Validation of a Quantitative Ultrasound Texture Analysis Model for Early Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: A Prospective Serial Imaging Study

• Published in: Cancers Vol. 17; no. 15; p. 2594
• Main Authors: Moore-Palhares, Daniel, Sannachi, Lakshmanan, Chan, Adrian Wai, Dasgupta, Archya, DiCenzo, Daniel, Gandhi, Sonal, Pezo, Rossanna, Eisen, Andrea, Warner, Ellen, Wright, Frances, Look Hong, Nicole, Sadeghi-Naini, Ali, Skarpathiotakis, Mia, Curpen, Belinda, Betel, Carrie, Kolios, Michael C., Trudeau, Maureen, Czarnota, Gregory J.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.08.2025; MDPI
• Subjects: Adjuvant treatment; Artificial intelligence; Breast cancer; Cancer; Cancer therapies; Care and treatment; Chemotherapy; ErbB-2 protein; Estrogen receptors; Fourier transforms; Image processing; Invasiveness; Learning algorithms; Machine learning; Oncology, Experimental; Patients; Radiomics; Response rates; Statistical analysis; Tomography; Trastuzumab; Tumors; Ultrasonic imaging; Ultrasound; Canada; Ontario; quantitative ultrasound; neoadjuvant chemotherapy; precision oncology; radiomics; breast cancer; machine learning

Background/Objectives: Patients with breast cancer who do not achieve a complete response to neoadjuvant chemotherapy (NAC) may benefit from intensified adjuvant systemic therapy. However, such treatment escalation is typically delayed until after tumour resection, which occurs several months into t...