Clinical trial · Observational
AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images
Risk Classification and Prediction of Histopathological Subtypes in Basal Cell Carcinoma Using a CNN-Based Artificial Intelligence Model on Dermoscopic Images
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This retrospective observational study aims to develop and evaluate a convolutional neural network (CNN)-based artificial intelligence model for risk classification and histopathological subtype prediction of basal cell carcinoma (BCC) using clinical and dermoscopic images. Histopathologically confirmed BCC cases from a dermatology archive will be included. The primary objective is to assess the diagnostic performance of the CNN model in classifying BCC as low-risk or high-risk. Secondary objectives include predicting histopathological subtypes and comparing the model's performance with that of dermatology physicians. Histopathological diagnosis will serve as the reference standard. All archived data will be anonymized before analysis.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Basal Cell Carcinoma | Basal Cell Carcinoma | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Low-Risk Basal Cell Carcinoma
- description
- Patients with histopathologically confirmed low-risk basal cell carcinoma, including nodular, superficial, pigmented, adenoid, solid, and nodulocystic subtypes. Clinical and dermoscopic images will be used for artificial intelligence-based risk classification and subtype prediction.
- label
- High-Risk Basal Cell Carcinoma
- description
- Patients with histopathologically confirmed high-risk basal cell carcinoma, including infiltrative, micronodular, morpheaform, and basosquamous subtypes. Clinical and dermoscopic images will be used for artificial intelligence-based risk classification and histopathological subtype prediction.
Primary outcomes (1)
- measure
- Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
- timeFrame
- Baseline
- description
- Diagnostic accuracy of the convolutional neural network model in distinguishing low-risk and high-risk basal cell carcinoma using dermoscopic images, compared with histopathological diagnosis as the reference standard.
Secondary outcomes (2)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 0 Years
- Maximum age
- 100 Years
Show eligibility criteria text
Inclusion Criteria: * Patients with histopathologically confirmed basal cell carcinoma. * Cases with a specified histopathological subtype. * Availability of dermoscopic images with sufficient image quality and resolution for artificial intelligence analysis. Exclusion Criteria: * Cases without histopathological confirmation of basal cell carcinoma. * Cases with unspecified histopathological subtype. * Images with insufficient quality or resolution for artificial intelligence analysis. * Cases without available dermoscopic images.
References
Publications (0)
Data not yet available