ArticleScientific reports2022
Identification of metastatic primary cutaneous squamous cell carcinoma utilizing artificial intelligence analysis of whole slide images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The Dutch cutaneous squamous cell carcinoma and metastasis (D-SQUAME) study: a nationwide discovery cohort and nationwide validation cohort with nested case-control designs for risk prediction modeling.European journal of epidemiology · 2026Article
- Early detection of metastatic risk in primary cutaneous melanoma using weakly supervised learning.Scientific reports · 2026Article
- The pre-metastatic niche: mechanisms, heterogeneity, and therapeutic opportunities.Molecular cancer · 2026Review
- Prediction of clinical outcomes of advanced cutaneous squamous cell carcinoma to PD-1 inhibition directly from histopathology slides using inferred transcriptomics.Frontiers in immunology · 2026Article
- Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.NPJ precision oncology · 2025Article
- Enhancing Early Detection of Metastatic Cutaneous Squamous Cell Carcinoma (CSCC): Integrating AI With Histopathological Assessments.Experimental dermatology · 2025Article
- Case Reports and Artificial Intelligence Challenges on Squamous Cell Carcinoma Developed on Chronic Radiodermitis.Journal of clinical medicine · 2025Article
- Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis.NPJ digital medicine · 2025Article
- Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosis.Research square · 2023Article
- Personalised decision making to predict absolute metastatic risk in cutaneous squamous cell carcinoma: development and validation of a clinico-pathological model.EClinicalMedicine · 2023Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images.Scientific reports · 2023Article
- Giant cutaneous squamous cell carcinoma of the popliteal fossa skin: A case report.World journal of clinical cases · 2022Article
- The State of Artificial Intelligence in Skin Cancer Publications.Journal of cutaneous medicine and surgeryArticle
- SCC-Net: A lightweight attention-enhanced deep learning model for automated squamous cell carcinoma detection.Digital healthArticle
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Authors and funding
7 authors.
Funding
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Abstract
Cutaneous squamous cell carcinoma (cSCC) harbors metastatic potential and causes mortality. However, clinical assessment of metastasis risk is challenging. We approached this challenge by harnessing artificial intelligence (AI) algorithm to identify metastatic primary cSCCs. Residual neural network-architectures were trained with cross-validation to identify metastatic tumors on clinician annotated, hematoxylin and eosin-stained whole slide images representing primary non-metastatic and metastatic cSCCs (n = 104). Metastatic primary tumors were divided into two subgroups, which metastasize rapidly (≤ 180 days) (n = 22) or slowly (> 180 days) (n = 23) after primary tumor detection. Final model was able to predict whether primary tumor was non-metastatic or rapidly metastatic with slide-level area under the receiver operating characteristic curve (AUROC) of 0.747. Furthermore, risk factor (RF) model including prediction by AI, Clark's level and tumor diameter provided higher AUROC (0.917) than other RF models and predicted high 5-year disease specific survival (DSS) for patients with cSCC with 0 or 1 RFs (100% and 95.7%) and poor DSS for patients with cSCCs with 2 or 3 RFs (41.7% and 40.0%). These results indicate, that AI recognizes unknown morphological features associated with metastasis and may provide added value to clinical assessment of metastasis risk and prognosis of primary cSCC.
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