Faculty Bibliography

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.

There is a paucity of information on racial and ethnic disparities relating to barriers to care in healthcare access and utilization in patients with cutaneous malignancies. We conducted a cross-sectional analysis of adults with melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) in the National Institutes of Health (NIH) All of Us Research Program collected between May 2018 and July 2022. Participants included adults (aged 18 or older) with cutaneous malignancy who completed the Health Care Access and Utilization survey. We identified 5,817 adults who were diagnosed with BCC (67%), cSCC (28.9%), and melanoma (23.9%). Non-Hispanic Black (NHB) and Hispanic patients were more likely than non-Hispanic White (NHW) patients to delay a primary care visit due to cost (p = 0.005 and p = 0.015, respectively). NHB patients were also more likely to delay care due to lack of transportation (p < 0.001). NHB and Hispanic patients were more likely to place importance on seeing a provider from the same background (NHB p < 0.002; Hispanic p = 0.002) and also were more likely to report never being able to see such a provider (NHB p < 0.001; Hispanic p = 0.002). Medicaid/Medicare patients, non-college graduates, and those with lower incomes also faced increased barriers to care, including delays due to cost and transportation issues. People of color with cutaneous malignancies are more likely to delay care in seeing primary care providers due to cost or transportation issues. This study provides important insights on disproportionate healthcare usage among racial/ethnic groups that may help mitigate healthcare disparities.

BACKGROUND:Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in skin cancer diagnosis may alleviate potential care gaps. OBJECTIVE:The aim of this systematic review was to offer an in-depth exploration of published AI algorithms trained on dermoscopic and macroscopic clinical images for the diagnosis of melanoma, basal cell carcinoma, and cutaneous squamous cell carcinoma (cSCC). METHODS:Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic review was conducted on peer-reviewed articles published between January 1, 2000, and January 26, 2023. RESULTS AND DISCUSSION/CONCLUSIONS:Among the 232 studies in this review, the overall accuracy, sensitivity, and specificity of AI for tumor detection averaged 90%, 87%, and 91%, respectively. Model performance improved with time. Despite seemingly impressive performance, the paucity of external validation and limited representation of cSCC and skin of color in the data sets limits the generalizability of the current models. In addition, dermatologists coauthored only 12.9% of all studies included in the review. Moving forward, it is imperative to prioritize robustness in data reporting, inclusivity in data collection, and interdisciplinary collaboration to ensure the development of equitable and effective AI tools.