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Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics
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IOP PUBLISHING
PHYSICA SCRIPTA
Phys. Scr. T157 (2013) 014057 (4pp)
doi:10.1088/0031-8949/2013/T157/014057
Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics L Lenhardt, I Zekovi´c, T Drami´canin and M D Drami´canin Institute for Nuclear Sciences ‘Vinca’, University of Belgrade, PO Box 522, 11001 Belgrade, Serbia E-mail:
[email protected]
Received 25 August 2012 Accepted for publication 14 January 2013 Published 15 November 2013 Online at stacks.iop.org/PhysScr/T157/014057 Abstract Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%. PACS numbers: 87.19.xj, 87.64.K−, 87.64.kv
(SFS) can be successfully used in cancer detection (Vo-Dinh 2000, Vengadesan et al 2002, Drami´canin et al 2005, 2006, 2011). This method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. The advantage of a synchronous spectrum over ordinary emission spectra is that it often has more features and provides more information. The obtained data are usually highly correlated with subtle differences between abnormal and normal tissues, and for this purpose artificial neural networks (ANN) can be very useful for building a diagnostic model. ANN is an adaptive system that modifies structure based on input findings to generate robust output. The benefit of this method is its consistency and objectivity due to lack of human fatigue and bias. In this paper, two different types of ANN were trained, the self-organizing map (SOM) and the feed-forward neural network. SOM is one of the most popular neural networks that convert high-dimensional nonlinear statistical relationships into simple geometric relationships in an unsupervised way (Kohonen 2001).
1. Introduction Skin cancer is one of the most common malignancies worldwide. Late detection delivers high mortality rates, but if diagnosed in the early stages it is one of the most treatable forms of cancer. Taking this problem into account, developing new methods for cancer diagnosis is of crucial significance. In the last few decades, in the field of cancer diagnostics, fluorescence spectroscopy has proven to be a very promising technique (Alfano et al 1987, Sterenborg et al 1994, Palmer et al 2003). Human tissues are a complex mixture of different molecules and some of these molecules, called endogenous fluorophores, have the ability to absorb and emit light of different wavelengths. The concentration and distribution of various fluorophores in the skin such as nicotinamide adenine dinucleotide (NADH), flavine adenine dinucleotide (FAD), keratin, collagen, elastin, amino acids, lipids and porphyrins are the fundamentals for discrimination between cancer and normal tissue by fluorescence spectroscopy. In recent years it has been shown that synchronous fluorescence spectroscopy 0031-8949/13/014057+04$33.00
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© 2013 The Royal Swedish Academy of Sciences
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Phys. Scr. T157 (2013) 014057
L Lenhardt et al
Figure 1. Total synchronous fluorescence spectra of different skin sample types: (a) melanoma, (b) nevus and (c) normal skin.
Unlike SOM, the feed-forward neural network is trained in a supervised way with prior knowledge of a sample’s group membership by data entering at the inputs passing through layers of neurons until it arrives at the outputs without any feedback information between layers (Jiang et al 2003). ANN are often trained to be further used as classification models (Rhee et al 2005, Drami´canin et al 2009). The aim of this work was to investigate the possibility of building a skin cancer diagnostic method by training ANN using total fluorescence spectra of different types of skin lesions.
2. Materials and methods Figure 2. Synchronous fluorescence spectra taken at 1λ = 70 nm of melanoma (full line), normal skin (dash line) and nevus (dot line) samples.
Synchronous fluorescence spectra of 50 skin samples (12 melanoma, 18 nevus and 20 normal skin samples) were measured ex vivo using a fluorescence spectrophotometer (Perkin Elmer LS45) in constant-wavelength mode. The spectra were collected at a scan rate of 200 nm min−1 in the excitation range from 330 to 550 nm and the wavelength offset range 1λ from 30 to 120 nm and automatically normalized to excitation by the instrument. Samples were obtained from human patients soon after surgical resection, histopathologically identified and then measured by the instrument at room temperature. In order to reduce the dimensionality of obtained spectra, principal component analysis (PCA) was applied. For training, calculated PCA components were introduced in SOM and the feed-forward neural network. To assess classification error, they were tested with new samples unknown to both ANN.
