Abstract

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the “spectral + spatial” features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (2)

M. F. Byrne, N. Chapados, F. Soudan, C. Oertel, M. L. Pérez, R. Kelly, N. Iqbal, F. Chandelier, and D. K. Rex, “Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model,” Gut 68(1), 94–100 (2019).
[Crossref]

K. Wang, X. Lu, H. Zhou, Y. Gao, J. Zheng, M. Tong, C. Wu, C. Liu, L. Huang, T. Jiang, F. Meng, Y. Lu, H. Ai, X. Y. Xie, L. Yin, P. Liang, J. Tian, and R. Zheng, “Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study,” Gut 68(4), 729–741 (2019).
[Crossref]

2018 (4)

H. Xue, A. Yang, F. Liu, X. Sun, and X. Liu, “Clinical significance of Serum Pepsinogen I/II and gastrin-17 determination in gastric cancer diagnosis and prognosis,” Eur. J. Inflammation 16, 1–4 (2018).
[Crossref]

F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” Ca-Cancer J. Clin. 68(6), 394–424 (2018).
[Crossref]

H. Katai, T. Ishikawa, K. Akazawa, Y. Isobe, I. Miyashiro, I. Oda, S. Tsujitani, H. Ono, S. Tanabe, T. Fukagawa, S. Nunobe, Y. Kakeji, and A. Nashimoto, “Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association(2001-2007),” Gastric Cancer 21(1), 144–154 (2018).
[Crossref]

R. S. van der Post, J. V. Dieren, A. Grelack, N. Hoogerbrugge, L. E. van der Kolk, P. Snaebjornsson, I. L. Vogelaar, J. H. van Krieken, T. M. Bisseling, and A. Cats, “Outcomes of screening gastroscopy in first-degree relatives of patients fulfilling hereditary diffuse gastric cancer criteria,” Gastrointest. Endosc. 87(2), 397–404.e2 (2018).
[Crossref]

2017 (3)

R. P. Merkow, G. Herrera, D. A. Goldman, H. G. Mark, A. S. Arnold, J. M. Vivian, E. S. Murray, F. B. Daniel, and G. Coit, “Endoscopic Ultrasound as a Pretreatment Clinical Staging Tool for Gastric Cancer: Association with Pathology and Outcome,” Ann. Surg. Oncol. 24(12), 3658–3666 (2017).
[Crossref]

L. Ma, G. Lu, D. Wang, X. Wang, Z. G. Chen, S. Muller, A. Chen, and B. Fei, “Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model,” SPIE Medical Imaging 137, 101372G (2017).
[Crossref]

O. Dohi, N. Yagi, A. Majima, Y. Horii, T. Kitaichi, Y. Onozawa, K. Suzuki, A. Tomie, R. Kimura-Tsuchiya, T. Tsuji, N. Yamada, N. Bito, T. Okayama, N. Yoshida, K. Kamada, K. Katada, K. Uchiyama, T. Ishikawa, T. Takagi, O. Handa, H. Konishi, Y. Naito, A. Yanagisawa, and Y. Itoh, “Diagnostic ability of magnifying endoscopy with blue laser imaging for early gastric cancer: a prospective study,” Gastric Cancer 20(2), 297–303 (2017).
[Crossref]

2016 (3)

R. Hüneburg, T. Marwitz, P. van Heteren, T. J. Weismüller, J. Trebicka, R. Adam, S. Aretz, A. P. Bouza, D. Pantelis, J. C. Kalff, J. Nattermann, and C. P. Strassburg, “Chromoendoscopy in combination with random biopsies does not improve detection of gastric cancer foci in CDH1 mutation positive patients,” Gastrointest. Endosc. 4(12), E1305–E1310 (2016).
[Crossref]

K. D. Miller, R. L. Siegel, C. C. Lin, A. B. Mariotto, J. L. Kramer, J. H. Rowland, K. D. Stein, R. Alteri, and A. Jemal, “Cancer Treatment and Survivorship Statistics, 2016,” Ca-Cancer J. Clin. 66(4), 271–289 (2016).
[Crossref]

