/health-info-knowledge-graph.html
  14. Häberle L et al (2012) Characterizing mammographic images by using generic texture features. Breast Cancer Res 14(2):R59Google Scholar
  15. Häberle L et al (2016) Mammographic density is the main correlate of tumors detected on ultrasound but not on mammography. Int J Cancer 139(9):1967–1974Google Scholar
  16. Katzorke N et al (2013) Prognostic value of HER2 on breast cancer survival. J Clin Oncol 31 (suppl; abstr 640)Google Scholar
  17. Kojima Y, Tsunoda H (2011) Mammography and ultrasound features of triple-negative breast cancer. Breast Cancer 18(3):146–151Google Scholar
  18. Kojima Y et al (2011) Radiographic features for triple negative ductal carcinoma in situ of the breast. Breast Cancer 18(3):213–220Google Scholar
  19. Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921Google Scholar
  20. Mavaddat N et al (2015) Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst 107(5). pii: djv036. doi:  10.1093/jnci/djv036
  21. Meier-Meitinger M et al (2012) Accuracy of radiological tumour size assessment and the risk for re-excision in a cohort of primary breast cancer patients. Eur J Surg Oncol 44–51Google Scholar
  22. Nolan E et al (2016) RANK ligand as a potential target for breast cancer prevention in BRCA1-mutation carriers. Nat Med 22(8):933–939Google Scholar
  23. Perou CM et al (2000) Molecular portraits of human breast tumours. Nature 747–52Google Scholar
  24. Purrington KS et al (2014) Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer. Carcinogenesis 1012–9Google Scholar
  25. Sigl V et al (2016) RANKL/RANK control Brca1 mutation-driven mammary tumors. Cell Res 761–74Google Scholar
  26. Sotiriou C, Pusztai L (2009) Gene-expression signatures in breast cancer. N Engl J Med 790–800Google Scholar
  27. Vachon CM et al (2015) The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst 107(5). pii: dju397. doi:  10.1093/jnci/dju397
  28. Venter JC et al (2001) The sequence of the human genome. Science 1304–51Google Scholar
  29. Yaghjyan L et al (2011) Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to tumor characteristics. J Natl Cancer Inst 1179–89Google Scholar

