Gastric cancer is a completely curable cancer when it can be detected at its early stage. Thus, because early detection of gastric cancer is important, cancer screening by gastroscopy is performed. Recently, the hyperspectral camera (HSC), which can observe gastric cancer at a variety of wavelengths, has received attention as a gastroscope. HSC permits the discerning of the slight color variations of gastric cancer, and we considered its applicability to a gastric cancer diagnostic support system. In this paper, after correcting reflectance to absorb the individual variations in the reflectance of the HSC, a gastric cancer diagnostic support system was designed using the corrected reflectance. In system design, the problems of selecting the optimum wavelength and optimizing the cutoff value of a classifier are solved as a pattern recognition problem by the use of training samples alone. Using the hold-out method with 104 cases of gastric cancer as samples, design and evaluation of the system were independently repeated 30 times. After analyzing the performance in 30 trials, the mean sensitivity was 72.2% and the mean specificity was 98.8%. The results showed that the proposed system was effective in supporting gastric cancer screening.
Gastric cancer is a completely curable cancer when it can be detected at its early stage. Thus, because early detection of gastric cancer is important, cancer screening by gastroscopy is performed. However, about 20% of gastric cancers are reportedly missed [
Because there is wide variability of cancer, not just in gastric cancer, even the same carcinoma can differ from person to person. Therefore, a hyperspectral camera (HSC), which can observe gastric cancer at a variety of wavelengths, has received attention [
The HSC can discern the slight color differences of gastric cancer [
In this paper, a certain type of reflectance correction is performed to absorb the individual differences, and a system is designed using the corrected reflectance. The design of such a system involves two tasks. One is that of selecting the optimum wavelength. The HSC can observe gastric cancer with various wavelengths, but, considering cost and real-time processing, the reflectance is obtained from the optimum wavelength alone. The other task is that of determining cutoff values to distinguish gastric cancer in classifier design. These problems are solved with a pattern recognition method using training samples alone. First, with regard to the selection of the optimum wavelength, using the feature selection method in which the Mahalanobis distance [
Endoscopic resections were performed in 104 cases of gastric cancer at Yamaguchi University Medical School Hospital between April 2010 and August 2012 [
Relationship between wavelength and reflectance.
Because the tissue type, shape, and color of gastric tumors vary, reflectance is not always uniform even in tumor sites. Thus, 10 points were obtained from the tumor regions. The 10 points were chosen so that they were uniformly dispersed throughout the tumor as much as possible. In the same manner, 11 points obtained from the normal sites were also chosen. One of the 11 points was used for reflectance correction, and, using the remaining points, the mean of normal sites was estimated. For details of the photographs, please see [
In pattern recognition, the samples used for system design are called training samples, and those for system evaluation are called test samples. Training samples and test samples must be different [
Figure
Examples of reflectance with individual differences.
First, the optimum wavelength is found. For wavelength selection, the criterion of wavelength
Because there are individual differences in reflectance, using reflectance values from the normal sites in each individual, the reflectance value of the normal site is corrected by (
A wavelength that maximizes the value of
Second, we explain the design process of a classifier to discriminate between pixels at the normal site and the tumor site within the images. In this paper, the minimum-distance classifier, which is the simplest classifier, is modified and used. The minimum-distance classifier assigns a pattern
There is generally a trade-off relationship between sensitivity and specificity; that is, the higher the sensitivity, the lower the specificity and vice versa. Because the system is used for gastric cancer screening, a cutoff value that yields the maximum sensitivity to avoid missing cancer is expected while maintaining high specificity. A cutoff value is defined by multiplying parameters
Modified minimum-distance classifier.
In this study, to optimize the cutoff value parameter
Training samples are randomly divided into subtraining samples and subtest samples.
Using subtraining samples,
Cutoff values are obtained using
Using each cutoff value, subtest samples are discriminated.
Regarding discrimination ability for the subtest samples, parameter
The best method to evaluate the system is to assess the percentages of the sensitivity and specificity after the test samples are discriminated practically. In this paper, 104 cases were available as samples. The 104 samples were randomly divided into 54 training samples and 50 test samples, and, using the 54 training samples alone, a system was designed through which 50 test samples were discriminated. The trials described above were repeated independently 30 times, and the discrimination ability of the system was evaluated. The flowchart of the evaluation is shown in Figure
Flowchart of system design and assessment.
