Vidal, Nahid Y.

BACKGROUND Implementing Mohs case complexity grading systems is critical due to inherent complexities of Mohs workflow coordination. Precise scheduling can improve wait times and resource allocation. OBJECTIVE To predict Mohs number of stages and reconstruction type as surrogates of case complexity and to use information derived from model training and practice to develop scheduling recommendations. METHODS Machine learning models were trained to predict number of Mohs stages and complex reconstruction across multiple practices and surgeons in an institutional enterprise, using information available on pathology reports. A workflow analysis by health systems engineering was conducted to identify improvement areas for efficiency. Information from modeling and workflow analysis was combined to develop fixed rules for scheduling recommendations. RESULTS In total, 18,473 Mohs cases were used for training/validation and 2,053 for testing. Models achieved an AUC-ROC of 0.83 (95% CI: 0.81–0.85) for prediction of complex reconstruction and 0.62 (0.60–0.66) for number of stages (1 vs 2 vs 3 or more). Anatomical location was the most valuable feature, with midface locations predicting higher case complexity and longer surgical time. CONCLUSION We demonstrated feasibility for prediction of Mohs surgery case complexity. Combining machine and human-derived knowledge provides complementary insights that can assist with improving scheduling and workflow.

Background Dermatofibrosarcoma protuberans (DFSP) arising in the vulvar and perivulvar region poses unique challenges to the dermatologic surgeon. The potential for broad subclinical extension and involvement of deeper and high-risk functional structures requires a strong knowledge of relevant anatomy. Careful considerations are required for preoperative arrangements, surgical day preparations, and postoperative surveillance to improve treatment outcomes and patient experiences. Objective The authors aim to provide a practical guide for the dermatologic surgeon, which outlines surgical planning and management recommendations for vulvar DFSP. Emphasis on pelvic anatomy through cadaveric and patient images as well as anatomical illustrations will be included. Materials and Methods A literature review was performed to identify evidence-based management recommendations for vulvar DFSP. Authors share anecdotal perioperative recommendations for optimizing the patient experience. A detailed pelvic dissection of a female cadaver was performed for anatomical depiction of relevant high-risk structures. Illustrations of sensory nerve distributions were created for reference. Conclusion Vulvar DFSP is a challenging cutaneous tumor in a sensitive anatomical location. As Mohs surgery offers clear advantages over wide local excision, the authors provide dermatologic surgeons with detailed anatomy review resources as well as recommendations and considerations for management and perioperative planning for this specialized site.

BACKGROUND There is a scarcity of artificial intelligence models trained on frozen pathology. One way to expand the clinical utility of models trained on permanent pathology is by applying them to frozen sections and fine-tune based on weaknesses. OBJECTIVE To qualitatively evaluate a deep learning model trained on permanent pathology to classify squamous cell carcinoma on Mohs surgery frozen sections to learn model shortcomings and inform retraining and fine-tuning. MATERIALS AND METHODS The authors trained a model for classification of tumor on 746 skin biopsy slides and tested it on 15 Mohs surgery frozen sections. The authors estimated performance metrics and compared the regions of interest generated by the model with the original H&E slides. RESULTS The model achieved an AUC-ROC of 0.985 and 0.796 for tumor classification in permanent pathology and in frozen sections, respectively. Regions of interest for frozen sections with scarce tumor areas were inaccurate, focusing on normal tissue for slides classified as false negative, or highlighting structures different from tumor (e.g., inflammation, muscle, and nerves) for slides classified as true positive. CONCLUSION Deep anatomical structures more commonly present in Mohs frozen pathology might represent data out-of-distribution for models trained on permanent pathology, potentially leading to inadequate model outputs.

BACKGROUND The use of Mohs micrographic surgery with melanocytic immunostains (MMS-I) for cutaneous melanoma is increasing. OBJECTIVE To assess local recurrence, melanoma-specific death rates in patients with invasive melanoma treated with MMS-I. MATERIALS AND METHODS A single-center retrospective review of patients with invasive melanoma treated with MMS-I from January 2008 to December 2018. RESULTS Three hundred fifty-two patients (359 melanomas) were included. The median age was 71 years; most patients were male (252%; 71.6%). Most tumors were T1a/b (341, 95%), H/N (322; 89.7%), and lentigo maligna subtype (281, 78.3%). At a median follow-up of 4.3 years, local recurrence rates were 1.4% (5) and 0.9% (3) among all-stage and T1a/b tumors, respectively. There were 3 melanoma-related deaths (0.9%). CONCLUSION MMS-I is associated with <1% risk of local recurrence and disease-specific mortality for T1a/b melanomas.