The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast composition category ACR BI-RADS c–d at DM and an abnormal finding (BI-RADS 3-4-5) at DM and/or BUS were considered. During CEM, a nonionic iodinated contrast agent (Iohexol 350 mg I/mL, 1.5 mL/kg) was power-injected intravenously. Images were evaluated independently by two breast radiologists. Findings classified as BI-RADS 1–3 were considered benign, while BI-RADS 4–5 were considered malignant. In case of discrepancies, the higher category was considered for DM+BUS. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, using histology/≥12-month follow-up as gold standards. In total, 51 patients with 65 breast lesions were included. 59 (90.7%) abnormal findings were detected at DM+BUS, and 65 (100%) at CEM. The inter-reader agreement was excellent (Cohen’s k = 0.87 for DM+BUS and 0.97 for CEM). CEM showed a 93.5% sensitivity (vs. 90.3% for DM+BUS), a 79.4–82.4% specificity (vs. 32.4–35.5% for DM+BUS) (McNemar p = 0.006), a 80.6–82.9% PPV (vs. 54.9–56.0% for DM+BUS), a 93.1–93.3% NPV (vs. 78.6–80.0% for DM+BUS), and a 86.1–87.7% accuracy (vs. 60.0–61.5% for DM+BUS). The AUC was higher for CEM than for DM+BUS (0.865 vs. 0.613 for Reader 1, and 0.880 vs. 0.628, for Reader 2) (p < 0.001). In conclusion, CEM had a better diagnostic performance than DM and BUS alone and combined together in patients with dense breasts.

Cryoablation is a minimally invasive technique currently employed in breast cancer care, that uses freeze and thaw cycles to treat benign breast lesions, small breast cancers or focal sites of metastatic disease in patients not eligible for surgery. The final goal of this procedure is to destroy breast cancer cells using extreme cold. In addition, several studies have shown that this technique seems to have an enhancing effect on the immune response, especially by increasing the expression of tumor neoantigens specific to tumor cells, which are then attacked and destroyed. Exploiting this effect, cryoablation in combination with immunotherapy could be the key to treating early-stage breast cancers or patients who are unsuitable for surgery. According to some recent studies, there are other potential tools that could be used to enhance the therapeutic effect of cryoablation, such as FE3O4 nanoparticles or the manipulation of aquaporin expression. The aim of this narrative review is to summarize the current evidence regarding the use, indications, advantages and disadvantages of cryoablation in the treatment of breast cancer.

Objectives: To evaluate the technical parameters and clinical outcomes of contrast-enhanced mammography (CEM)-guided biopsy for diagnosing breast cancer in patients with suspicious lesions showing post-contrast enhancement on CEM but undetectable on standard digital mammography (DM) or ultrasound (US). Materials and methods: A prospective study was conducted on 36 patients referred for CEM-guided biopsy based on suspicious (BI-RADS 4-5) enhancing-only lesions detected during previous CEM examinations and/or contrast-enhanced breast MRI (CE-MRI). Procedures were performed on a dedicated prone table using a vacuum-assisted biopsy device. Effectiveness parameters included success rate (lesion enhancement, diagnostic material collection, and correct clip positioning), procedure time, average glandular dose (AGD), compression force, and complication rate. Results: From January to November 2024, 36 patients underwent CEM-guided biopsy, with a success rate of 97.2% (35/36). The median procedure time was 29 min. The AGD was 0.88 mGy (range 0.5–1.4 mGy, SD ± 0.22). The average compression force was 4.94 kg (range 2–7 kg, SD ± 1.1). Of the 35 lesions biopsied, 20 (57.1%) were masses and 15 (42.9%) non-mass enhancements, with a mean lesion size of 13.2 mm. Breast lesions were classified as BIRADS 4a (10/35), BIRADS 4b (5/35), BIRADS 4c (8/35), and BIRADS 5 (12/35). Histopathological findings showed 57.1% (20/35) of lesions were malignant. Lesion classification included 5.7% (B1), 34.3% (B2), 2.9% (B3), 31.4% (B5a), and 25.7% (B5b). Conclusion: CEM-guided biopsy is an effective and accessible technique for targeting enhancing-only breast lesions, offering advantages over MRI-guided biopsy in terms of time, cost, and patient comfort. Critical relevance statement: Contrast-enhanced mammography-guided biopsy is an effective and accessible technique for targeting enhancing-only breast lesions, offering advantages over MRI-guided biopsy in terms of time, cost, and patient comfort. Key Points: Contrast-enhanced mammography (CEM)-guided biopsy has recently gained traction as a reliable alternative to MRI for targeting enhancing-only lesions. This study explores the clinical implementation of CEM-guided biopsy in a prone position in our university hospital, assessing its efficacy, safety, and diagnostic accuracy. CEM-guided biopsy is a promising technique for the precise targeting of breast lesions, with advantages over MRI-guided biopsy. Graphical

The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast cancer and to identify the predictive value of DM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing microcalcifications for radiologic–pathologic correlation. We relied on our prospectively maintained database of suspicious microcalcifications on DM, from which data were retrospectively collected between January 2020 and April 2023. We enrolled 158 patients, all of whom were subjected to biopsy. Additionally, 63 patients underwent breast DCE-MRI. Microcalcifications with a linear branched morphology were correlated with malignancies (p < 0.001), among which an association was highlighted between triple negatives (TNs) and segmental distribution (p < 0.001). Amorphous calcifications were correlated with atypical ductal hyperplasia (ADH) (p = 0.013), coarse heterogeneous (p < 0.001), and fine-pleomorphic (p = 0.008) with atypical lobular hyperplasia (ALH) and fine pleomorphic (p = 0.009) with flat epithelial atypia (FEA). Regarding DCE-MRI, no statistical significance was observed between non-mass lesions and ductal carcinoma in situ (DCIS). Concerning mass lesions, three were identified as DCIS and five as invasive ductal carcinoma (IDC). In conclusion, microcalcifications assessed in DM exhibit promising predictive characteristics concerning breast lesion subtypes, leading to a reduction in diagnostic times and further examination costs, thereby enhancing the clinical management of patients.

Objectives: The interpretation of contrast-enhanced mammography (CEM) images heavily depends on radiologists’ expertise, highlighting the need for automated tools to assist clinical decision-making.This pilot study aimed to develop a deep learning model using CEM images to predict the histological diagnosis of breast cancer. Methods: We retrospectively analyzed patients who underwent contrast-enhanced mammography (CEM) followed by histopathological assessment (October 2022-May 2023) across two centers. CEM images from center 1 were used for model development, including training and 10-fold cross-validation, while images from center 2 served as an independent external test set. The breast region was manually segmented, and two deep learning architectures were implemented as ensemble classifiers, using biopsy results as reference standard. Performance metrics included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC). Results: A total of 106 CEM images were retrospectively analyzed (50 from center 1 and 56 from center 2), obtained using different mammography systems. Histopathology identified 51 lesions (48%) as malignant and 55 (52%) as benign. Cross-validation yielded a ROC-AUC of 75% [58.4–91.6], accuracy of 68.7% [58.3–79], sensitivity of 68.9% [52.2–85.6], specificity of 67.8% [65.4–70.2], PPV of 69.3% [55.5–83.1], and NPV of 73.5% [55.2–91.8] (p < 0.05). External testing on images from center 2 (27 malignant, 29 benign) achieved an accuracy of 64.3%. Conclusions: The study showed promising performance but requires further development and dataset expansion. The model has the potential to be integrated into clinical practice. Advances in knowledge: A deep learning model based CEM provides a tool to support physicians in the decision-making process and reduce invasiveness in breast cancer management.