Literatur zu Abschn. 10.4

  1. Anton G, Bayer F, Beckmann M W, Durst J, Fasching P A, Haas W et al (2013) Grating-based dark-field imaging of human breast tissue; ZMP 23, 228Google Scholar
  2. Bech M, Jensen TH, Feidenhaus R, Bunk O, David C, Pfeiffer F (2009) Soft-tissue phase-contrast tomography with an x-ray tube source; Phys Med Biol 54:2747Google Scholar
  3. David C, Nöhammer B, Solak H H, Ziegler E (2002) Differential x-ray phase contrast imaging using a shearing interferometer; Applied Physics Letters 81(17):3287–3289Google Scholar
  4. Donath T, Pfeiffer F, Bunk O, Grünzweig C, Hempel E, Popescu S, Vock P, David C (2010) Toward clinical x-ray phase-contrast ct: demonstration of enhanced soft-tissue contrast in human specimen; Investigative radiology 45(7):445–452Google Scholar
  5. Michel T, Rieger J, Anton G, Bayer F, Beckmann M W, Durst J et al (2013) On a dark-field signal generated by micrometer sized calcifications in phase-contrast mammography; Phys Med Biol 58:2713Google Scholar
  6. Momose A, Kawamoto S, Koyama I, Hamaishi Y, Takai K, Suzuki Y (2003) Demonstration of x-ray talbot interferometry; Jpn J Appl Phys 42( 2;7B)L866–L868Google Scholar
  7. Pelzer G, Zang A, Anton G, Bayer F, Horn F, Kraus FM et al (2014) Energy weighted x-ray dark-field imaging; Optics Express 22(20):24507–24515Google Scholar
  8. Pfeiffer F, Bech M, Bunk O, Kraft P, Eikenberry E F, Bronnimann C, Grunzweig C, David C (2008) Hard-x-ray dark-field imaging using a grating interferometer; Nat Mater 7(2):134–137Google Scholar
  9. Pfeiffer F, Bunk O, David C, Bech M, Le Duc G, Bravin A, Cloetens P (2007) Highresolution brain tumor visualization using three-dimensional x-ray phase contrast tomography; Physics in medicine and biology 52(23):6923Google Scholar
  10. Pfeiffer F, Weitkamp T, Bunk O, David C (2006) Phase retrieval and differential phasecontrast imaging with low-brilliance x-ray sources; Nature Phys 2:258–261Google Scholar
  11. Pinzer B, Cacquevel M, Modregger P, McDonald S, Bensadoun J, Thuering T, Aebischer P, Stampanoni M (2012) Imaging brain amyloid deposition using grating-based differential phase contrast tomography; Neuroimage 61:1336–1346Google Scholar
  12. Reznikova E, Mohr J, Boerner M, Nazmov V, Jakobs P (2008) Soft X-ray lithography of high aspect ratio SU8 submicron structures; Microsystem Technologies 14(9):1683–1688Google Scholar
  13. Schleede S, Meinel F G, Bech M, Herzen J, Achterhold K, Potdevin G et al (2012) Emphysema diagnosis using x-ray dark-field imaging at a laser-driven compact synchrotron light source; Proceedings of the National Academy of Sciences (PNAS) 109(44):17880–17885Google Scholar
  14. Shinohara M, Yamashita T, Tawa H, Takeda M, Sasaki N, Takaya T et al (2008) Atherosclerotic plaque imaging using phase-contrast x-ray computed tomography; American Journal of Physiology-Heart and Circulatory Physiology 294(2):H1094–H1100Google Scholar
  15. Stutman D, Beck T J, Carrino J A, Bingham C O (2011) Talbot phase-contrast x-ray imaging for the small joints of the hand; Phys Med Biol 56(17):5697Google Scholar
  16. Takeda M, Yamashita T, Shinohara M, Sasaki N, Tawa H, Nakajima K, Momose A, Hirata K-J (2012) Beneficial effect of anti-platelet therapies on atherosclerotic lesion formation assessed by phaseontrast x-ray ct imaging; The international journal of cardiovascular imaging 28(5):1181– 1191Google Scholar
  17. Weitkamp T, David C, Bunk O, Bruder J, Cloetens P, Pfeiffer F (2008) X-ray phase radiography and tomography of soft tissue using grating interferometry; Eur J Radiology 68S:S13–S17Google Scholar
  18. Yaroshenko A, Meinel F G, Bech M, Tapfer A, Velroyen A, Schleede S et al (2013) Pulmonary emphysema diagnosis with a preclinical small-animal x-ray dark-field scatter-contrast scanner; Radiology 269:427–433Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  • Gisela Anton
    • 1
  • Pascal Baltzer
    • 2
  • Julius Emons
    • 3
  • Peter Andreas Fasching
    • 3
  • Rüdiger Schulz-Wendtland
    • 4
  • Christian Weismann
    • 5
  1. 1.Lehrstuhl für Experimentalphysik (Teilchen- und Astroteilchenphysik)Universität Erlangen-NürnbergErlangen
  2. 2.Klinische Abteilung für Allgemeine Radiologie und KinderradiologieUniversitätsklinik für Radiologie und Nuklearmedizin Allgemeines KrankenhausWienÖsterreich
  3. 3.FrauenklinikUniversitätsklinikum ErlangenErlangen
  4. 4.Radiologisches Institut/Gynäkologische RadiologieUniversitätsklinikum ErlangenErlangen
  5. 5.Universitäts-Institut für RadiologieLKH SalzburgSalzburgÖsterreich

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