Diagnostic tissue spectroscopy and its applications to gastrointestinal endoscopy

Physically meaningful Monte Carlo approach to the four-flux solution of a dense multilayered system

Due to the complexity of the radiative transfer equation, light transport problems are commonly solved using either models under restrictive assumptions, e.g., N-flux models where infinite lateral extension is assumed, or numerical methods. While the latter can be applied to more general cases, it is difficult to relate their parameters to the physical properties of the systems under study. Hence in this contribution we present, first, a review of a four-flux formalism to study the light transport problem in a plane-parallel system together with a derivation of equations to evaluate the different contributions to the total absorptance and, second, as a complementary tool, a Monte Carlo algorithm with a direct correspondence between its inputs and the properties of the system. The combination of the four-flux model and the Monte Carlo approach provides (i) all convergence warranties since the formalism has been established as a limit and (ii) new added capabilities, i.e., both temporal (transient states) and spatial (arbitrarily inhomogeneous media) resolution. The support between the theoretical model and the numerical tool is reciprocal since the model is utilized to set a Monte Carlo discretization criterion, while the Monte Carlo approach is used to validate the aforementioned model. This reinforces the parallel approach used in this work. Furthermore, we provide some examples to show its capabilities and potential, e.g., the study of the temporal distribution of a delta-like pulse of light.

Emerging technology: applications of Raman spectroscopy for prostate cancer

There is a need in prostate cancer diagnostics and research for a label-free imaging methodology that is nondestructive, rapid, objective, and uninfluenced by water. Raman spectroscopy provides a molecular signature, which can be scaled from micron-level regions of interest in cells to macroscopic areas of tissue. It can be used for applications ranging from in vivo or in vitro diagnostics to basic science laboratory testing. This work describes the fundamentals of Raman spectroscopy and complementary techniques including surface enhanced Raman scattering, resonance Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, confocal Raman spectroscopy, stimulated Raman scattering, and spatially offset Raman spectroscopy. Clinical applications of Raman spectroscopy to prostate cancer will be discussed, including screening, biopsy, margin assessment, and monitoring of treatment efficacy. Laboratory applications including cell identification, culture monitoring, therapeutics development, and live imaging of cellular processes are discussed. Potential future avenues of research are described, with emphasis on multiplexing Raman spectroscopy with other modalities.

Developing an Instrument-Independent Algorithm for Raman Spectroscopy: A Case of Cancer Detection

One of the problems in the use of Raman spectroscopy for cancer detection in clinical application is the variety of Raman instruments, producing different spectra for the same sample, due to the nature of the measurement system. This prevents the measured spectra from different systems to be compared against one another without appropriate tools and techniques. Therefore, for each instrument one needs to spend considerable amount of time to prepare a set of reference data based on which the future measurements to be interpreted. For early diagnosis of cancer by Raman spectroscopy, there is a need for an algorithm by which such diagnosis can be made by any type of Raman instrument giving rise to the same findings. In the present study we have investigated the detection of breast cancer in three classes of breast samples (normal, benign and cancer) using three different Raman instruments (Almega, Bruker and R3000) to develop an algorithm that, irrespective of the type of Raman instrument, can be applied to the spectra to extract the features necessary to arrive at the same diagnosis. In doing so, we employed different pre-processing methods to eliminate the instrument-dependent effects on the spectra enabling us to fuse such spectra obtained from different instruments. Then, we classified the data using support vector machine (SVM) and multi-layer perception (MLP) to assess the degree to which the employed methods have been able to detect cancer. The results of the study showed that the range and resolution matching using spline interpolation, and noise and fluorescence elimination using wavelet and SNV normalizations were the most sensitive and accurate procedures for eliminating the instrumental specification-based effects and fusing the data from different instruments.

Comparative evaluation of the diagnostic performance of autofluorescence and diffuse reflectance in oral cancer detection: a clinical study

Autofluorescence (AF) and diffuse reflectance (DR) spectroscopic techniques have shown good diagnostic accuracies for noninvasive detection of oral cavity cancer. In the present study, AF and DR spectra recorded in vivo from the same set of sites in 65 patients were analyzed using Principal component analysis (PCA) and linear discriminant analysis (LDA). The effectiveness of these two techniques was assessed by comparison with gold standard and their discrimination efficiency was determined from the area under the receiver operator characteristic (AUC-ROC) curve. Analysis using a DR technique shows a higher AUC-ROC of 0.991 as against 0.987 for AF spectral data.