Vibrational spectroscopy: a clinical tool for cancer diagnostics

Vibrational spectroscopy techniques have demonstrated potential to provide non-destructive, rapid, clinically relevant diagnostic information. Early detection is the most important factor in the prevention of cancer. Raman and infrared spectroscopy enable the biochemical signatures from biological tissues to be extracted and analysed. In conjunction with advanced chemometrics such measurements can contribute to the diagnostic assessment of biological material. This paper also illustrates the complementary advantage of using Raman and FTIR spectroscopy technologies together. Clinical requirements are increasingly met by technological developments which show promise to become a clinical reality. This review summarises recent advances in vibrational spectroscopy and their impact on the diagnosis of cancer.

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.

Towards automated classification of clinical optical coherence tomography data of dense tissues

The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters, whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition, normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common requirements. The interest of the presented methods lies in the prospect of objective, quick, and/or post-acquisition processing. There is a potential to improve clinical decision making based on automated processing of OCT data.

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.

Near real-time classification of optical coherence tomography data using principal components fed linear discriminant analysis

An optical coherence tomography (OCT) prediction algorithm is designed and tested on a data set of sample images (taken from vegetables and porcine tissues) to demonstrate proof of concept. Preprocessing and classification of data are fully automated, at a rate of 60,000 A-scansmin on a standard computer and can be considered to deliver in near real-time. A data set consisting of nine groups was classified correctly in 82% of cases after cross-validation. Sets of fewer groups reach higher rates. The algorithm is able to distinguish groups with strong visual similarity among several groups of varying resemblance. Surface recognition and normalizing to the surface are essential for this approach. The mean divided by the standard deviation is a suitable descriptor for reducing a set of surface normalized A-scans. The method enables grouping of separate A-scans and is therefore straightforward to apply on 3-D data. OCT data can reliably be classified using principal component analysis combined with linear discriminant analysis. It remains to be shown whether this algorithm fails in the clinical setting, where interpatient variation can be greater than the deviations that are investigated as a disease marker.

Endoscopic screening and surveillance for Barrett's esophagus--clinical implications

There is now a clear causal relationship between symptomatic gastroesophageal reflux and esophageal adenocarcinoma (Lagergren et al, 1999). The risk factor is now identified as Barrett's metaplasia (Solaymani et al, 2004). Chronic reflux results in Barrett's metaplastic change, and the route to carcinoma is a stepwise progression, through dysplasia to invasive carcinoma (Jankowski et al, 2000). Earlier-stage disease is found in patients undergoing surveillance and is the major predictor of survival following surgery (Fountoulakis et al, 2004). Screening and surveillance by endoscopic biopsy regimen has profound implications for the allocation of healthcare resources and the provision of clinical services. Screening a high-risk group such as men with gastroesophageal reflux disease (GERD) will result in the detection of more patients with Barrett's esophagus, many of whom are asymptomatic. Once detected, questions remain as to surveillance intervals and the current methodology for surveillance. There are profound challenges with the accurate endoscopic and pathologic detection and categorization of Barrett's metaplasia, dysplasia , and, indeed, cancer. New endoscopic detection methods are being investigated to improve the diagnosis and definition of the premalignant phenotype. The detection of dysplasia requires increased surveillance and usually intervention either endoscopically or with surgery.