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Background: Nuclear 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., Objectives: To 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., Methods: The 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., Results: MSA-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., Conclusions: This 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., (© 2024 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.)

Competing Interests: Conflicts of interest: the authors declare no conflicts of interest.

Background: Paget disease, Bowen disease, and malignant melanoma in situ are intraepidermal neoplasms, characterized by the presence of pagetoid scatter of atypical cells in the epidermis. This study reviewed the frequency of select histologic criteria to validate their usefulness in the histologic distinction between these entities., Methods: One hundred forty-four specimens with the diagnosis of Bowen disease, 144 specimens with Paget disease (mammary and extramammary), and 144 specimens with malignant melanoma in situ were examined microscopically to define frequencies of select histologic criteria present in each disease., Results: Comparison between mammary Paget and extramammary Paget disease showed no significant differences in the features studied. Crushing of basal keratinocytes, presence of atypical cells in the corneum, and presence of large cells with amphophilic cytoplasm were significantly noted in Paget disease. Transition between the atypical clear cells and surrounding keratinocytes was absent in all cases of melanoma in situ and in 87 (60.4%) cases of Paget disease, but it was significantly associated with Bowen disease (98.6%). Dyskeratotic cells were significantly associated with Bowen disease cases., Conclusion: Our study demonstrated a practical histologic approach to differentiate between intraepidermal pagetoid neoplasms. Careful histologic study of the proposed criteria may reduce reliance on immunohistochemical stains.