Abstract

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650 nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome.

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

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References

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    [Crossref]
  26. T.-F. Wu, C.-J. Lin, and R. C. Weng, “Probability estimates for multi-class classification by pairwise coupling,” J. Mach. Learn. Res. 5, 975–1005 (2004).
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    [Crossref]
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    [Crossref]
  35. R. L. van Veen, A. Amelink, M. Menke-Pluymers, C. van der Pol, and H. J. Sterenborg, “Optical biopsy of breast tissue using differential path-length spectroscopy,” Phys. Med. Biol. 50(11), 2573–2581 (2005).
    [Crossref]
  36. C. Zhu, G. M. Palmer, T. M. Breslin, J. Harter, and N. Ramanujam, “Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: A monte-carlo-model-based approach,” J. Biomed. Opt. 13(3), 034015 (2008).
    [Crossref]
  37. L. L. de Boer, B. H. W. Hendriks, F. van Duijnhoven, M. J. T. F. D. Vrancken Peeters-Baas, K. K. Van de Vijver, C. E. Loo, K. Jóźwiak, H. J. C. M. Sterenborg, and T. J. M. Ruers, “Using DRS during breast conserving surgery: Identifying robust optical parameters and dealing with inter-patient variation,” Biomed. Opt. Express 7(12), 5188–5200 (2016).
    [Crossref]

2019 (1)

E. Kho, L. L. de Boer, K. K. Van de Vijver, F. van Duijnhoven, M.-J. T. F. D. Vrancken Peeters, H. J. C. M. Sterenborg, and T. J. M. Ruers, “Hyperspectral imaging for resection margin assessment during surgery,” Clin. Cancer Res. 25(12), 3572–3580 (2019).
[Crossref]

2018 (1)

B. W. Maloney, D. M. McClatchy, B. W. Pogue, K. D. Paulsen, W. A. Wells, and R. J. Barth, “Review of methods for intraoperative margin detection for breast conserving surgery,” J. Biomed. Opt. 23(10), 1 (2018).
[Crossref]

2017 (8)

S. Boughorbel, F. Jarray, and M. El-Anbari, “Optimal classifier for imbalanced data using matthews correlation coefficient metric,” PLoS One 12(6), e0177678 (2017).
[Crossref]

L. Langhans, M.-B. Jensen, M.-L. M. Talman, I. Vejborg, N. Kroman, and T. F. Tvedskov, “Reoperation rates in ductal carcinoma in situ vs invasive breast cancer after wire-guided breast-conserving surgery,” JAMA Surg. 152(4), 378–384 (2017).
[Crossref]

E. R. St John, R. Al-Khudairi, H. Ashrafian, T. Athanasiou, Z. Takats, D. J. Hadjiminas, A. Darzi, and D. R. Leff, “Diagnostic accuracy of intraoperative techniques for margin assessment in breast cancer surgery: A meta-analysis,” Ann. Surg. 265(2), 300–310 (2017).
[Crossref]

A. Pardo Franco, E. Real Peña, V. Krishnaswamy, J. M. López Higuera, B. W. Pogue, and O. M. Conde Portilla, “Directional kernel density estimation for classification of breast tissue spectra,” IEEE Trans. Med. Imaging 36(1), 64–73 (2017).
[Crossref]

D. Versteegden, L. Keizer, M. Schlooz-Vries, L. Duijm, C. Wauters, and L. Strobbe, “Performance characteristics of specimen radiography for margin assessment for ductal carcinoma in situ: A systematic review,” Breast Cancer Res. Treat. 166(3), 669–679 (2017).
[Crossref]

P. Taroni, A. M. Paganoni, F. Ieva, A. Pifferi, G. Quarto, F. Abbate, E. Cassano, and R. Cubeddu, “Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study,” Sci. Rep. 7(1), 40683 (2017).
[Crossref]

G. Lu, J. V. Little, X. Wang, C. C. Griffith, M. El-Deiry, A. Y. Chen, and B. Fei, “Detection of head and neck cancer in surgical specimens using quantitative hyperspectral imaging,” Clin. Cancer Res. 23(18), 5426–5436 (2017).
[Crossref]

