Vacuum-assisted Evacuation (VAEv) of Symptomatic and/or Voluminous Breast Haematomas Following Surgeries and Percutaneous Procedures.

BACKGROUND/AIM: Among postoperative complications in breast surgery, postoperative hematoma is the most common occurrence. While mostly self-limited, in some cases surgical revision is mandatory. Among percutaneous procedures, preliminary studies demonstrated the efficacy of vacuum-assisted breast biopsy (VAB) in evacuating postprocedural breast hematomas. However, no data are available regarding VAB evacuating postoperative breast hematomas. Therefore, the present study aimed to investigate the efficacy of the VAB system in evacuating postoperative and postprocedural hematoma, symptom resolution, and avoidance of surgery. PATIENTS AND METHODS: From January 2016 to January 2020, patients with ≥25 mm symptomatic breast hematomas developed after breast-conserving surgery (BCS) and percutaneous procedures were retrospectively enrolled from a perspective-maintained database. Hematoma maximum diameter, estimated hematoma volume, total procedure time, and visual analog scale (VAS) score before ultrasound (US) vacuum-assisted evacuation (VAEv) were recorded. At one-week VAS score, residual hematoma volume, and complications were recorded. RESULTS: Among 932 BCSs and 618 VAB procedures, a total of 15 late postoperative hematomas were recorded (9 after BCS and 6 after VAB). Preoperative median diameter was 43.00 (35.50-52.50) mm and median volume 12.60 (7.35-18.30) mm3 Regarding VAEv, median time recorded was 25.92 (21.89-36.81) min. At one week, the median hematoma reduction was 83.00% (78.00%-87.5%) with a statistically significant VAS reduction (5.00 vs. 2.00; p>0.001). No surgical treatment was needed and only one case of seroma occurred. CONCLUSION: VAEv represents a promising safe, time and resource-sparing treatment modality for the evacuation of breast hematomas, potentially decreasing the rate of reoperation after surgery.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.