Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis.
Demi WekkingMichele PorcuPushpamali De SilvaLuca SabaMario ScartozziCinzia SolinasPublished in: Current oncology reports (2023)
Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- lymph node
- polycystic ovary syndrome
- magnetic resonance
- diffusion weighted imaging
- squamous cell carcinoma
- coronary artery disease
- cardiovascular disease
- electronic health record
- machine learning
- pregnant women
- breast cancer risk
- deep learning
- young adults
- radiation therapy
- adipose tissue
- cardiovascular events
- locally advanced
- insulin resistance