B. Fei, G. Lu, X. Wang, H. Zhang, J. V. Little, M. R. Patel, C. C. Griffith, M. W. El-Diery, and A. Y. Chen, “Label-free reflectance hyperspectral imaging for tumor margin assessment: A pilot study on surgical specimens of cancer patients,” J. Biomed. Opt. 22(8), 086009 (2017).
[Crossref]

2016 (5)

Z. Han, A. Zhang, X. Wang, Z. Sun, M. D. Wang, and T. Xie, “In vivo use of hyperspectral imaging to develop a noncontact endoscopic diagnosis support system for malignant colorectal tumors,” J. Biomed. Opt. 21(1), 016001 (2016).
[Crossref]

L. L. de Boer, B. H. W. Hendriks, F. van Duijnhoven, M. J. T. F. D. Vrancken Peeters-Baas, K. K. Van de Vijver, C. E. Loo, K. Jóźwiak, H. J. C. M. Sterenborg, and T. J. M. Ruers, “Using DRS during breast conserving surgery: Identifying robust optical parameters and dealing with inter-patient variation,” Biomed. Opt. Express 7(12), 5188–5200 (2016).
[Crossref]

J. J. Keating, C. Fisher, R. Batiste, and S. Singhal, “Advances in intraoperative margin assessment for breast cancer,” Curr. Surg. Rep. 4(4), 15 (2016).
[Crossref]

A. L. Merrill, S. B. Coopey, R. Tang, M. P. McEvoy, M. C. Specht, K. S. Hughes, M. A. Gadd, and B. L. Smith, “Implications of new lumpectomy margin guidelines for breast-conserving surgery: Changes in reexcision rates and predicted rates of residual tumor,” Ann. Surg. Oncol. 23(3), 729–734 (2016).
[Crossref]

A. L. Merrill, R. Tang, J. K. Plichta, U. Rai, S. B. Coopey, M. P. McEvoy, K. S. Hughes, M. C. Specht, M. A. Gadd, and B. L. Smith, “Should new “no ink on tumor” lumpectomy margin guidelines be applied to ductal carcinoma in situ (DCIS)? A retrospective review using shaved cavity margins,” Ann. Surg. Oncol. 23(11), 3453–3458 (2016).
[Crossref]

2015 (3)

E. L. Vos, A. Jager, C. Verhoef, A. C. Voogd, and L. B. Koppert, “Overall survival in patients with a re-excision following breast conserving surgery compared to those without in a large population-based cohort,” Eur. J. Cancer 51(3), 282–291 (2015).
[Crossref]

S. Alrahbi, P. M. Chan, B. C. Ho, M. D. Seah, J. J. Chen, and E. Y. Tan, “Extent of margin involvement, lymphovascular invasion, and extensive intraductal component predict for residual disease after wide local excision for breast cancer,” Clin. Breast Cancer 15(3), 219–226 (2015).
[Crossref]

L. L. de Boer, B. Molenkamp, T. M. Bydlon, B. H. W. Hendriks, J. Wesseling, H. J. C. M. Sterenborg, and T. J. M. Ruers, “Fat/Water ratios measured with diffuse reflectance spectroscopy to detect breast tumor boundaries,” Breast Cancer Res. Treat. 152(3), 509–518 (2015).
[Crossref]

2014 (3)

M. Denstedt, A. Bjorgan, M. Milanič, and L. L. Randeberg, “Wavelet based feature extraction and visualization in hyperspectral tissue characterization,” Biomed. Opt. Express 5(12), 4260–4280 (2014).
[Crossref]

K. Butler-Henderson, A. H. Lee, R. I. Price, and K. Waring, “Intraoperative assessment of margins in breast conserving therapy: A systematic review,” The Breast 23(2), 112–119 (2014).
[Crossref]

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

2013 (2)

N. Neittaanmäki-Perttu, M. Grönroos, T. Tani, I. Pölönen, A. Ranki, O. Saksela, and E. Snellman, “Detecting field cancerization using a hyperspectral imaging system,” Lasers Surg. Med. 45(7), 410–417 (2013).
[Crossref]

