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Journal Abstract Search

322 related items for PubMed ID: 35323359

  • 1. Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.
    Fusco R, Di Bernardo E, Piccirillo A, Rubulotta MR, Petrosino T, Barretta ML, Mattace Raso M, Vallone P, Raiano C, Di Giacomo R, Siani C, Avino F, Scognamiglio G, Di Bonito M, Granata V, Petrillo A.
    Curr Oncol; 2022 Mar 13; 29(3):1947-1966. PubMed ID: 35323359
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  • 3. Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography.
    Petrillo A, Fusco R, Di Bernardo E, Petrosino T, Barretta ML, Porto A, Granata V, Di Bonito M, Fanizzi A, Massafra R, Petruzzellis N, Arezzo F, Boldrini L, La Forgia D.
    Cancers (Basel); 2022 Apr 25; 14(9):. PubMed ID: 35565261
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  • 7. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
    Milenković J, Hertl K, Košir A, Zibert J, Tasič JF.
    Artif Intell Med; 2013 Jun 25; 58(2):101-14. PubMed ID: 23548472
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  • 8. A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.
    Petrillo A, Fusco R, Petrosino T, Vallone P, Granata V, Rubulotta MR, Pariante P, Raiano N, Scognamiglio G, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Sorgente E, Pecori B, Cerciello V, Boldrini L.
    Radiol Med; 2024 Jun 25; 129(6):864-878. PubMed ID: 38755477
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  • 9. Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol.
    Milenković J, Dalmış MU, Žgajnar J, Platel B.
    Med Phys; 2017 Sep 25; 44(9):4652-4664. PubMed ID: 28622412
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  • 10. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI.
    Yuan Y, Giger ML, Li H, Bhooshan N, Sennett CA.
    Acad Radiol; 2010 Sep 25; 17(9):1158-67. PubMed ID: 20692620
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  • 12. Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.
    Maiti S, Nayak S, Hebbar KD, Pendem S.
    F1000Res; 2024 Sep 25; 13():91. PubMed ID: 38571894
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  • 14. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
    Ji Y, Li H, Edwards AV, Papaioannou J, Ma W, Liu P, Giger ML.
    Cancer Imaging; 2019 Sep 18; 19(1):64. PubMed ID: 31533838
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  • 18. Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.
    Zhang X, Teng X, Zhang J, Lai Q, Cai J.
    Breast Cancer Res; 2024 May 14; 26(1):77. PubMed ID: 38745321
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  • 19. Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.
    Meng M, Zhang M, Shen D, He G.
    Medicine (Baltimore); 2022 Nov 11; 101(45):e31214. PubMed ID: 36397422
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  • 20. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.
    Agner SC, Soman S, Libfeld E, McDonald M, Thomas K, Englander S, Rosen MA, Chin D, Nosher J, Madabhushi A.
    J Digit Imaging; 2011 Jun 11; 24(3):446-63. PubMed ID: 20508965
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