In the test samples, at first, the mean value is calculated from 11 reflectance values of normal sites in the training samples, and the reflectance value that is closest to the mean value of the 11 reflectance values is selected from them as a typically normal reflectance value. The mean value of the remaining 10 reflectance values is calculated again and is defined as the mean reflectance value of normal sites. With respect to tumor sites, similar to the normal sites of the training samples, the mean value of 10 reflectance values of tumor sites is defined as the mean reflectance value of tumor sites. The corrected reflectance values of the normal and tumor sites are obtained by subtracting the typically normal reflectance values from the respective mean reflectance values.
Figure
First rank | Second rank | Third rank | |
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Optimum wavelength |
770 nm | 675 nm | 680 nm |
Optimum parameter |
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Examples of corrected reflectance values for training samples.
The results of the discrimination with the use of 770 nm and 1/4 are shown in Table
Discrimination ability of the system using 770 nm and 1/4 for test samples.
Rate of discrimination (%) | Sensitivity (%) | Specificity (%) | Youden index |
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85.5 | 72.2 | 98.8 | 0.710 |
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The numbers in the upper row indicate the mean values, and the numbers in the lower row indicate the 95% confidence interval.
The point of this study can be found in the correction of individual differences of reflectance. Therefore, to clarify the effects of this correction, we conducted an experiment comparing techniques that perform correction with techniques that do not perform correction. In both techniques, feature selection was performed using the Mahalanobis distance, and the modified minimum-distance classifier was used. As shown in Table
Discrimination ability with and without correction.
Optimum wavelength | Optimum parameter | Rate of discrimination (%) | Sensitivity (%) | Specificity (%) | Youden index | |
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With correction | 770 nm | 1/4 | 85.5 | 72.2 | 98.8 | 0.710 |
Without correction | 780 nm | 1/5 | 67.0 | 73.8 | 60.2 | 0.340 |
We used only one wavelength for discrimination in our original study. Therefore, we conducted an experiment to determine whether discrimination performance could be improved by increasing the number of wavelengths. Specifically, the discrimination experiment was conducted on two-dimensional feature space by combining the optimum wavelength of 770 nm and the second-rank wavelength of 675 nm. The results are shown in Table
Effect of an increase in the number of features.
Wavelength | Optimum parameter | Rate of discrimination (%) | Sensitivity (%) | Specificity (%) | Youden index |
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770 nm | 1/4 | 85.5 | 72.2 | 98.8 | 0.710 |
770 nm, 675 nm | 1/4 | 86.4 | 74.9 | 97.9 | 0.729 |
Real-time processing was required in this study. Not only does increasing the number of features lengthen the processing time, but also the hardware scale increases. Since no clear improvement in discrimination performance is obtained by increasing the number of features, by emphasizing the real-time processing, we have adopted a one-dimensional system by using one wavelength. For practical use, there may be a problem with the photographic speed of the HSC, which photographs at many wavelengths. However, since the system uses a single optimum wavelength, the photographic speed of the HSC does not matter, and thus real-time processing is achievable.
Finally, when this system is applied in practical use, correction will be a problem. Correction requires normal samples, but such samples are not actually available. Therefore, in this paper, we hypothesize that when the camera photographs the inside of the stomach, almost all of the pixels within the image will be normal pixels. If the image contains many pixels of cancer, a doctor can easily detect cancer without the support of the system. In general, this hypothesis is considered to be formed for the images used for gastric cancer screening. If this hypothesis is satisfied, one pixel is randomly selected within the images and can be used as a normal pixel for correction.
This research depends on the data that is acquired. This means that the individual wavelength and cutoff value should be optimized depending on the hyperspectral camera that is used. Therefore, the values of 770 nm and 1/4 might not be valid when another hyperspectral camera is used. However, as revealed in this study, to resolve the problem of individual differences in patients, the value of this study is in establishing an approach whereby real-time processing is possible.
In this paper, we developed a diagnostic support system for gastric cancer that could discern between a pixel of a normal site and a pixel of a tumor site for each pixel in the images of 104 gastric cancer cases photographed by a HSC. Based on the results of 30 independent trials with the optimal wavelength 770 nm and cutoff value of 1/4, it was shown that this system is effective in screening for gastric cancer, achieving an average sensitivity of 72.2% and average specificity of 98.8%.
From the standpoint of this study, whether a lesion is gastric cancer is ultimately determined by the physician, and the system supports the physician to avoid missing gastric cancer. For this purpose, the system can discriminate on a pixel-by-pixel basis and support a physician’s interpretation with a color display of the regions consisting of pixels discriminated as a tumor in the images.
The data used here are from images of tissues photographed by the HSC immediately after gastric cancer resection. In the future, we aim to use the system in the clinical setting, and we are planning to perform experiments using images photographed from within the stomach.
The authors declare that there is no conflict of interests regarding the publication of this paper.