Laparoscopic surgery is a minimally invasive way of cancer resection, which is expected to increase in number. However, because a typical laparoscope can only receive visible light, there is a risk of accidentally damaging nerves that are similar in color to other tissues. To solve this problem, near-infrared (NIR) light (approximately 700-2,500 nm) is considered to be effective because of its feature; component analysis based on the molecular vibrations specific to biomolecules. Previously, we have developed NIR multispectral imaging (MSI) laparoscopy, which acquires NIR spectrum at 14 wavelengths with a band-pass filter. However, since the wavelength is limited, the optimal wavelength for identification cannot be studied. In this study, we developed the world's first laparoscopic device capable of NIR hyperspectral imaging (HSI) with an increased number of wavelengths. Furthermore, NIR-HSI was conducted in a living pig, and the machine-learning was demonstrated to identify nerves and other tissues; accuracy was 0.907.
SignificanceDetermining the extent of gastric cancer (GC) is necessary for evaluating the gastrectomy margin for GC. Additionally, determining the extent of the GC that is not exposed to the mucosal surface remains difficult. However, near-infrared (NIR) can penetrate mucosal tissues highly efficiently.AimWe investigated the ability of near-infrared hyperspectral imaging (NIR-HSI) to identify GC areas, including exposed and unexposed using surgical specimens, and explored the identifiable characteristics of the GC.ApproachOur study examined 10 patients with diagnosed GC who underwent surgery between 2020 and 2021. Specimen images were captured using NIR-HSI. For the specimens, the exposed area was defined as an area wherein the cancer was exposed on the surface, the unexposed area as an area wherein the cancer was present although the surface was covered by normal tissue, and the normal area as an area wherein the cancer was absent. We estimated the GC (including the exposed and unexposed areas) and normal areas using a support vector machine, which is a machine-learning method for classification. The prediction accuracy of the GC region in every area and normal region was evaluated. Additionally, the tumor thicknesses of the GC were pathologically measured, and their differences in identifiable and unidentifiable areas were compared using NIR-HSI.ResultsThe average prediction accuracy of the GC regions combined with both areas was 77.2%; with exposed and unexposed areas was 79.7% and 68.5%, respectively; and with normal regions was 79.7%. Additionally, the areas identified as cancerous had a tumor thickness of >2 mm.ConclusionsNIR-HSI identified the GC regions with high rates. As a feature, the exposed and unexposed areas with tumor thicknesses of >2 mm were identified using NIR-HSI.
Two main features of near-infrared (NIR) light are the ability to perform component analysis based on spectral differences and to have permeability to biological tissues. These features make the technology to acquire NIR spectral of the deep lesion and analyze the components by each pixel, called hyperspectral imaging (HSI). Mounting this technology to a laparoscope enables visualization of invisible or looking-similar tissues in visible light during laparoscopic surgery. In this research, the developed NIR-HSI laparoscopic device acquired NIR spectrum images on in vivo pig specimens. Through the experiments, the difference in spectrum between the artery and surrounding other tissues was confirmed. Additionally, a machine learning procedure provided high accuracy detection of the artery area; accuracy, precision, and recall are 0.868 %, 0.921 %, and 0.637 % respectively.
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