A. W. Chan, P. Mercier, D. Schiller, R. Bailey, S. Robbins, D. T. Eurich, M. B. Sawyer, and D. Broadhurst, “1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer,” Br. J. Cancer 114(1), 59–62 (2016).
[Crossref]

2015 (9)

T. Gotoda, H. Ishikawa, H. Ohnishi, K. Sugano, C. Kusano, C. Yokoi, Y. Matsuyama, and F. Moriyasu, “Randomized controlled trial comparing gastric cancer screening by gastrointestinal X-ray with serology for Helicobacter pylori and pepsinogens followed by Gastrointest. Endosc.,” Gastric Cancer 18(3), 605–611 (2015).
[Crossref]

T. Saito, Y. Kurokawa, S. Takiguchi, Y. Miyazaki, T. Takahashi, M. Yamasaki, H. Miyata, K. Nakajima, M. Mori, and Y. Doki, “Accuracy of multidetector-row CT in diagnosing lymph node metastasis in patients with gastric cancer,” Eur. Radiol. 25(2), 368–374 (2015).
[Crossref]

Y. H. Park and N. Kim, “Review of atrophic gastritis and intestinal metaplasia as a premalignant lesion of gastric cancer,” J. Cancer Prev. 20(1), 25–40 (2015).
[Crossref]

Q. Li, Y. Shao, X. Zhang, T. Zheng, M. Miao, L. Qin, B. Wang, G. Ye, B. Xiao, and J. Guo, “Plasma long noncoding RNA protected by exosomes as a potential stable biomarker for gastric cancer,” Tumor Biol. 36(3), 2007–2012 (2015).
[Crossref]

H. Doyama, N. Yoshida, S. Tsuyama, R. Ota, Y. Takeda, H. Nakanishi, K. Tsuji, K. Tominaga, S. Tsuji, K. Takemura, S. Yamada, K. Katayanagi, H. Kurumaya, A. Iwashita, and K. Yao, “The “white globe appearance” (WGA): a novel marker for a correct diagnosis of early gastric cancer by magnifying endoscopy with narrow-band imaging (M-NBI),” Gastrointest. Endosc. 3(2), E120–E124 (2015).
[Crossref]

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

P. Li, S. Chen, H. Chen, X. Mo, T. Li, Y. Shao, B. Xiao, and J. Guo, “Using circular RNA as a novel type of biomarker in the screening of gastric cancer,” Clin. Chim. Acta 444, 132–136 (2015).
[Crossref]

X. Zhou, C. Yin, Y. Dang, F. Ye, and G. Zhang, “Identification of the long non-coding RNA H19 in plasma as a novel biomarker for diagnosis of gastric cancer,” Sci. Rep. 5(1), 11516 (2015).
[Crossref]

X. Zhou, C. Yin, Y. Dang, F. Ye, and G. Zhang, “Identification of the long non-coding RNA H19 in plasma as a novel biomarker for diagnosis of gastric cancer,” Sci. Rep. 5(1), 11516 (2015).
[Crossref]

2014 (7)

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 010901 (2014).
[Crossref]

L. Yang, S. Yang, and R. Zhang, “Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine,” IEEE Geosci. Remote Sensing Lett. 11(3), 651–655 (2014).
[Crossref]

M. J. Duffy, R. Lamerz, C. Haglund, A. Nicolini, M. Kalousová, L. Holubec, and C. Sturgeon, “Tumor markers in colorectal cancer, gastric cancer and gastrointestinal stromal cancers: European group on tumor markers 2014 guidelines update,” Int. J. Cancer 134(11), 2513–2522 (2014).
[Crossref]

G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, “Advances in Hyperspectral Image Classification,” IEEE Signal Process. Mag. 31(1), 45–54 (2014).
[Crossref]

A. C. Croce and G. Bottiroli, “Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis,” Eur. J. Histochem. 58(4), 2461 (2014).
[Crossref]

Y. Shao, M. Ye, X. Jiang, W. Sun, X. Ding, Z. Liu, G. Ye, X. Zhang, B. Xiao, and J. Guo, “Gastric juice long noncoding RNA used as a tumor marker for screening gastric cancer,” Cancer 120(21), 3320–3328 (2014).
[Crossref]

