MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer

Factors Associated with Malignant Biopsy Results for Newly Detected Lesions within One Year after Breast Cancer Surgery

RATIONALE AND OBJECTIVES

This study aimed to identify the factors associated with malignant biopsy results for new lesions within one year after breast cancer surgery.

MATERIALS AND METHODS

This retrospective study included 192 lesions from 186 patients who underwent biopsy for newly developed breast lesions within one year of breast cancer surgery. All patients underwent breast ultrasound (US) at 6 months and breast US with mammography one year after surgery. We analyzed the biopsy results, patient age, characteristics of previous cancers (histologic type, stage, molecular subtype, histologic and nuclear grade, Ki-67 index, extensive intraductal component, lymphovascular invasion (LVI)), history of neoadjuvant chemotherapy (NAC), adjuvant therapy, and characteristics of biopsied lesions (location, mode of detection, imaging features, and Breast Imaging Reporting and Data System category). Multivariate logistic regression was performed to predict malignant results after a biopsy of the new lesion in the early postoperative period.

RESULTS

The mean patient age was 49.0 (range, 28-82) years. During follow-up, 137 lesions developed in the ipsilateral remnant breast or mastectomy bed, and 55 lesions developed in the contralateral breast. In total, 37 (19.3%) of the biopsied lesions were malignant, and the following conditions were associated with malignant results in the newly detected lesions: irregularly shaped hypoechoic mass with increased vascularity, presence of previous LVI, history of NAC, and no history of adjuvant radiotherapy or hormone therapy in the indicated patients.

CONCLUSION

Active biopsy may be warranted for new lesions with suspicious imaging findings in the breast or operation bed of patients with LVI, a history of NAC, and no history of adjuvant radiotherapy or hormone therapy, even within one year of breast cancer surgery.

Breast cancer risk prediction using machine learning: a systematic review

Background

Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.

Objective and methods

This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.

Results

A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.

Conclusion

This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.