Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

• Published in: Cancers Vol. 15; no. 21; p. 5216
• Main Authors: Saleh, Gehad A, Batouty, Nihal M, Gamal, Abdelrahman, Elnakib, Ahmed, Hamdy, Omar, Sharafeldeen, Ahmed, Mahmoud, Ali, Ghazal, Mohammed, Yousaf, Jawad, Alhalabi, Marah, AbouEleneen, Amal, Tolba, Ahmed Elsaid, Elmougy, Samir, Contractor, Sohail, El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Adjuvant treatment; Artificial intelligence; BI-RADS; Biological markers; Biomarkers; Biopsy; Breast cancer; Cancer; Chemotherapy; Deep learning; Diagnosis; Health aspects; Learning algorithms; Machine learning; Magnetic resonance imaging; Malignancy; Mammography; Medical research; molecular imaging; Mutation; Patients; PET-CT; Radiology; Technology application; Ultrasonic imaging; Ultrasound imaging; New York; Canada
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15215216

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists’ proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists’ capabilities and ameliorating patient outcomes in the realm of breast cancer management.