Surgical resection of skin cancer implies safety margins delineation: currently, surgeons have no diagnostic aid to narrow or widen such margins if necessary. A promising approach is the use of optical methods, which can be used non-invasively and offer real-time diagnostic assistance.
This study presents the results of classification of autofluorescence (AF) and diffuse reflectance (DR) spectra obtained in vivo on skin Basal Cell Carcinomas (BCC) and Squamous Cell Carcinomas (SCC), Actinic Keratoses (AK) and Healthy skin (H) of 140 patients. The bimodal spectroscopic instrument used in this study uses five LEDs for fluorescence excitation at wavelengths peaks between 365 and 415 nm, and a xenon lamp featuring 350-800 nm emission range to obtain AF and DR spectra for four source-detector distances (from 400 to 1000 μm).
The classification (C vs H, H vs AK) was done by support vector machine, discriminant analysis, and multilayer perceptron. Final accuracy of two-class classification tests for almost all pairs of classes was more than 80%. This study presents a comparison of the performance of these combination of methods with the standard clinical procedure.
Colorectal cancer is the second most common cancer and the second with the highest associated deaths in the world. Methods used in clinical practice for colon cancer diagnosis are fairly effective but quite unpleasant and not always applicable in situations where the patient has symptoms of colonic obstruction. This problem can be solved by the use of optical methods that can be applied less invasively.
This study presents the results of classification of cancerous and healthy colon tissue absorption coefficient spectra. The absorption coefficient was measured using direct calculations from the total reflectance and total transmittance spectra obtained ex vivo. Classification was performed using support vector machine, multilayer perceptron and linear discriminant analysis.
Information on skin phototype and ages is of cosmetic and medical interest in some procedures like objective evaluation of cosmetic treatments effectiveness, laser wavelength choice, risk of skin cancer recurrence and skin evaluation before cosmetic surgeries. Phototype may be evaluated using the Fitzpatrick questionnaire whose results are impaired by patients’ subjective answers; melanometers may be used but are not always available in dermatology practice. Tewameter, corneometer or cutometer are used to evaluate skin features that may be related to skin age but they lack evaluation of skin internal structure directly related to skin age (fibrosis, elastosis, etc.). Optical spectroscopy combining autofluorescence (AF) and diffuse reflectance (DR) may be a promising and non-invasive alternative to these tests.
In the current study, a bimodal spectroscopic device was used to obtain in vivo spatially resolved AF and DR spectra of skin in the visible range. Five LEDs featuring wavelength peaks at 365, 385, 395, 400 and 415 nm and a xenon lamp featuring a 350-800 nm spectral emission were used as light sources. Four source-detector separation (SDS) were used: 400, 600, 800, and 1000 μm.
Spectra were taken in different anatomical sites on 131 patients of different age and gender during a clinical study. Spectra were analysed using classification (support vector machine and multilayer perceptron) and regression (multilayer perceptron, linear, kernel ridge and Lasso) methods. Results of skin phototype and age estimation from AF and DR spectra obtained in vivo using machine learning methods will be presented and discussed.
KEYWORDS: Skin, Skin cancer, Feature extraction, Data processing, Principal component analysis, In vivo imaging, Diffuse reflectance spectroscopy, Autofluorescence
Non-invasive diagnosis of skin pathologies as skin cancer using optical methods has become increasingly common in recent years. However, the related skin data processing is often quite complex, and the way in which this step is carried out can significantly affect the final results. This study presents the results of diffuse reflectance spectra (with spectral range of the emission source is 300-800 nm) and autofluorescence spectra (with 7 autofluorescence excitation wavelengths in the 360-430 nm range) obtained in vivo from precancerous and benign skin lesions of various types (compensatory hyperplasia, atypical hyperplasia and dysplasia). The skin lesions were modelled using a preclinical model in mice. Spectra were taken in the range of 317 - 789 nm at three different source-detector separations: 271, 536 and 834 μm. The spectra obtained were processed using statistical feature extraction techniques, traditional machine learning (support vector machine, linear discriminant analysis, k-nearest neighbors) and deep learning methods (artificial neural network, convolutional neural network, autoencoder). This study presents a comparison of the performance of these methods and their combinations for multiclass classification of skin lesions.
Optical biopsy methods, which consists of analysing the response of tissue to light excitation, are being increasingly used in recent years for the diagnosis of skin pathologies. At the same time, the use of multimodal methods often significantly increases diagnostic efficiency as well as extending the limits of applicability of the methods. This contribution presents the results of in vivo analysis of precancerous and benign skin conditions (compensatory hyperplasia, atypical hyperplasia and dysplasia) in mice preclinical model, based on bimodal spectroscopic data, including multiply excited autofluorescence with 7 autofluorescence excitation wavelengths in the 360-430 nm range and diffuse reflectance spectroscopy with xenon lamp, that emits mainly in the 300-800 nm spectral range, as a source. The instrument used in this study provided the ability to collect spectra in the spectral range 317 - 789 nm at three different source-detector separations: 271, 536 and 834 μm. The results were processed using machine learning methods (principal component analysis, support vector machine, linear discriminant analysis, artificial neural network) and then various data fusion methods (Stacking, Begging, Boosting, Voting) were implemented to combine the results of analysis of all the modalities. This study presents a comparison of the performance of these data fusion methods. The results obtained in this work can be further applied to the diagnosis of carcinoma using optical biopsy methods.
The aim of the current study is to evaluate the classification accuracy and provide corresponding biological interpretation of four classification methods used on autofluorescence (AF) and diffuse reflectance (DR) spectra acquired in vivo on healthy human skin of different phototypes, civil and apparent age groups. Spectroscopic data were acquired on 91 patients using the SpectroLive device. The latter spatially and spectrally-resolved device features four source-to-detector distances (D1-D4) and six excitation light sources: 5 peaks for AF and one broadband white light for DR. For all patients, spectra were acquired on two healthy skin sites i.e. hand palm and inner wrist chosen for their low sun exposure. Four classification methods were tested: Support Vector Machine, K-Nearest Neighbors, Linear Discriminant Analysis and Artificial Neural Network. All combinations of excitation wavelengths, distances and skin sites acquisition were tested to find out the best classification results following a training step on 67 % of the dataset and a validation step on 33 % of the dataset. Classification accuracies were compared using Principal Components Analysis and statistical features. For civil and biological skin age groups discrimination, best classification results (70 % and 76 % respectively) were obtained when combining autofluorescence spectral features from three excitation wavelengths (385, 395 and 405 nm) all acquired at the shortest distance (400 µm) on hand palm. The combination of AF, inner wrist and the longest distance (1 mm) gave the best classification results (76 %) for phototype groups discrimination.
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