Presurgical mapping of basal cell carcinoma or squamous cell carcinoma by confocal laser endomicroscopy compared to traditional micrographic surgery: a single-centre prospective feasibility study.

BACKGROUND: At present, no ideal diagnostic tools exist in the market to excise cancer tissue with the required safety margins and to achieve optimal aesthetic results using tissue-conserving techniques. OBJECTIVES: In this prospective study, confocal laser endomicroscopy (CLE) and the traditional gold standard of magnifying glasses (MG) were compared regarding the boundaries of in vivo basal cell carcinoma and squamous cell carcinoma. MATERIALS & METHODS: Tumour diameters defined by both methods were measured and compared with those determined by histopathological examination. Nineteen patients were included in the study. RESULTS: The CLE technique was found to be superior to excisional margins based on MG only. Re-excision was required in 68% of the cases following excision based on MG evaluation, but only in 27% of the cases for whom excision margins were based on CLE. CONCLUSION: Our results are promising regarding the distinction between tumour and healthy surrounding tissue, and indicate that presurgical mapping of basal cell carcinoma and squamous cell carcinoma is possible. The tool itself should be developed further with special attention to early detection of skin cancer.

Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.

Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information.