Assessing the malignancy of suspicious breast microcalcifications: the role of contrast enhanced mammography.

PURPOSE: To assess the role of contrast-enhanced mammography (CEM) in predicting the malignancy of breast calcifications. MATERIAL AND METHODS: We retrospectively evaluated patients with suspicious calcifications (BIRADS 4) who underwent CEM and stereotactic vacuum-assisted biopsy (VAB) at our institution. We assessed the sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of CEM in predicting malignancy of microcalcifications with a 95% confidence interval; we performed an overall analysis and a subgroup analysis stratified into group A-low risk (BIRADS 4a) and group B-medium/high risk (BIRADS 4b-4c). We then evaluated the correlation between enhancement and tumour proliferation index (Ki-67) for all malignant lesions. RESULTS: Data from 182 patients with 184 lesions were collected. Overall the SE of CEM in predicting the malignancy of microcalcifications was 0.70, SP was 0.85, the PPV was 0.82, the NPV was 0.76 and AUC was 0.78. SE in group A was 0.89, SP was 0.89, PPV was 0.57, NPV was 0.98 and AUC was 0.75. SE in group B was 0.68, SP was 0.80, PPV was 0.87, NPV was 0.57 and AUC was 0.75. Among malignant microcalcifications that showed enhancement (N = 52), 61.5% had Ki-67 ≥ 20% and 38.5% had low Ki-67 values. Among the lesions that did not show enhancement (N = 22), 90.9% had Ki-67 < 20% and 9.1% showed high Ki-67 values 20%. CONCLUSIONS: The absence of enhancement can be used as an indicative parameter for the absence of disease in cases of low-suspicious microcalcifications, but not in intermediate-high suspicious ones for which biopsy remains mandatory and can be used to distinguish indolent lesions from more aggressive neoplasms, with consequent reduction of overdiagnosis and overtreatment.

Exploring the Potential of Artificial Intelligence in Breast Ultrasound.

Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient's care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.

Magnetic Localization of Breast Lesions: A Large-Scale European Evaluation in a National Cancer Institute.

INTRODUCTION: For decades the standard for preoperative breast lesions' localization has been wire localization. In recent years the options for nonwired localization have significantly expanded and include radioactive seeds, radar reflectors, radiofrequency identification tags and magnetic seeds. The aim of our study is to evaluate on a large scale the performance of preoperative magnetic seed localization of nonpalpable breast lesions. MATERIAL AND METHODS: We prospectively collected data on all patients undergoing image-guided magnetic seed localization from September 2019 to December 2022. We analyzed imaging findings, histological results, and type of surgery. The primary outcome was the successful localization rate. Secondary outcomes were the successful placement rate, the ease of percutaneous positioning, the procedural complications, and the reintervention rate. RESULTS: A total of 1123 magnetic seeds were placed in 1084 patients by 4 radiologists under ultrasound (1053) or stereotactic (70) guidance. All seeds were detectable transcutaneously in all breasts sizes and at all depths by 7 surgeons with a success rate of 100%. A total of 97.5% seeds were correctly placed into the target lesions (only 2.5% were dislocated). All radiologists have shown good compliance during the procedure, and there were no complications or safety issues. The reoperation rate was 5.1%. CONCLUSIONS: Image-guided localization with magnetic seeds is an easy, safe, reliable, and effective method for localizing nonpalpable breast lesions. Both radiologists and surgeons agreed that the technology was intuitive to use and that it can be widely applied in preoperative localization in breast units.