A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: Integration of Multimodal Radiomics With Clinical and Molecular Subtype Markers

• Published in: IEEE access Vol. 12; pp. 104983 - 105003
• Main Authors: Gamal, Abdelrahman, Sharafeldeen, Ahmed, Alnaghy, Eman, Alghandour, Reham, Saleh Alghamdi, Norah, Ali, Khadiga M., Shamaa, Sameh, Aboueleneen, Amal, Elsaid Tolba, Ahmed, Elmougy, Samir, Ghazal, Mohammed, Contractor, Sohail, El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Breast Cancer; Chemotherapy; DW-MRI; Feature extraction; Genetic algorithms; Health services; Image enhancement; Machine learning; Magnetic resonance imaging; Medical imaging; Medical treatment; MRI; Neoadjuvant Chemotherapy; Pathology; Radiomics; Systems design; Texture; Treatment Response Prediction; Tumor Clinical Markers; Tumors

The primary objective of this paper is to develop a machine learning-based approach capable of predicting the treatment response of neoadjuvant chemotherapy (NAC) to enhance breast cancer treatment management. The proposed system aims to predict NAC outcomes across three categories: pathological com...