Apocrine adenocarcinoma in the setting of apocrine hidrocystoma of the leg

Apocrine hidrocystoma is a benign, cystic lesion often presenting in the periorbital region. Apocrine adenocarcinoma is the rare, malignant counterpart occurring mainly in the axilla and anogenital region. There is a paucity of literature on both entities and co-occurrence has been reported in only 5 cases. We present the case of a 48-year-old man with a history of total body irradiation for chronic myelocytic leukemia, diabetes mellitus, and obesity who presented with a calf mass of two years' duration. Epidermal inclusion cyst was presumed and excisional biopsy was carried out. Pathologic analysis revealed apocrine adenocarcinoma in the setting of a precursor apocrine hidrocystoma. Our patient's unique altered immunity and the direct effects of irradiation on the local microenvironment may have resulted in his rare presentation of co-occurrence of apocrine adenocarcinoma within an existing apocrine hidrocystoma. To our knowledge, our patient is the first reported patient with this presentation in the lower extremity

Bridging the Gap: A Pilot Study on the Efficacy of Nerve Allografts in Autologous Breast Reconstruction

Background: Breast anesthesia is commonly reported after mastectomy and reconstruction. During deep inferior epigastric perforator (DIEP) flap reconstruction, we coapt at least one of the T10–12 thoracoabdominal nerves within the flap to the anterior cutaneous branch of the 3rd intercostal nerve using a nerve allograft. We aim to evaluate the efficacy of nerve grafting in improving sensory recovery following neurotized DIEP flap reconstruction. Methods: Thirty patients (54 breasts) underwent immediate neurotized DIEP flap reconstruction using nerve grafts. Sensitivity evaluation was performed in nine breast regions. For each patient, sensation was compared between two time points: 3 to 6 months postoperatively versus 12 to 24 months postoperatively. The reconstructive BREAST-Q was used to survey patients’ satisfaction of their breasts, physical wellbeing, psychosocial wellbeing, and sexual wellbeing. Results: At 3 to 6 months postoperatively, patients had a mean sensitivity measurement of 52.1 g/mm2. At 12 to 24 months postoperatively, patients had a mean sensitivity measurement of 40.3 g/mm2. There was a significant decrease in the mean cutaneous threshold required for patients to perceive sensation between the two time points (–29.1 percent, p = 0.041). On the reconstructive BREAST-Q, patients scored significantly higher in breast satisfaction (56.7/100 versus 75.1/100, +32.5 percent, p = 0.032) and physical wellbeing (66.0/100 versus 85.5/100, +20.2 percent, p = 0.022) between the two time points. Conclusions: Patients who undergo nerve graft-based DIEP flap reconstruction can expect significant improvements in sensation to pressure over time. This improvement found on sensory testing correlates with significant improvement in patients’ BREAST-Q scores

Machine-Learning Prediction of Capsular Contraction after Two-Stage Breast Reconstruction

Background: Two-stage breast reconstruction is a common technique used to restore preoperative appearance in patients undergoing mastectomy. However, capsular contracture may develop and lead to implant failure and significant morbidity. The objective of this study is to build a machine-learning model that can determine the risk of developing contracture formation after two-stage breast reconstruction. Methods: A total of 209 women (406 samples) were included in the study cohort. Patient characteristics that were readily accessible at the preoperative visit and details pertaining to the surgical approach were used as input data for the machine-learning model. Supervised learning models were assessed using 5-fold cross validation. A neural network model is also evaluated using a 0.8/0.1/0.1 train/validate/test split. Results: Among the subjects, 144 (35.47%) developed capsular contracture. Older age, smaller nipple-inframammary fold distance, retropectoral implant placement, synthetic mesh usage, and postoperative radiation increased the odds of capsular contracture (p < 0.05). The neural network achieved the best performance metrics among the models tested, with a test accuracy of 0.82 and area under receiver operative curve of 0.79. Conclusion: To our knowledge, this is the first study that uses a neural network to predict the development of capsular contraction after two-stage implant-based reconstruction. At the preoperative visit, surgeons may counsel high-risk patients on the potential need for further revisions or guide them toward autologous reconstruction

Using a Machine Learning Approach to Predict the Need for Elective Revision and Unplanned Surgery after Implant-based Breast Reconstruction

Summary:. Two-stage implant-based reconstruction after mastectomy may require secondary revision procedures to treat complications, correct defects, and improve aesthetic outcomes. Patients should be counseled on the possibility of additional procedures during the initial visit, but the likelihood of requiring another procedure is dependent on many patient- and surgeon-specific factors. This study aims to identify patient-specific factors and surgical techniques associated with higher rates of secondary procedures and offer a machine learning model to compute individualized assessments for preoperative counseling. A training set of 209 patients (406 breasts) who underwent two-stage alloplastic reconstruction was created, with 45.57% of breasts (185 of 406) requiring revisional or unplanned surgery. On multivariate analysis, hypertension, no tobacco use, and textured expander use corresponded to lower odds of additional surgery. In contrast, higher initial tissue expander volume, vertical radial incision, and larger nipple-inframammary fold distance conferred higher odds of additional surgery. The neural network model trained on clinically significant variables achieved the highest collective performance metrics, with ROC AUC of 0.74, sensitivity of 84.2, specificity of 63.6, and accuracy of 62.1. The proposed machine learning model trained on a single surgeon’s data offers a precise and reliable tool to assess an individual patient’s risk of secondary procedures. Machine learning models enable physicians to tailor surgical planning and empower patients to make informed decisions aligned with their lifestyle and preferences. The utilization of this technology is especially applicable to plastic surgery, where outcomes are subject to a variety of patient-specific factors and surgeon practices, including threshold to perform secondary procedures