ArticleDiagnostics (Basel, Switzerland)2022
Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Five-Year Follow-Up of Patients With Histologically Aggressive Basal Cell Carcinoma Treated With Interferon.Journal of skin cancer · 2026Article
- Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Artificial Intelligence-Assisted Dermatologic Screening: Epidemiology and Clinical Features of Basal Cell Carcinoma, Squamous Cell Carcinoma, Seborrheic Keratosis and Actinic Keratosis.Bioengineering (Basel, Switzerland) · 2025Review
- The Epidemiological and Histopathological Profiling of Basal Cell Carcinoma: Insights from a 4-Year Institutional Cohort in a Romanian Clinical County Hospital.Diagnostics (Basel, Switzerland) · 2025Article
- Basal Cell Carcinoma: An Old Friend with Multiple Faces.Cancers · 2025Review
- Article
- Editorial on Special Issue "Artificial Intelligence in Pathological Image Analysis".Diagnostics (Basel, Switzerland) · 2023Article
- Reliable deep learning for coronary artery disease detection: a patient-level, statistically validated MRI study.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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Authors and funding
4 authors.
Funding
Abstract
Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes.
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