Early detection of Basal Cell Carcinoma using Optical Coherence Tomography and Pattern Recognition Techniques
Description Basal cell carcinoma (BCC) is the most common tumor in light-skinned humans. BCC is induced at least in part by ultraviolet irradiation and with the aging of the American population, these tumors will continue to cause significant morbidity. Due to their limited metastatic potential, BCC are usually not fatal. However, because of their propensity to invade and destroy surrounding tissues, early treatment is essential. Especially in the face, one of the common sites for this tumor, destruction of surrounding tissues such as the eye or nose is a major problem. In the face, the treatment of choice is excisional surgery. Since BCC usually invades the tissues surrounding the main tumor mass without causing clinically visible changes, determination of the margins of the tumor becomes difficult. This has led to the development of Mohs micrographic surgery (MMS). In this type of surgery the main tumor is removed and then the edges of the excised tissue are processed for histological examination. If the tumor can still be detected histologically at the edges of the excised tissue, more tissue is removed until the margins are " clear ". Because of the time needed to mark the excised tissue and process the frozen sections for histological examination, MMS is a time consuming and expensive procedure. The specific aim of this proposal is to combine a new imaging technology-Optical Coherence To-mography-with the proven success of pattern classification techniques and apply these to the diagnosis and early detection of BCC. OCT is a non-invasive, rapid technique for imaging slices of tissue at an unprecedented resolution of 10 micrometers or less. This allows the imaging of the fine structures of the skin necessary for determining tumor margins. Our research will develop procedures for 1) acquiring images of skin at a depth of WHATEVER [ask Zhonping] 2) Compare the images obtained by OCT at the tumor's margin with the tumor margins defined by histological assessment during MMS. 3) Evaluate the ability of pattern recognition techniques for automating the diagnosis.