M. J. Duffy, R. Lamerz, C. Haglund, A. Nicolini, M. Kalousová, L. Holubec, and C. Sturgeon, “Tumor markers in colorectal cancer, gastric cancer and gastrointestinal stromal cancers: European group on tumor markers 2014 guidelines update,” Int. J. Cancer 134(11), 2513–2522 (2014).
[Crossref]

2013 (3)

J. Li, J. M. Bioucas-Dias, and A. Plaza, “Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning,” IEEE Trans. Geosci. Remote Sensing 51(2), 844–856 (2013).
[Crossref]

N. Bedard, R. A. Schwarz, A. Hu, V. Bhattar, J. Howe, M. D. Williams, A. M. Gillenwater, R. R. Kortum, and T. S. Tkaczyk, “Multimodal snapshot spectral imaging for oral cancer diagnostics: a pilot study,” Biomed. Opt. Express 4(6), 938–949 (2013).
[Crossref]

M. S. Bergholt, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, J. B. Yan So, A. Shabbir, and Z. Huang, “Fiber-optic Raman spectroscopy probes gastric carcinogenesis in vivo at endoscopy,” J. Biophotonics 6(1), 49–59 (2013).
[Crossref]

2011 (1)

J. Zhang, S. B. Guo, and Z. J. Duan, “Application of magnifying narrow-band imaging endoscopy for diagnosis of early gastric cancer and precancerous lesion,” BMC Gastroenterol. 11(1), 135 (2011).
[Crossref]

2009 (1)

A. Picon, O. Ghita, P. F. Whelan, and P. M. Iriondo, “Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data,” IEEE Trans. Ind. Inf. 5(4), 483–494 (2009).
[Crossref]

2008 (2)

M. Areia, P. Amaro, M. Dinis-Ribeiro, M. A. Cipriano, C. Marinho, A. Costa-Pereira, C. Lopes, L. Moreira-Dias, J. M. Romozinho, H. Gouveia, D. Freitas, and M. C. Leito, “External validation of a classification for methylene blue magnification chromoendoscopy in premalignant gastric lesions,” Gastrointest. Endosc. 67(7), 1011–1018 (2008).
[Crossref]

P. Correa and M. B. Piazuelo, “Natural history of helicobacter pylori infection,” Dig. Liver Dis. 40(7), 490–496 (2008).
[Crossref]

2007 (2)

J. A. Evans, B. E. Bouma, J. Bressner, M. Shishkov, G. Y. Lauwers, M. Mino-Kenudson, N. S. Nishioka, and G. J. Tearney, “Identifying intestinal metaplasia at the squamocolumnar junction by using optical coherence tomographyp,” Gastrointest. Endosc. 65(1), 50–56 (2007).
[Crossref]

Y. Xu, Z. Xu, and X. Ling, “Clinical Study on Gastric Carcinoma and Gastritis Using mid-FT-IR Spectroscopy,” Acta Sci. Nat. Univ. Pekin. 43(4), 441–445 (2007).

2006 (1)

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2005 (1)

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2003 (1)

