Abstract

We construct a microscopic hyperspectral imaging system to distinguish between normal and cancerous gastric cells. We study common transmission-spectra features that only emerge when the samples are dyed with hematoxylin and eosin (H&E) stain. Subsequently, we classify the obtained visible-range transmission spectra of the samples into three zones. Distinct features are observed in the spectral responses between the normal and cancerous cell nuclei in each zone, which depend on the pH level of the cell nucleus. Cancerous gastric cells are precisely identified according to these features. The average cancer-cell identification accuracy obtained with a backpropagation algorithm program trained with these features is 95%.

© 2015 Optical Society of America

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References

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    [Crossref] [PubMed]
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2014 (6)

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

W. S. Yi, J. Zhang, H. M. Jiang, and N. Y. Zhang, “Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics,” Proc. SPIE 9230, 92301V (2014).

K. Christian, M. Johanna, A. Werner, B. Kathrin, G. M. Tesfay, H. Robert, A. Abbas, W. Stefan, B. Andreas, N. F. Wilhelm, and S. Florian, “Raman difference spectroscopy: a non-invasive method for identification of oral squamous cell carcinoma,” Biomed. Opt. Express 5(9), 3252–3265 (2014).
[PubMed]

2013 (4)

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

2008 (1)

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

2007 (1)

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

2006 (1)

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

2001 (1)

T. Hastie and M. Zhu, “Dimension reduction and visualization in discriminant analysis - discussion,” Aust. Nz. J. Stat. 43, 179–185 (2001).

1984 (1)

J. L. Wike-Hooley, J. Haveman, and H. S. Reinhold, “The Relevance of Tumour pH to the Treatment of Malignant Disease,” Radiother. Oncol. 2(4), 343–366 (1984).
[Crossref] [PubMed]

1924 (1)

O. Warburg, “Improved method of measurement of respiration and glycolosis,” Biochem. Z. 152, 51–63 (1924).

Abbas, A.

Acar, U.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Amott, D.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Andreas, B.

Bayram, B.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Beach, J.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Bergeron, E.

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

Bigler, S.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Cavdaroglu, G. C.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Celik, L.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Chen, C.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Chen, Z. G.

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

Choyke, P. L.

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Christian, K.

Coifman, R. R.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

Coppi, A. C.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

Cubuk, R.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Davis, G. L.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

DeVerse, R. A.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

Donald, P. J.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Faruque, F.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Farwell, D. G.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Fei, B. W.

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

Florian, S.

Fujita, Y.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Gandour-Edwards, R.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Geshwind, F. B.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

Goto, A.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Guler, I.

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

Halig, L. M.

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

Hama, Y.

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Hamabe, K.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Hamamoto, Y.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Hastie, T.

T. Hastie and M. Zhu, “Dimension reduction and visualization in discriminant analysis - discussion,” Aust. Nz. J. Stat. 43, 179–185 (2001).

Haveman, J.

J. L. Wike-Hooley, J. Haveman, and H. S. Reinhold, “The Relevance of Tumour pH to the Treatment of Malignant Disease,” Radiother. Oncol. 2(4), 343–366 (1984).
[Crossref] [PubMed]

Hu, M. B.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Hughson, M.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Jankevicius, F.

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

Jiang, H. M.

W. S. Yi, J. Zhang, H. M. Jiang, and N. Y. Zhang, “Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics,” Proc. SPIE 9230, 92301V (2014).

Johanna, M.

Johnson, W.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Karakis, R.

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

Kathrin, B.

Kilic, Y. A.

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

Kiyotoki, S.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Kobayashi, H.

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Koca, H. K.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Koyama, Y.

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Kuru, Y.

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

Lai, K.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Li, H.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Li, Y.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Liu, J.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Loja, M. N.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Lu, G. L.

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

Luo, Z.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Luu, Q. C.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Maggioni, M.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

Meunier, M.

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

Narin, B.

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Nguyen, D. M.

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Nishikawa, J.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Nitin, N.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Okamoto, T.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Patskovsky, S.

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

Pucetaite, M.

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

Qin, X. L.

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

Qu, A. P.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Reinhold, H. S.

J. L. Wike-Hooley, J. Haveman, and H. S. Reinhold, “The Relevance of Tumour pH to the Treatment of Malignant Disease,” Radiother. Oncol. 2(4), 343–366 (1984).
[Crossref] [PubMed]

Rioux, D.

