Construction of an Aggressiveness Diagnostic Model for Basal Cell Carcinoma Using Multimodal Ultrasound Features

Area of Science:

• Dermatology
• Medical Imaging
• Oncology

Background:

• Basal cell carcinoma (BCC) is the most common skin cancer.
• Accurate assessment of BCC aggressiveness is crucial for effective treatment planning.
• Current methods for assessing BCC aggressiveness can be invasive or lack precision.

Purpose of the Study:

• To develop a nomogram-based diagnostic model for assessing BCC aggressiveness.
• To integrate clinical data with multimodal ultrasound imaging features.
• To establish an evidence-based framework for clinical decision-making in BCC management.

Main Methods:

• Retrospective analysis of 120 pathologically confirmed BCC lesions.
• Classification of BCC lesions into high-risk (n=30) and low-risk (n=90) groups.
• Comparison of clinical and multimodal ultrasound features (5-18 MHz transducer) between groups.
• Logistic regression to identify predictors of aggressiveness and nomogram development/validation.

Main Results:

• Factors significantly associated with BCC aggressiveness included infiltration level (IL), intralesional echoic pattern (IEP), maximum infiltration depth (MID), and average elastic Young's modulus (Eave).
• Multivariate analysis confirmed IL, IEP, MID, and Eave as independent predictors of high-risk BCC.
• The developed nomogram demonstrated robust predictive performance with a concordance index (C-index) of 0.921.

Conclusions:

• A multimodal ultrasound-based nomogram effectively predicts BCC aggressiveness.
• This nomogram serves as a noninvasive tool for clinical assessment.
• The model aids in treatment planning for basal cell carcinoma.