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A. W. Chan, P. Mercier, D. Schiller, R. Bailey, S. Robbins, D. T. Eurich, M. B. Sawyer, and D. Broadhurst, “1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer,” Br. J. Cancer 114(1), 59–62 (2016).
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M. Plummer, E. Buiatti, G. Lopez, S. Peraza, J. Vivas, W. Oliver, and N. Munoz, “Histological diagnosis of precancerous lesions of the stomach: a reliability study,” Int. J. Epidemiol. 26(4), 716–720 (1997).
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G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, “Advances in Hyperspectral Image Classification,” IEEE Signal Process. Mag. 31(1), 45–54 (2014).
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A. W. Chan, P. Mercier, D. Schiller, R. Bailey, S. Robbins, D. T. Eurich, M. B. Sawyer, and D. Broadhurst, “1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer,” Br. J. Cancer 114(1), 59–62 (2016).
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L. Ma, G. Lu, D. Wang, X. Wang, Z. G. Chen, S. Muller, A. Chen, and B. Fei, “Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model,” SPIE Medical Imaging 137, 101372G (2017).
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P. Li, S. Chen, H. Chen, X. Mo, T. Li, Y. Shao, B. Xiao, and J. Guo, “Using circular RNA as a novel type of biomarker in the screening of gastric cancer,” Clin. Chim. Acta 444, 132–136 (2015).
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P. Li, S. Chen, H. Chen, X. Mo, T. Li, Y. Shao, B. Xiao, and J. Guo, “Using circular RNA as a novel type of biomarker in the screening of gastric cancer,” Clin. Chim. Acta 444, 132–136 (2015).
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L. Ma, G. Lu, D. Wang, X. Wang, Z. G. Chen, S. Muller, A. Chen, and B. Fei, “Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model,” SPIE Medical Imaging 137, 101372G (2017).
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R. P. Merkow, G. Herrera, D. A. Goldman, H. G. Mark, A. S. Arnold, J. M. Vivian, E. S. Murray, F. B. Daniel, and G. Coit, “Endoscopic Ultrasound as a Pretreatment Clinical Staging Tool for Gastric Cancer: Association with Pathology and Outcome,” Ann. Surg. Oncol. 24(12), 3658–3666 (2017).
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A. C. Croce and G. Bottiroli, “Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis,” Eur. J. Histochem. 58(4), 2461 (2014).
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A. Conneau, H. Schwenk, L. Barrault, and Y. L. Cun. “Very deep convolutional networks for natural language processing,” arXiv preprint arXiv:1606.01781(2016).

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X. Zhou, C. Yin, Y. Dang, F. Ye, and G. Zhang, “Identification of the long non-coding RNA H19 in plasma as a novel biomarker for diagnosis of gastric cancer,” Sci. Rep. 5(1), 11516 (2015).
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Figures (10)

Fig. 1.
Fig. 1. Fluorescence hyperspectral imaging system.
Fig. 2.
Fig. 2. Schematic diagram of residual network
Fig. 3.
Fig. 3. Schematic diagram of spectral acquisition: (A) original fluorescence image; (B) binarized image; and (C) average spectrum by normalization of each tissue.
Fig. 4.
Fig. 4. Average spectrum of normal, atrophic gastritis, intestinal metaplasia, and gastric cancer: (A) average spectrum of each tissue with error bars and (B) average spectral intensity of each tissue at 496 nm, 546 nm, and 670 nm.
Fig. 5.
Fig. 5. Second-derivative spectra of nonprecancerous lesions, precancerous lesions, and gastric cancer: (A) average spectrum of second derivative and (B) average spectrum of second-derivative with error bars.
Fig. 6.
Fig. 6. Schematic diagram of spatiospectral preprocessing.
Fig. 7.
Fig. 7. Image of nonprecancerous lesions, precancerous lesions, and gastric cancer: Figs.7(A)–(C) histopathological image; Figs.7(D)–(F) original fluorescent image; and Figs.7(G)–(I) spatiospectral image.
Fig. 8.
Fig. 8. Schematic diagram of Resnet34 for early diagnosis of gastric cancer: (A) image dataset by using spatiospectral preprocessing; (B) convolutional layer and residual module; and (C) fully connected layer and softmax multiclassifier.
Fig. 9.
Fig. 9. Modeling results of Resnet34: (A) accuracy rate varies with number of iterations and (B) curve of loss function varies with number of iterations.
Fig. 10.
Fig. 10. Model hyperparameter optimization results.

Tables (5)

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Table 1. Autofluorescence groups, excitation wavelengths, and emission wavelengths of human tissue components.

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Table 2. Case and picture information of nonprecancerous lesion group, precancerous lesion group, and gastric cancer group.

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Table 3. Sample set division of spatiospectral image.

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Table 4. Results of Resnet34 model for test set.

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Table 5. Comparison of early diagnosis effect (accuracy, specificity, and sensitivity) of gastric cancer based on different methods.

Equations (6)

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h l + 1 = R e l u ( h l + F ( h l , w l ) )
h l + 1 = R e l u ( φ ( h l ) + F ( h l , w l ) )
A c c u r a c y = n c o r r e c t n t o t a l
L o s s = N L L ( W , b ) = i = 1 | Y | l o g Y ( i )
TPR = T P T P + F N
F P R = T N F P + T N