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

Robert, H.

Sablinskas, V.

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

Saito, M.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Sakaida, I.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Satori, S.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Siddiqi, A. M.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Simard, M.

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

Stefan, W.

Steiner, G.

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

Sun, S. R.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Takeuchi, Y.

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Tesfay, G. M.

Tez, M.

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

Truong, A. Q.

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Urano, Y.

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Urboniene, V.

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

Wang, D. S.

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

Wang, L. W.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Warburg, O.

O. Warburg, “Improved method of measurement of respiration and glycolosis,” Biochem. Z. 152, 51–63 (1924).

Warner, F. J.

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

Werner, A.

Wike-Hooley, J. L.

J. L. Wike-Hooley, J. Haveman, and H. S. Reinhold, “The Relevance of Tumour pH to the Treatment of Malignant Disease,” Radiother. Oncol. 2(4), 343–366 (1984).
[Crossref] [PubMed]

Wilhelm, N. F.

Williams, W.

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Yi, W. S.

W. S. Yi, J. Zhang, H. M. Jiang, and N. Y. Zhang, “Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics,” Proc. SPIE 9230, 92301V (2014).

Yuan, J. P.

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Zelvys, A.

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

Zhang, J.

W. S. Yi, J. Zhang, H. M. Jiang, and N. Y. Zhang, “Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics,” Proc. SPIE 9230, 92301V (2014).

Zhang, N. Y.

W. S. Yi, J. Zhang, H. M. Jiang, and N. Y. Zhang, “Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics,” Proc. SPIE 9230, 92301V (2014).

Zhu, M.

T. Hastie and M. Zhu, “Dimension reduction and visualization in discriminant analysis - discussion,” Aust. Nz. J. Stat. 43, 179–185 (2001).

Analyst (Lond.) (1)

S. Patskovsky, E. Bergeron, D. Rioux, M. Simard, and M. Meunier, “Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells,” Analyst (Lond.) 139(20), 5247–5253 (2014).
[Crossref] [PubMed]

Aust. Nz. J. Stat. (1)

T. Hastie and M. Zhu, “Dimension reduction and visualization in discriminant analysis - discussion,” Aust. Nz. J. Stat. 43, 179–185 (2001).

Biochem. Z. (1)

O. Warburg, “Improved method of measurement of respiration and glycolosis,” Biochem. Z. 152, 51–63 (1924).

Biomed. Opt. Express (1)

Cancer (1)

A. M. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer 114(1), 13–21 (2008).
[Crossref] [PubMed]

Cancer Prev. Res. (Phila.) (1)

Z. Luo, M. N. Loja, D. G. Farwell, Q. C. Luu, P. J. Donald, D. Amott, A. Q. Truong, R. Gandour-Edwards, and N. Nitin, “Widefield optical imaging of changes in uptake of glucose and tissue extracellular pH in head and neck cancer,” Cancer Prev. Res. (Phila.) 7(10), 1035–1044 (2014).
[Crossref] [PubMed]

Clin. Cancer Res. (1)

Y. Koyama, Y. Hama, Y. Urano, D. M. Nguyen, P. L. Choyke, and H. Kobayashi, “Spectral fluorescence molecular imaging of lung metastases targeting HER2/neu,” Clin. Cancer Res. 13(10), 2936–2945 (2007).
[Crossref] [PubMed]

Eng. Appl. Artif. Intell. (1)

R. Karakis, M. Tez, Y. A. Kilic, Y. Kuru, and I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer,” Eng. Appl. Artif. Intell. 26(3), 945–950 (2013).
[Crossref]

J. Biomed. Opt. (3)

G. L. Lu, L. M. Halig, D. S. Wang, X. L. Qin, Z. G. Chen, and B. W. Fei, “Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging,” J. Biomed. Opt. 19(10), 106004 (2014).
[Crossref] [PubMed]

V. Urboniene, M. Pucetaite, F. Jankevicius, A. Zelvys, V. Sablinskas, and G. Steiner, “Identification of kidney tumor tissue by infrared spectroscopy of extracellular matrix,” J. Biomed. Opt. 19(8), 087005 (2014).
[Crossref] [PubMed]

