Differentiation of Basal Cell Carcinoma and Trichoepithelioma: An Immunohistochemical Study

Carrasquillo, Osward Y.; Cruzval-O’Reilly, Estefanía; Sánchez, Julio E.; Valentín-Nogueras, Sheila M.

doi:10.1097/dad.0000000000001783

Cited by 5 publications

(13 citation statements)

References 25 publications

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“…Meanwhile, we have looked at features relating to nuclear topology and found increased distance within tumor nuclei in BCC compared with TE. This is in alignment with pathological manifestations which BCC tumor cells are arranged loosely for mucin presenting within or surrounding tumor nodules 5 . The most suggestive indicator of minimum distance between tumor nuclei is T‐T_meanEdgeLength.…”

Section: Discussionsupporting

confidence: 59%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…BCCs have variable histological subtypes (superficial, nodular, pigmented, infundibulocystic, micronodular, infiltrating and sclerosing, etc. ), while TEs present with a relatively simple morphological pattern which consists of nests and strands of basaloid cells in a racemiform configuration intermingling with or without horn cysts) 5 . The image complexity may associate with relatively poor differentiation because of rapid disorganized cells growth and formation of irregular organizational patterns, such as infiltrating BCC 23 .…”

Section: Discussionmentioning

confidence: 99%

“…To differentiate these two entities, a lot of efforts has been made including immunohistochemical stains with minimal help. 5…”

Section: Introductionmentioning

confidence: 99%

“…TE and BCC share similar cytology in term of the basaloid epithelial component while the surrounding stromal reaction is different. To differentiate these two entities, a lot of efforts has been made including immunohistochemical stains with minimal help 5 . HR‐BCCs represent the population which are more likely to recurrence and metastasis and need more extensive care in contrast to LR one.…”

Section: Introductionmentioning

confidence: 99%

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

Lan,

Guo,

Wang

et al. 2024

Skin Research and Technology

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BackgroundNuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity.ObjectivesTo develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high‐risk (HR) and low‐risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors.MethodsThe deep learning systems were trained on 161 H&E ‐stained sections which contained 51 sections of HR‐BCC, 50 sections of LR‐BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi‐head self‐attention (MSA) U‐Net, nuclei segmentation by HoVer‐Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers.ResultsMSA‐U‐Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977 ± 0.0159, 0.955 ± 0.0181, 0.885 ± 0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T‐T_meanEdgeLength, T‐T_Nsubgraph, S‐T_HarmonicCentrality, S‐S_Degrees. The risk stratification model can well predict HR‐BCC and LR‐BCC with 0.920 ± 0.0579, 0.839 ± 0.0176, 0.825 ± 0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR‐BCC and LR‐BCC comprised IntensityMin, Solidity, T‐T_minEdgeLength, T‐T_Coreness, T‐T_Degrees, T‐T_Betweenness, S‐T_Degrees.ConclusionsThis framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%