Nipple-Sparing Mastectomy in 99 Patients With a Mean Follow-up of 5 Years

Background. The safety and practicality of nipple-sparing mastectomy (NSM) are controversial. Methods. Review of a large breast center's experience identified 99 women who underwent intended NSM with subareolar biopsy and breast reconstruction for primary breast cancer. Outcome was assessed by biopsy status, postoperative nipple necrosis or removal, cancer recurrence, and cancer-specific death. Results. NSM was attempted for invasive cancer (64 breasts, 24 with positive lymph nodes), noninvasive cancer (35 breasts), and/or contralateral prophylaxis (50 breasts). Twenty-two nipples (14%) were removed because of positive subareolar biopsy results (frozen or permanent section). Seven patients underwent a pre-NSM surgical delay procedure because of increased risk for nipple necrosis. Reconstruction used transverse rectus abdominis myocutaneous flaps (56 breasts), latissimus flaps with expander (35 breasts), or expander alone (58 breasts). Of 127 retained nipples, 8 (6%) became necrotic and 2 others (2%) were removed at patient request. There was no nipple necrosis when NSM was performed after a surgical delay procedure. At a mean follow-up of 60.2 months, all 3 patients with recurrence had biopsy-proven subareolar disease and had undergone nipple removal at original mastectomy. There were no deaths. Conclusions. Five-year recurrence rate is low when NSM margins (frozen section and permanent) are negative. Nipple necrosis can be minimized by incisions that maximize perfusion of surrounding skin and by avoiding long flaps. A premastectomy surgical delay procedure improves nipple survival in high-risk patients. NSM can be performed safely with all types of breast reconstruction

Optimizing Nipple Position following Nipple-Sparing Mastectomy

Background: The best treatment for nipple malposition following nipple-sparing mastectomy is prevention. This article reviews basic elements for success in nipple-sparing mastectomy and offers an option to patients with grade 2–3 breast ptosis who strongly desire to preserve the nipple. Methods: Retrospective review identified patients undergoing nipple-sparing mastectomy and immediate reconstruction. Results: Patient selection centered on realistic goals for postoperative breast size, nipple position, and when not to save the nipple. The choice of device considered projection and nipple centralization as equal components and led to wider, lower profile devices selectively for the first stage of reconstruction. In severe grade 2–3 nipple ptosis, an inferior vertical incision or wedge excision was used to enhance nipple position postoperatively. Eighteen consecutive patients underwent 32 implant-based breast reconstructions following nipple-sparing mastectomy with the vertical incision. The average age was 45 years old, and the average body mass index was 26.7. Direct-to-implant reconstruction was performed in 25%, whereas 75% had tissue expander-implant reconstruction. Overall complications included infection (3%) and nipple necrosis (3%) leading to explant in 1 reconstruction. Conclusions: The final nipple position following nipple-sparing mastectomy can be optimized with preoperative planning. The vertical incision, combined with proper patient selection and choice of device, may increase eligibility for nipple-sparing procedures in patients with grade 2–3 ptosis who desire nipple preservation

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory