Suchergebnisse - Models and Dynamics of Technology Diffusion

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    Quelle: Advances in Neural Information Processing Systems, 38 (2025-07-03); The Thirty-Ninth Annual Conference on Neural Information Processing Systems, San Diego, United States - California [US-CA], December 2-7, 2025

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    Quelle: Discrete Dynamics in Nature and Society, Vol 2021 (2021)

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    Quelle: The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.
    Houssami N, Macaskill P, von Minckwitz G, Marinovich ML, Mamounas E. Meta-analysis of the association of breast cancer subtype and pathologic complete response to neoadjuvant chemotherapy. Eur J Cancer. 2012 Dec;48(18):3342-54. doi: 10.1016/j.ejca.2012.05.023. Epub 2012 Jul 3.
    Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol. 2011 Jun;35(6):853-60. doi: 10.1097/PAS.0b013e31821a0696.
    Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012 Mar 8;366(10):883-892. doi: 10.1056/NEJMoa1113205.
    Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012 Dec;3(6):573-89. doi: 10.1007/s13244-012-0196-6. Epub 2012 Oct 24.
    Gillies RJ, Schornack PA, Secomb TW, Raghunand N. Causes and effects of heterogeneous perfusion in tumors. Neoplasia. 1999 Aug;1(3):197-207. doi: 10.1038/sj.neo.7900037.
    von Minckwitz G, Rezai M, Loibl S, Fasching PA, Huober J, Tesch H, Bauerfeind I, Hilfrich J, Eidtmann H, Gerber B, Hanusch C, Kuhn T, du Bois A, Blohmer JU, Thomssen C, Dan Costa S, Jackisch C, Kaufmann M, Mehta K, Untch M. Capecitabine in addition to anthracycline- and taxane-based neoadjuvant treatment in patients with primary breast cancer: phase III GeparQuattro study. J Clin Oncol. 2010 Apr 20;28(12):2015-23. doi: 10.1200/JCO.2009.23.8303. Epub 2010 Mar 22.
    Kaufmann M, von Minckwitz G, Bear HD, Buzdar A, McGale P, Bonnefoi H, Colleoni M, Denkert C, Eiermann W, Jackesz R, Makris A, Miller W, Pierga JY, Semiglazov V, Schneeweiss A, Souchon R, Stearns V, Untch M, Loibl S. Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: new perspectives 2006. Ann Oncol. 2007 Dec;18(12):1927-34. doi: 10.1093/annonc/mdm201. Epub 2007 Nov 12.
    Pinker K, Helbich TH, Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol. 2017 Jan;90(1069):20160715. doi: 10.1259/bjr.20160715. Epub 2016 Nov 2.
    Yoon HJ, Kim Y, Chung J, Kim BS. Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging. Breast J. 2019 May;25(3):373-380. doi: 10.1111/tbj.13032. Epub 2018 Mar 30.
    Wang J, Shih TT, Yen RF. Multiparametric Evaluation of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer Using Integrated PET/MR. Clin Nucl Med. 2017 Jul;42(7):506-513. doi: 10.1097/RLU.0000000000001684.
    Erratum: Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters. Clin Nucl Med. 2021 Jun 1;46(6):525. doi: 10.1097/RLU.0000000000003704. No abstract available.
    Lim I, Noh WC, Park J, Park JA, Kim HA, Kim EK, Park KW, Lee SS, You EY, Kim KM, Byun BH, Kim BI, Choi CW, Lim SM. The combination of FDG PET and dynamic contrast-enhanced MRI improves the prediction of disease-free survival in patients with advanced breast cancer after the first cycle of neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2014 Oct;41(10):1852-60. doi: 10.1007/s00259-014-2797-4. Epub 2014 Jun 14.
    Hylton NM, Blume JD, Bernreuter WK, Pisano ED, Rosen MA, Morris EA, Weatherall PT, Lehman CD, Newstead GM, Polin S, Marques HS, Esserman LJ, Schnall MD; ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. Radiology. 2012 Jun;263(3):663-72. doi: 10.1148/radiol.12110748.
    Yuan Y, Chen XS, Liu SY, Shen KW. Accuracy of MRI in prediction of pathologic complete remission in breast cancer after preoperative therapy: a meta-analysis. AJR Am J Roentgenol. 2010 Jul;195(1):260-8. doi: 10.2214/AJR.09.3908.
    Choi JH, Kim HA, Kim W, Lim I, Lee I, Byun BH, Noh WC, Seong MK, Lee SS, Kim BI, Choi CW, Lim SM, Woo SK. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep. 2020 Dec 3;10(1):21149. doi: 10.1038/s41598-020-77875-5.
    Romeo V, Clauser P, Rasul S, Kapetas P, Gibbs P, Baltzer PAT, Hacker M, Woitek R, Helbich TH, Pinker K. AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging. 2022 Jan;49(2):596-608. doi: 10.1007/s00259-021-05492-z. Epub 2021 Aug 10.

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    Quelle: Complexity, Vol 2021 (2021)

    Dateibeschreibung: text/xhtml

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