Line‐field confocal optical coherence tomography of basal cell carcinoma: A retrospective study on diagnostic performance.

Introduction Objectives Materials and Methods Results Conclusions Line‐field confocal optical coherence tomography (LC‐OCT) represents one of the newest non‐invasive in vivo skin imaging techniques. Previous studies described morphologic criteria of basal cell carcinoma (BCC) under LC‐OCT examination. Preliminary data on LC‐OCT diagnostic performance for BCC have recently been published but showed only a modest improvement compared to dermoscopy, possibly due to study limitations.The main objective of this study was to find diagnostic performance parameters of LC‐OCT for differentiating BCC from its clinical/dermoscopic mimickers and for discriminating among BCC subtypes. An additional objective was to suggest a simple, user‐friendly diagnostic algorithm based on the most powerful LC‐OCT features in the field of BCC and its imitators.Equivocal BCC lesions imaged with an LC‐OCT device prior to biopsy/excision were included. Three observers blinded for histopathological diagnosis retrospectively formulated clinical, dermoscopic and LC‐OCT diagnoses and evaluated LC‐OCT features for each study lesion.A total of 303 lesions (173 BCCs and 130 non‐BCCs) from 225 patients were included. For the differentiation of BCC from BCC imitators, the use of LC‐OCT increased the diagnostic accuracy compared to clinical examination by 24% and compared to dermoscopy by 12%. For the discrimination of sBCC from other BCC subtypes, LC‐OCT increased the diagnostic accuracy compared to clinical examination by 18% and compared to dermoscopy by 12%. The presence of lobule with millefeuille pattern was a significant feature for BCC diagnosis. Lobule shape and location allowed BCC subtype discrimination.The accuracy of BCC diagnosis can be increased by at least 12% with the use of LC‐OCT compared to clinical and dermoscopic examinations alone, both in terms of BCC differentiation from clinical/dermoscopic imitators and in terms of BCC‐subtype discrimination. A diagnostic algorithm based on significant features for BCC diagnosis is proposed, for which further validation is required. [ABSTRACT FROM AUTHOR]