Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Radial sectioning of the resected tumor and surrounding tissue is the most common form of intra-operative and post-operative margin assessment. However, this technique samples only a tiny fraction of the available tissue and therefore may result in incomplete excision of the tumor, increasing the risk of recurrence and distant metastasis and decreasing survival. Repeat procedures, chemotherapy, and other resulting treatments pose significant morbidity, mortality, and fiscal costs for our healthcare system. Mohs Micrographic Surgery (MMS) is used for the removal of basal cell and squamous cell carcinoma utilizing frozen sections for real-time margin assessment while assessing 100% of the peripheral and deep margins, resulting in a recurrence rate of less than one percent. Real-time assessment in many tumor types is constrained by tissue size and complexity and the time to process tissue and evaluate slides while a patient is under general anesthesia. In this study, we developed an artificial intelligence (AI) platform, ArcticAI, which augments the surgical workflow to improve efficiency by reducing rate-limiting steps in tissue preprocessing and histological assessment through automated mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma (BCC) as a model system, the results demonstrate that ArcticAI can provide effective grossing recommendations, accurately identify tumor on histological sections, map tumor back onto the surgical resection map, and automate pathology report generation resulting in seamless communication between the surgical pathology laboratory and surgeon. AI-augmented-surgical excision workflows may make real-time margin assessment for the excision of more complex and challenging tumor types more accessible, leading to more streamlined and accurate tumor removal while increasing healthcare delivery efficiency.

Background: Staffing shortages and inadequate healthcare access have driven the development of artificial intelligence (AI)-enabled tools in medicine. Accuracy of these algorithms has been extensively investigated, but research on downstream effects of AI integration into the clinical workflow is lacking. Objective: We aim to analyze how integration of a basal cell carcinoma detection and tumor mapping algorithm in a Mohs micrographic surgery (MMS) unit may impact waiting times in the surgical pathology laboratory and on the floor. Methods: Time spent on each task and slide, staff, and histotechnician waiting times were analyzed over a 20 day period in a MMS unit. A simulated AI workflow was created and the time differences between the real and simulated workflows were compared. Results: Simulated addition of the algorithm led to improvements of 64% in slide waiting time (1:03:39 per case), 36% in staff waiting time (59:09 per case), and 25% in histotechnician waiting time (25:27 per case). Limitations: A single MMS unit was analyzed and AI integration was performed retrospectively, rather than in real time. Conclusions: AI integration results in significantly reduced slide, staff, and histotechnician waiting time, which enables increased productivity and a streamlined clinical workflow.

Importance: Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumor removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. Objective: To develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. Design: A retrospective cohort study was conducted using frozen cSCC section slides and adjacent tissues. Setting: This study was conducted in a tertiary care academic center. Participants: Patients undergoing Mohs micrographic surgery for cSCC between January and March 2020. Exposures: Frozen section slides were scanned and annotated, delineating benign tissue structures, inflammation, and tumor to develop an AI algorithm for real-time margin analysis. Patients were stratified by tumor differentiation status. Epithelial tissues including epidermis and hair follicles were annotated for moderate-well to well differentiated cSCC tumors. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC at 50-micron resolution. Main Outcomes and Measures: The performance of the AI algorithm in identifying cSCC at 50-micron resolution was reported using the area under the receiver operating characteristic curve. Accuracy was also reported by tumor differentiation status and by delineation of cSCC from epidermis. Model performance using histomorphological features alone was compared to architectural features (i.e., tissue context) for well-differentiated tumors. Results: The AI algorithm demonstrated proof of concept for identifying cSCC with high accuracy. Accuracy differed by differentiation status, driven by challenges in separating cSCC from epidermis using histomorphological features alone for well-differentiated tumors. Consideration of broader tissue context through architectural features improved the ability to delineate tumor from epidermis. Conclusions and Relevance: Incorporating AI into the surgical workflow may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumors/neoplasms. Further algorithmic improvement is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumors, and to map tumors to their original anatomical position/orientation. Future studies should assess the efficiency improvements and cost benefits and address other confounding pathologies such as inflammation and nuclei.

Fluorescence paired-agent imaging combined with en face margin analysis can detect positive margins in low tumor-bearing (<1% tumor volume) whole tissue samples, as demonstrated in xenograft murine models.