Denna sida har översatts automatiskt och översättningens korrekthet kan inte garanteras. Vänligen se engelsk version för en källtext.

AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images (BCC-AI)

29 juni 2026 uppdaterad av: Tugce Nur Izbudak Kara, Istanbul Training and Research Hospital

Risk Classification and Prediction of Histopathological Subtypes in Basal Cell Carcinoma Using a CNN-Based Artificial Intelligence Model on Dermoscopic Images

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.

Studieöversikt

Status

Rekrytering

Betingelser

Detaljerad beskrivning

Basal cell carcinoma (BCC) is the most common skin malignancy and comprises histopathological subtypes with different biological behaviors, recurrence risks, and treatment implications. Accurate identification of high-risk and low-risk subtypes is important for clinical decision-making. Dermoscopy improves diagnostic accuracy in BCC; however, prediction of histopathological risk categories based solely on dermoscopic findings remains challenging.

This retrospective observational study will use archived clinical and dermoscopic images, histopathology reports, and clinical records of patients with histopathologically confirmed BCC. All data will be anonymized before analysis. Images containing identifiable patient information will be excluded.

A convolutional neural network (CNN)-based artificial intelligence model will be developed using clinical and dermoscopic images. Images will undergo preprocessing, including standardization of image size, normalization procedures, and removal of potentially identifiable information. The dataset will be divided into training, validation, and test sets while maintaining separation at the patient level to avoid data leakage.

The primary outcome is the diagnostic performance of the CNN model for classification of BCC into low-risk and high-risk histopathological groups. Secondary outcomes include prediction of histopathological subtypes and comparison of model performance with dermatologist assessments. Histopathological diagnosis will serve as the reference standard.

Model performance will be evaluated using accuracy, sensitivity, specificity, precision, recall, F1 score, and area under the receiver operating characteristic curve (ROC-AUC). Comparisons between the artificial intelligence model and physician assessments will be performed using appropriate statistical methods. Interobserver agreement may also be assessed when applicable.

Studietyp

Observationell

Inskrivning (Beräknad)

2500

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studiekontakt

Studieorter

  • Turkiet (Türkiye)
    • Istanbul
      • Istanbul, Istanbul, Turkiet (Türkiye), 34000
        • Rekrytering
        • Istanbul Training and Research Hospital
        • Kontakt:
        • Kontakt:
        • Huvudutredare:
          • Ayse Esra Koku Aksu, MD
        • Underutredare:
          • Tugce Nur Izbudak Kara, MD
        • Underutredare:
          • Duygu Yamen, MD

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Barn
  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

Nej

Testmetod

Icke-sannolikhetsprov

Studera befolkning

The study population consists of patients with histopathologically confirmed basal cell carcinoma who have dermoscopic images of sufficient quality for artificial intelligence analysis and documented histopathological subtype information. Archived clinical and dermoscopic images collected at Istanbul Training and Research Hospital will be retrospectively analyzed.

Beskrivning

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.

Studieplan

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

Kohorter och interventioner

Grupp / Kohort
Low-Risk Basal Cell Carcinoma
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.
High-Risk Basal Cell Carcinoma
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.

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups
Tidsram: Baseline
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.
Baseline

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Diagnostic accuracy (accuracy, sensitivity, specificity, F1-score and ROC-AUC) of convolutional neural network for histopathological subtype prediction of basal cell carcinoma using dermoscopic images
Tidsram: baseline
Diagnostic performance of the convolutional neural network in predicting histopathological subtypes of basal cell carcinoma from dermoscopic images compared with histopathological diagnosis (reference standard). Diagnostic accuracy will be assessed using accuracy, sensitivity, specificity, precision, F1-score and ROC-AUC.
baseline
Diagnostic accuracy (accuracy, sensitivity, specificity, F1-score and ROC-AUC) of artificial intelligence compared with dermatologists for basal cell carcinoma risk classification
Tidsram: baseline
Comparison of diagnostic performance between the artificial intelligence model and dermatologists in risk classification of basal cell carcinoma. Performance will be assessed using accuracy, sensitivity, specificity, precision, F1-score and ROC-AUC.
baseline

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

Istanbul Training and Research Hospital

Utredare

  • Huvudutredare: Ayse Esra Koku Aksu, MD, Istanbul Training and Research Hospital

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Faktisk)

22 maj 2026

Primärt slutförande (Beräknad)

22 maj 2027

Avslutad studie (Beräknad)

22 maj 2027

Studieregistreringsdatum

Först inskickad

19 juni 2026

Först inskickad som uppfyllde QC-kriterierna

29 juni 2026

Första postat (Faktisk)

30 juni 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

30 juni 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

29 juni 2026

Senast verifierad

1 juni 2026

Mer information

Termer relaterade till denna studie

Nyckelord

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • SBU-IEAH-DERM-BCCAI-163
  • IEAH-EC-163 (Annan identifierare: Istanbul Training and Research Hospital Non-Interventional Clinical Research Ethics Committee)

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

JA

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

Denna information hämtades direkt från webbplatsen clinicaltrials.gov utan några ändringar. Om du har några önskemål om att ändra, ta bort eller uppdatera dina studieuppgifter, vänligen kontakta register@clinicaltrials.gov. Så snart en ändring har implementerats på clinicaltrials.gov, kommer denna att uppdateras automatiskt även på vår webbplats .

Prenumerera