Multimodality Imaging of Postmastectomy Breast Reconstruction Techniques, Complications, and Tumor Recurrence.

For women undergoing mastectomy, breast reconstruction can be performed by using implants or autologous tissue flaps. Mastectomy options include skin- and nipple-sparing techniques. Implant-based reconstruction can be performed with saline or silicone implants. Various autologous pedicled or free tissue flap reconstruction methods based on different tissue donor sites are available. The aesthetic outcomes of implant- and flap-based reconstructions can be improved with oncoplastic surgery, including autologous fat graft placement and nipple-areolar complex reconstruction. The authors provide an update on recent advances in implant reconstruction techniques and contemporary expanded options for autologous tissue flap reconstruction as it relates to imaging modalities. As breast cancer screening is not routinely performed in this clinical setting, tumor recurrence after mastectomy and reconstruction is often detected by palpation at physical examination. Most local recurrences occur within the skin and subcutaneous tissue. Diagnostic breast imaging continues to have a critical role in confirmation of disease recurrence. Knowledge of the spectrum of benign and abnormal imaging appearances in the reconstructed breast is important for postoperative evaluation of patients, including recognition of early and late postsurgical complications and breast cancer recurrence. The authors provide an overview of multimodality imaging of the postmastectomy reconstructed breast, as well as an update on screening guidelines and recommendations for this unique patient population. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Natural language processing in radiology: Clinical applications and future directions.

Natural language processing (NLP) is a wide range of techniques that allows computers to interact with human text. Applications of NLP in everyday life include language translation aids, chat bots, and text prediction. It has been increasingly utilized in the medical field with increased reliance on electronic health records. As findings in radiology are primarily communicated via text, the field is particularly suited to benefit from NLP based applications. Furthermore, rapidly increasing imaging volume will continue to increase burden on clinicians, emphasizing the need for improvements in workflow. In this article, we highlight the numerous non-clinical, provider focused, and patient focused applications of NLP in radiology. We also comment on challenges associated with development and incorporation of NLP based applications in radiology as well as potential future directions.