Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer

• Published in: Frontiers in endocrinology (Lausanne) Vol. 16; p. 1548888
• Main Authors: Wang, Si-Rui, Tian, Feng, Zhu, Tong, Cao, Chun-Li, Wang, Jin-Li, Li, Wen-Xiao, Li, Jun, Hou, Ji-Xue
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 27.02.2025
• Subjects: Adult; Aged; Axilla; axillary lymph nodes burden; breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Endocrinology; Female; Humans; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic Metastasis - diagnostic imaging; Machine Learning; Middle Aged; Nomograms; Radiomics; Retrospective Studies; Ultrasonography - methods; ultrasound; radiomics; axillary lymph nodes burden; breast cancer; machine learning; ultrasound

This study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients. A total of 131 breast cancer patients with axillary lymph node metastasis (ALNM) were enrol...