D. Evers, R. Nachabe, M.-J. Vranken Peeters, J. van der Hage, H. Oldenburg, E. Rutgers, G. Lucassen, B. W. Hendriks, J. Wesseling, and T. M. Ruers, “Diffuse reflectance spectroscopy: Towards clinical application in breast cancer,” Breast Cancer Res. Treat. 137(1), 155–165 (2013).
[Crossref]

2012 (2)

M. Vidal and J. M. Amigo, “Pre-processing of hyperspectral images. Essential steps before image analysis,” Chemom. Intell. Lab. Syst. 117, 138–148 (2012).
[Crossref]

K. Esbona, Z. Li, and L. G. Wilke, “Intraoperative imprint cytology and frozen section pathology for margin assessment in breast conservation surgery: A systematic review,” Ann. Surg. Oncol. 19(10), 3236–3245 (2012).
[Crossref]

2011 (1)

R. Nachabé, D. J. Evers, B. H. Hendriks, G. W. Lucassen, M. van der Voort, E. J. Rutgers, M.-J. V. Peeters, J. A. Van der Hage, H. S. Oldenburg, and J. Wesseling, “Diagnosis of breast cancer using diffuse optical spectroscopy from 500 to 1600 nm: comparison of classification methods,” J. Biomed. Opt. 16(8), 087010 (2011).
[Crossref]

2009 (1)

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: Current modalities and future directions,” Ann. Surg. Oncol. 16(10), 2717–2730 (2009).
[Crossref]

2008 (1)

C. Zhu, G. M. Palmer, T. M. Breslin, J. Harter, and N. Ramanujam, “Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: A monte-carlo-model-based approach,” J. Biomed. Opt. 13(3), 034015 (2008).
[Crossref]

2007 (1)

C. S. Kaufman, L. Jacobson, B. A. Bachman, L. B. Kaufman, C. Mahon, L.-J. Gambrell, R. Seymour, J. Briscoe, K. Aulisio, and A. Cunningham, “Intraoperative digital specimen mammography: Rapid, accurate results expedite surgery,” Ann. Surg. Oncol. 14(4), 1478–1485 (2007).
[Crossref]

2005 (1)

R. L. van Veen, A. Amelink, M. Menke-Pluymers, C. van der Pol, and H. J. Sterenborg, “Optical biopsy of breast tissue using differential path-length spectroscopy,” Phys. Med. Biol. 50(11), 2573–2581 (2005).
[Crossref]

2004 (1)

T.-F. Wu, C.-J. Lin, and R. C. Weng, “Probability estimates for multi-class classification by pairwise coupling,” J. Mach. Learn. Res. 5, 975–1005 (2004).

1998 (1)

T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Comput. 10(7), 1895–1923 (1998).
[Crossref]

1989 (1)

1975 (1)

B. W. Matthews, “Comparison of the predicted and observed secondary structure of T4 phage lysozyme,” Biochim. Biophys. Acta, Protein Struct. 405(2), 442–451 (1975).
[Crossref]

1936 (1)

R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugen. 7(2), 179–188 (1936).
[Crossref]

Abbate, F.

P. Taroni, A. M. Paganoni, F. Ieva, A. Pifferi, G. Quarto, F. Abbate, E. Cassano, and R. Cubeddu, “Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study,” Sci. Rep. 7(1), 40683 (2017).
[Crossref]

Al-Khudairi, R.

E. R. St John, R. Al-Khudairi, H. Ashrafian, T. Athanasiou, Z. Takats, D. J. Hadjiminas, A. Darzi, and D. R. Leff, “Diagnostic accuracy of intraoperative techniques for margin assessment in breast cancer surgery: A meta-analysis,” Ann. Surg. 265(2), 300–310 (2017).
[Crossref]

Alrahbi, S.

S. Alrahbi, P. M. Chan, B. C. Ho, M. D. Seah, J. J. Chen, and E. Y. Tan, “Extent of margin involvement, lymphovascular invasion, and extensive intraductal component predict for residual disease after wide local excision for breast cancer,” Clin. Breast Cancer 15(3), 219–226 (2015).
[Crossref]

Amelink, A.

