| Literature DB >> 31444031 |
David Coronado-Gutiérrez1, Gorane Santamaría2, Sergi Ganau2, Xavier Bargalló2, Stefania Orlando3, M Eulalia Oliva-Brañas3, Alvaro Perez-Moreno4, Xavier P Burgos-Artizzu5.
Abstract
This study aimed to assess the potential of state-of-the-art ultrasound analysis techniques to non-invasively diagnose axillary lymph nodes involvement in breast cancer. After exclusion criteria, 105 patients were selected from two different hospitals. The 118 lymph node ultrasound images taken from these patients were divided into 53 cases and 65 controls, which made up the study series. The clinical outcome of each node was verified by ultrasound-guided fine needle aspiration, core needle biopsy or surgical biopsy. The achieved accuracy of the proposed method was 86.4%, with 84.9% sensitivity and 87.7% specificity. When tested on breast cancer patients only, the proposed method improved the accuracy of the sonographic assessment of axillary lymph nodes performed by expert radiologists by 9% (87.0% vs 77.9%). In conclusion, the results demonstrate the potential of ultrasound image analysis to detect the microstructural and compositional changes that occur in lymph nodes because of metastatic involvement.Entities:
Keywords: Axillary lymph node; Breast cancer; Cancer metastasis; Deep learning; Image analysis; Image biomarker; Machine learning; Quantitative ultrasound; Ultrasound
Mesh:
Year: 2019 PMID: 31444031 DOI: 10.1016/j.ultrasmedbio.2019.07.413
Source DB: PubMed Journal: Ultrasound Med Biol ISSN: 0301-5629 Impact factor: 2.998