Review: optical micrometer resolution scanning for non-invasive grading of precancer in the human uterine cervix.

Management of cervical precancer is archetypal for other cancer prevention programmes but has to consider diagnostic and logistic challenges. Numerous optical tools are emerging for non-destructive near real-time early diagnosis of precancerous lesions of the cervix. Non-destructive, real-time imaging modalities have reached pre-commercial status, but high resolution mapping tools are not yet introduced in clinical settings. The NCBI PubMed web page was searched using the keywords 'CIN diagnosis' and the combinations of 'cervix {confocal, optical coherence tomography, ftir, infrared, Raman, vibrational, spectroscopy}'. Suitable titles were identified and their relevant references followed. Challenges in precancer management are discussed. The following tools capable of non-destructive high resolution mapping in a clinical environment were selected: confocal microscopy, optical coherence tomography, IR spectroscopy, and Raman spectroscopy. Findings on the clinical performance of these techniques are put into context in order to assist the reader in judging the likely performance of these methods as diagnostic tools. Rationale for carrying out research under the prospect of the HPV vaccine is given.

Optical coherence tomography: a potential tool for unsupervised prediction of treatment response for Port-Wine Stains.

BACKGROUND: Treatment of Port-Wine Stains (PWS) suffers from the absence of a reliable real-time tool for monitoring a clinical endpoint. Response to treatment varies substantially according to blood vessel geometry. Even though optical coherence tomography (OCT) has been identified as a modality with potential to suit this need, it has not been introduced as a standard clinical monitoring tool. One reason could be that - although OCT acquires data in real-time - gigabyte data transfer, processing and communication to a clinician may impede the implementation as a clinical tool. OBJECTIVES: We investigate whether an automated algorithm can address this problem. METHODS: Based on our understanding of pulsed dye laser treatment, we present the implementation of an unsupervised, real-time classification algorithm which uses principal components data reduction and linear discriminant analysis. We evaluate the algorithm using 96 synthesized test images and 7 clinical images. RESULTS: The synthesized images are classified correctly in 99.8%. The clinical images are classified correctly in 71.4%. CONCLUSIONS: Principal components-fed linear discriminant analysis (PC-fed LDA) may be a valuable method to classify clinical images. Larger sampling numbers are required for a better training model. These results justify undertaking a study involving more patients and show that disease can be described as a function of available treatment options.