Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo

In Vivo Optical Coherence Tomography for the Detection, Subtyping, and Margin Assessment of Facial Basal Cell Carcinoma: A Comparative Study with Histopathology

Background: Basal cell carcinoma (BCC) is the most common skin cancer, with several subtypes that vary from one another in biological behaviour and therapeutic consequences. The aim of the study was to evaluate in vivo OCT diagnostic accuracy to detect and subtype facial BCCs, using histopathology as the gold standard. Patients and Methods: This single-centre, prospective, diagnostic accuracy study was carried out on 136 patients who were presenting with a total of 220 clinically suspicious facial lesions for BCC. All lesions were imaged by OCT before their surgical excision. OCT findings were compared to the histopathological results in the respect of detection of BCC, subtype, tumour depth, and margin status. Results: OCT showed an excellent diagnostic performance for the detection of BCC in general: sensitivity, 96.8%; specificity, 98.2%; and accuracy, 97.5%. The AUC for the detection of BCC was 0.97. Subtype sensitivity for OCT was highest for superficial, 93.1%, and nodular BCC, 92.1%, and marginally lower for micronodular, 89.3%, and infiltrative subtypes, 90.0%. Depth measurements of OCT-derived tumour strongly correlated with those obtained by histopathology: mean depth, 2.3 ± 0.9 mm for OCT versus 2.2 ± 0.8 mm for histopathology; p = 0.08. Conclusions: The performed OCT showed very good agreement in the detection, subtyping, and preoperative assessment of BCC facial lesions. In addition to its non-invasive characteristics, its robustness regarding the evaluation of tumour depth and margins makes OCT very promising for improved surgical planning by reducing unnecessary excisions.

Artificial Intelligence in Dermoscopy: Enhancing Diagnosis to Distinguish Benign and Malignant Skin Lesions

This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google's platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google's servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model's proficiency in distinguishing between benign and malignant skin lesions. The use of Google's Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model's robustness and facilitate its integration into clinical practice.