Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

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 tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real-time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumours, and to map tumours to their original anatomical position/orientation.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Perspectives of Dermatology Faculty Toward Millennial Trainees and Colleagues: A National Survey.

OBJECTIVE: To assess the attitudes and beliefs of faculty dermatologists regarding perceived characteristics of millennial trainees and colleagues. PARTICIPANTS AND METHODS: We conducted a cross-sectional survey of dermatology physician-educators listed in the Fellowship and Residency Electronic Interactive Database from August 1, 2019, to October 31, 2019. This survey consisted of 26 items (5-point Likert scales) representing positive, negative, and neutral millennial stereotypes relevant to graduate medical education. Participants' responses were analyzed using the chi-squared goodness of fit test with dichotomized data. RESULTS: Seventy-six dermatology physician-educators participated in the national survey. A statistically significant response pattern was seen in 18 of 26 (69%) tested stereotypes. Positive judgments included denial of hesitations about working with millennials (P = .038) and agreement with the notions that millennials are technologically savvy (P < .001), socially just (P < .001), equally capable dermatologists as other generations (P < .001), enjoyable to work with (P < .001), easy to connect with interpersonally (P < .001), and promising future leaders of medicine (P = .039). Negative judgments included perceptions of the word millennial as a pejorative (P < .001) and of millennials being relatively entitled (P < .001), overly sensitive to feedback (P < .001), less polite (P < .001), and less hard-working (P < .001) compared with prior generations. CONCLUSION: This study represents the first national survey of the attitudes and perspectives of dermatology physician-educators regarding perceived characteristics of millennial trainees and colleagues. Our results suggest that dermatology faculty endorse various positive, negative, and neutral stereotypes regarding Generation Y. Early recognition of implicit biases can inform curricular design and prepare educators to address generational gaps in medical education.

Hopx expression defines a subset of multipotent hair follicle stem cells and a progenitor population primed to give rise to K6+ niche cells.

The mammalian hair follicle relies on adult resident stem cells and their progeny to fuel and maintain hair growth throughout the life of an organism. The cyclical and initially synchronous nature of hair growth makes the hair follicle an ideal system with which to define homeostatic mechanisms of an adult stem cell population. Recently, we demonstrated that Hopx is a specific marker of intestinal stem cells. Here, we show that Hopx specifically labels long-lived hair follicle stem cells residing in the telogen basal bulge. Hopx(+) cells contribute to all lineages of the mature hair follicle and to the interfollicular epidermis upon epidermal wounding. Unexpectedly, our analysis identifies a previously unappreciated progenitor population that resides in the lower hair bulb of anagen-phase follicles and expresses Hopx. These cells co-express Lgr5, do not express Shh and escape catagen-induced apoptosis. They ultimately differentiate into the cytokeratin 6-positive (K6) inner bulge cells in telogen, which regulate the quiescence of adjacent hair follicle stem cells. Although previous studies have suggested that K6(+) cells arise from Lgr5-expressing lower outer root sheath cells in anagen, our studies indicate an alternative origin, and a novel role for Hopx-expressing lower hair bulb progenitor cells in contributing to stem cell homeostasis.

Interconversion between intestinal stem cell populations in distinct niches.

Intestinal epithelial stem cell identity and location have been the subject of substantial research. Cells in the +4 niche are slow-cycling and label-retaining, whereas a different stem cell niche located at the crypt base is occupied by crypt base columnar (CBC) cells. CBCs are distinct from +4 cells, and the relationship between them is unknown, though both give rise to all intestinal epithelial lineages. We demonstrate that Hopx, an atypical homeobox protein, is a specific marker of +4 cells. Hopx-expressing cells give rise to CBCs and all mature intestinal epithelial lineages. Conversely, CBCs can give rise to +4 Hopx-positive cells. These findings demonstrate a bidirectional lineage relationship between active and quiescent stem cells in their niches.