Artificial intelligence for basal cell carcinoma: diagnosis and distinction from histological mimics

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features

“…Such a combination is similar to pathologist's diagnostic thinking and is expected to be applied in clinical practice. When dealing with common clinical problem (hesitation between BCC and TE), the differentiation classifier can discriminate BCC from TE in model setting with comparable performance to some of previous studies 17,19 . The performance of the this classifiers is above mean level (sensitivity = 89.2%, specificity = 81.1%) for machine learning in the diagnosis of non‐melanoma skin cancers, but under the top level to differentiate BCC from TE (recall = 100%) 17,19 .…”

“…17,19 However, the later model was trained only on 5 sections of TE and test on one slide to differentiate BCC from TE. 17 The small sample size of TE and single center study reduced its confidence of differentiation, while our differentiation classifier is or surrounding tumor nodules. 5 The most suggestive indicator of minimum distance between tumor nuclei is T-T_meanEdgeLength.…”

“…In the daily pathological diagnostic workflow of BCC, distinguishing BCC from potential mimics and stating aggressive (HR‐BCC) or nonaggressive types (LR‐BCC) are essential skills for dermatopathologists 15 . Deep learning models have been applied separately for the differentiation and risk stratification of BCC, but not yet been used to construct a pathological framework to accomplish such clinical procedure for daily dermatopathological diagnosis 16,17 . In addition, the importance of nuclear and TME features which have been proved to be important factors among other cancer types, and have been used to predict tumor prognosis, benign and malignant judgment, and disease risk classification in non‐dermatopathological area, although these characteristics are often underestimated in dermatopathology 18–21 .…”

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma

“…[11][12][13][14][15][16][17][18][19][20][21][22][23][24] Numerous studies were concerned with the use of DL models for detection of basal cell carcinomas. [25][26][27][28][29][30][31] As the most common human malignant skin tumor in Central Europe with annually increasing incidence rates, basal cell carcinoma (BCC) represents a major portion of samples submitted to dermatopathology labs. 32 At the same time, BCCs are well suited for establishing automated AI-based image recognition due to their clear histological criteria.…”

Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms

“…Dabei zeigten DL‐Algorithmen gute Ergebnisse bei der Diagnose von Onychomykosen, entzündlichen Dermatosen und kutanen Lymphomen, melanozytären Läsionen sowie Tumoren aus der Gruppe der nichtmelanotischen Hautkrebserkrankungen (NMSC) 11–24 . Zahlreiche Untersuchungen beschäftigten sich mit der Anwendung von DL‐Modellen zur Erkennung von Basalzellkarzinomen 25–31 . Das Basalzellkarzinom (BCC) stellt als häufigster maligner Hauttumor des Menschen in Zentraleuropa mit jährlich steigenden Inzidenzraten einen Großteil des Einsendeguts von dermatopathologischen Laboren dar 32 .…”