Nonlinear pattern recognition for laser‐induced fluorescence diagnosis of cancer

Majumder, Shovan K.; Ghosh, Nirmalya; Kataria, S. P.; Gupta, Pradeep Kumar

doi:10.1002/lsm.10191

Cited by 43 publications

(31 citation statements)

References 27 publications

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“…Evaluated methods include principle peak ratio, differential normalized fluorescence (DNF), bivariate DNF (2-D-DNF), principal component analysis, and correlation coefficient mapping. [56][57][58][59][60][61][62][63][64] Among these methods, DNF is a simple, straightforward method and provides excellent classification. 56 In the DNF analysis, the diagnostic features are extracted from the difference between the averaged cancerous and averaged normal tissue spectra.…”

Section: Optimizing Results: Processing and Interpretation Of Spectramentioning

confidence: 99%

Laser-induced fluorescence made simple: implications for the diagnosis and follow-up monitoring of basal cell carcinoma

et al. 2014

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Abstract. Noninvasive treatments are increasingly being used for the management of basal cell carcinoma (BCC), the predominant type of nonmelanoma skin cancer, making the development of noninvasive diagnostic technologies highly relevant for clinical practice. Laser-induced fluorescence (LIF) spectroscopy emerges as an attractive diagnostic technique for the diagnosis and demarcation of BCC due to its noninvasiveness, high sensitivity, real-time measurements, and user-friendly methodology. LIF relies on the principle of differential fluorescence emission between abnormal and normal skin tissues (ex vivo and in vivo) in response to excitation by a specific wavelength of light. Fluorescence originates either from endogenous fluorophores (autofluorescence) or from exogenously administered fluorophores (photosensitizers). The measured optical properties and fluorophore contributions of normal skin and BCC are significantly different from each other and correlate well with tissue histology. Photodynamic diagnosis (PDD) is based on the visualization of a fluorophore, with the ability to accumulate in tumor tissue, by the use of fluorescence imaging. PDD may be used for detecting subclinical disease, determining surgical margins, and following-up patients for residual tumor or BCC relapse. In this review, we will present the basic principles of LIF and discuss its uses for the diagnosis, management, and follow-up of BCC.

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Section: Optimizing Results: Processing and Interpretation Of Spectramentioning

confidence: 99%

Laser-induced fluorescence made simple: implications for the diagnosis and follow-up monitoring of basal cell carcinoma

et al. 2014

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“…Different methods are available for this purpose like principal component analysis (PCA), artificial neural network (ANN), and maximum representation and discrimination feature (MRDF). 31 In the present work we have employed PCA, since it is easy to extract spectral information, which could be used for understanding biochemical differences between the different classes of tissues in a more quantitative manner.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of normal, inflammatory, premalignant, and malignant oral tissue: A Raman spectroscopy study

et al. 2005

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Optical spectroscopy methods are fast emerging as potential alternatives for early diagnosis of cancer. A Raman spectroscopy method for discrimination of normal and malignant oral tissues has been developed by us earlier. It is necessary to evaluate and establish the validity of the approach before it can be routinely used. In the present study, our Raman spectroscopy investigations are extended further to evaluate the efficacy of the technique to discriminate between normal, inflammatory, premalignant, and malignant conditions in oral tissue. Spectral profiles of normal, malignant, premalignant, and inflammatory conditions show pronounced differences between one another. Spectra of normal tissues can be attributed mainly to lipids whereas pathological tissue spectra are dominated by proteins. Principal components analysis (PCA) of the spectral data sets belonging to the four different categories showed that scores of factors differentiated between

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“…This method is not relevant to the scattering of the detection environment and the alteration of optical system but can't identify the location of cancer [66]. Simultaneously, other auxiliary detection methods will be performed as well to get more convincing results such as the biopsy spectra analysis [17,67].…”

Section: Lif For Cancer Diagnosismentioning

confidence: 99%

Laser-induced fluorescence: Progress and prospective for in vivo cancer diagnosis

et al. 2013

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Cancer diagnosis and treatment are of great interest due to the high death rate of cancer. To improve the cure rates of cancer, a diagnostic tool which can detect and treat cancer at initial stages is great needed. Laser-induced fluorescence (LIF) is an adequate analytical technique with advantages of high sensitivity, low sample consumption, short testing time, and suitable for in situ testing. Therefore, it has become one of the most widely used spectroscopic methods for cancer in vivo diagnosis in recent years. This review mainly focuses on the applications of in vivo LIF to distinguish premalignant, malignant from normal tissues in a variety of organ systems, such as lung breast, colon, cervix, esophagus, and bronchus. The potential influence factors for cancer diagnostics and the subsequent suitability of the method to different applications are well discussed. Meanwhile, the technical merits and weaknesses of the LIF technology for cancer diagnosis are also evaluated. Furthermore, different exogenous fluorophores, endogenous fluorophores, and fluorophores synthesized in the tissue are compared on their active principle and effect contrast. The technical potentials of LIF for further development and future applications are also presented as well in this review.

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Nonlinear pattern recognition for laser‐induced fluorescence diagnosis of cancer

Cited by 43 publications

References 27 publications

Laser-induced fluorescence made simple: implications for the diagnosis and follow-up monitoring of basal cell carcinoma

Laser-induced fluorescence made simple: implications for the diagnosis and follow-up monitoring of basal cell carcinoma

Discrimination of normal, inflammatory, premalignant, and malignant oral tissue: A Raman spectroscopy study

Laser-induced fluorescence: Progress and prospective for in vivo cancer diagnosis

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