these regions originates from several fluorophores like the co-enzymes NADH and FAD. A skin cancer diagnostic method based on measurement of SFS and a classification model built using two different ANN was developed in several steps. First reduction of dimensionality was necessary due to the large size of gathered SFS. For that purpose PCA was applied on SFS data and a five-component PCA model was acquired. PCA is an unsupervised statistical method used to distinguish and identify patterns in data and express them in such a manner as to point out their similarities and differences. This method transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components, which account for most of the variance in the observed variables. This allowed us to explore our data and to determine for which wavelength offset value (1λ) best separation between groups was achieved. After examining the results of PCA, it was concluded that the best differentiation between classes is for 1λ = 70 nm, figure 2. In figure 3 one can see an obvious distinction between classes. Taking this into consideration, 25 samples’ PCA scores for 1λ = 70 nm were used to train SOM and the feed-forward neural network. In order to enlarge the number of samples for ANN training, we created a normally distributed set of data based on the mean value and standard deviation of 25 samples used for training. With a new data set of 2000 samples we trained both ANN. SOM learns to group data based on similarity and topology, and as a result it assigns the same indices to each class. It is used for reduction of dimensionality and clustering
3. Results and discussion Figure 1 presents total synchronous fluorescence spectra in the form of contour diagrams of different skin samples: normal skin, nevus and melanoma. Emission patterns reflect the specificity of endogenous fluorophores and their microenvironments in skin. It can be clearly seen that the main differences in the fluorescence of skin lesions are observable in several spectral regions. The first region spans the excitation wavelength from 330 to 400 nm and the synchronous interval from 30 to 55 nm. Structural proteins of the extracellular matrix, collagen and elastin have maximum excitation in this region (Ramanujam 2000). The second and third spectral regions cover the excitation wavelength interval from 430 to 480 nm, 350 to 400 nm and the synchronous interval from 30 to 50 nm, 65 to 90 nm, respectively. Fluorescence of 2
Phys. Scr. T157 (2013) 014057
L Lenhardt et al
Figure 5. Schematic representation of the feed-forward network architecture.
Figure 3. PCA scores obtained from SFS data taken at 1λ = 70 nm of melanoma, normal skin and nevus samples.
2 and 3%, while for the feed-forward network it was between 3 and 4%.
4. Conclusion In this work, we used statistical analysis and ANN to classify data of synchronous fluorescence spectra with the aim of developing a diagnostic method for skin cancer diagnosis. The fluorescence spectra of three different types of skin lesions, melanoma, nevus and normal skin, revealed differences between them due to differences in concentration of various fluorophores and their microenvironments. The application of PCA provided data with enlarged variances between sample groups. It is shown that both SOM and feed-forward neural networks gave promising results with a 96–98% success tissue classification rate. Moreover, the presented method is fast, sensitive, inexpensive and non-destructive. Based on these findings, we can conclude that SFS combined with neural network-based classification has good potential in melanoma diagnostics.
Figure 4. Schematic representation of the SOM architecture.
data. We introduced a 2000 × 5 matrix (2000 samples and 5 parameters describing every sample) to SOM with 3 neurons (size 1 × 3), figure 4, and started training with 150 iterations. Through all this, SOM did not have any information about the sample’s group membership (unsupervised learning). When trained, SOM was tested with 25 new samples unknown to the network and the classification error was acquired. This process of SOM training and testing was repeated several times, every time using a different combination of data for training and testing. Doing this we obtained a more accurate efficiency overview of this method. Feed-forward networks, figure 5, contain series of layers. The first layer gets network input data, and each subsequent layer is connected to the previous layer while the final layer gives network output. They are usually used for input to output mapping. We used the feed-forward network with one hidden layer to build a classification model by giving it as input the 2000 × 5 matrix, the same matrix used for SOM training, and the matrix with information of class membership of all 2000 samples (supervised learning). The process of testing was the same as for SOM; several models were built and tested using different combinations of data, and the mean classification error for all models was calculated. Based on the obtained classification errors of neural networks, we found that both networks provide high sensitivity with classification errors between 2 and 4%. For different input data the SOM classification error was between
Acknowledgment This work was supported by the Serbian Ministry of Science and Technological Development (project numbers 173049 and 45020).
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