S. Kiyotoki, J. Nishikawa, T. Okamoto, K. Hamabe, M. Saito, A. Goto, Y. Fujita, Y. Hamamoto, Y. Takeuchi, S. Satori, and I. Sakaida, “New method for detection of gastric cancer by hyperspectral imaging: a pilot study,” J. Biomed. Opt. 18(2), 026010 (2013).
[Crossref] [PubMed]

Neural Netw. World (1)

B. Bayram, H. K. Koca, B. Narin, G. C. Cavdaroglu, L. Celik, U. Acar, and R. Cubuk, “An efficient algorithm for automatic tumor detection in contrast enhanced breast MRI by using artificial neural network (Neubrea),” Neural Netw. World 23(5), 483–498 (2013).
[Crossref]

Optical Biopsy VI (1)

M. Maggioni, G. L. Davis, F. J. Warner, F. B. Geshwind, A. C. Coppi, R. A. DeVerse, and R. R. Coifman, “Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections - art. no. 60910I,” Optical Biopsy VI 6091, I910 (2006).

PLoS ONE (1)

L. W. Wang, A. P. Qu, J. P. Yuan, C. Chen, S. R. Sun, M. B. Hu, J. Liu, and Y. Li, “Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value,” PLoS ONE 8(12), e82314 (2013).
[Crossref] [PubMed]

Proc. SPIE (1)

W. S. Yi, J. Zhang, H. M. Jiang, and N. Y. Zhang, “Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics,” Proc. SPIE 9230, 92301V (2014).

Radiother. Oncol. (1)

J. L. Wike-Hooley, J. Haveman, and H. S. Reinhold, “The Relevance of Tumour pH to the Treatment of Malignant Disease,” Radiother. Oncol. 2(4), 343–366 (1984).
[Crossref] [PubMed]

Other (3)

D. J. Lv, and X. Deng, “Application of Artificial Neural Network in Simulating Subjective Evaluation of Tumor Segmentation,” Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment 7966 (2011).

F. Vasefi, B. Kaminska, M. Brackstone, and J. J. L. Carson, “Hyperspectral angular domain imaging for ex-vivo breast tumor detection,” Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues Xi 8587 (2013).

J. Lee, R. Kozikowski, N. Molnar, D. W. Siemann, and B. S. Sorg, “In vivo spectral and fluorescence imaging microscopy of tumor microvessel blood supply and oxygenation changes following vascular targeting agent treatment,” Dynamics and Fluctuations in Biomedical Photonics Ix 8222 (2012).

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Figures (7)

Fig. 1
Fig. 1 (A) Architecture of hyperspectral microscopic imaging system and (B) spectral scanning process.
Fig. 2
Fig. 2 The emission spectrum of the white light LED (A) and the transmission and reflectance of the dichroic with un-polarized light (B).
Fig. 3
Fig. 3 Microscopic image of tumor nests in gastric tissues.
Fig. 4
Fig. 4 Transmission spectra of (A) normal cells and (B) cancerous cells in gastric tissues from eight different samples.
Fig. 5
Fig. 5 Microscopic images of cancerous gastric tissues and transmission spectra of normal and cancerous cell nuclei from three different samples. The three different zones in the visible region are identified by different colors.
Fig. 6
Fig. 6 Comparison between two cells of the same type in the same sample. (A) Transmission spectra of two normal cell nuclei and (B) transmission spectra of two cancerous cell nuclei.
Fig. 7
Fig. 7 Identification of cancerous cells that spread to normal tissues. The label A indicates a normal cell, B a cancerous cell that spreads to the normal tissues, and C a cancerous cell in the tumor nest. While cells A and B have similar morphological features, they exhibit different spectral responses.

Tables (2)

Tables Icon

Table 1 Differentiation in spectral differentiation parameters calculated from the entire spectrum between cells in each of eight samples

Tables Icon

Table 2 Accuracy of identification cancerous and normal cells in different samples

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

T= I sample I background
d= k=1 n ( T 1k T 2k ) 2
{ 20 nm S (( λ s2 λ s2 +20 nm T λnormal T λcancerous )( λ s2 20 nm λ s2 T λnormal T λcancerous ))>0.05 T λnormal T λcancerous >0 (from λ s2 to λ s2 +20 nm)
{ T λnormal T λcancerous >0 (from λ s3 to λ s3 20 nm) T λnormal T λcancerous <0 (from λ s3 to λ s3 +90 nm)
L= λ end λ start S
ADT= 1 L | λ start λ end T λ1 T λ2 |
Accuracy= N ture N total ×100%

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