R. L. van Veen, A. Amelink, M. Menke-Pluymers, C. van der Pol, and H. J. Sterenborg, “Optical biopsy of breast tissue using differential path-length spectroscopy,” Phys. Med. Biol. 50(11), 2573–2581 (2005).
[Crossref]

Amigo, J. M.

M. Vidal and J. M. Amigo, “Pre-processing of hyperspectral images. Essential steps before image analysis,” Chemom. Intell. Lab. Syst. 117, 138–148 (2012).
[Crossref]

Ashrafian, H.

E. R. St John, R. Al-Khudairi, H. Ashrafian, T. Athanasiou, Z. Takats, D. J. Hadjiminas, A. Darzi, and D. R. Leff, “Diagnostic accuracy of intraoperative techniques for margin assessment in breast cancer surgery: A meta-analysis,” Ann. Surg. 265(2), 300–310 (2017).
[Crossref]

Athanasiou, T.

E. R. St John, R. Al-Khudairi, H. Ashrafian, T. Athanasiou, Z. Takats, D. J. Hadjiminas, A. Darzi, and D. R. Leff, “Diagnostic accuracy of intraoperative techniques for margin assessment in breast cancer surgery: A meta-analysis,” Ann. Surg. 265(2), 300–310 (2017).
[Crossref]

Aulisio, K.

C. S. Kaufman, L. Jacobson, B. A. Bachman, L. B. Kaufman, C. Mahon, L.-J. Gambrell, R. Seymour, J. Briscoe, K. Aulisio, and A. Cunningham, “Intraoperative digital specimen mammography: Rapid, accurate results expedite surgery,” Ann. Surg. Oncol. 14(4), 1478–1485 (2007).
[Crossref]

Bachman, B. A.

C. S. Kaufman, L. Jacobson, B. A. Bachman, L. B. Kaufman, C. Mahon, L.-J. Gambrell, R. Seymour, J. Briscoe, K. Aulisio, and A. Cunningham, “Intraoperative digital specimen mammography: Rapid, accurate results expedite surgery,” Ann. Surg. Oncol. 14(4), 1478–1485 (2007).
[Crossref]

Barnes, R.

Bart, J.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: Current modalities and future directions,” Ann. Surg. Oncol. 16(10), 2717–2730 (2009).
[Crossref]

Barth, R. J.

B. W. Maloney, D. M. McClatchy, B. W. Pogue, K. D. Paulsen, W. A. Wells, and R. J. Barth, “Review of methods for intraoperative margin detection for breast conserving surgery,” J. Biomed. Opt. 23(10), 1 (2018).
[Crossref]

Batiste, R.

J. J. Keating, C. Fisher, R. Batiste, and S. Singhal, “Advances in intraoperative margin assessment for breast cancer,” Curr. Surg. Rep. 4(4), 15 (2016).
[Crossref]

Bjorgan, A.

Boughorbel, S.

S. Boughorbel, F. Jarray, and M. El-Anbari, “Optimal classifier for imbalanced data using matthews correlation coefficient metric,” PLoS One 12(6), e0177678 (2017).
[Crossref]

Breslin, T. M.

C. Zhu, G. M. Palmer, T. M. Breslin, J. Harter, and N. Ramanujam, “Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: A monte-carlo-model-based approach,” J. Biomed. Opt. 13(3), 034015 (2008).
[Crossref]

Briscoe, J.

C. S. Kaufman, L. Jacobson, B. A. Bachman, L. B. Kaufman, C. Mahon, L.-J. Gambrell, R. Seymour, J. Briscoe, K. Aulisio, and A. Cunningham, “Intraoperative digital specimen mammography: Rapid, accurate results expedite surgery,” Ann. Surg. Oncol. 14(4), 1478–1485 (2007).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI, (Springer, 2015), 234–241.

Butler-Henderson, K.

K. Butler-Henderson, A. H. Lee, R. I. Price, and K. Waring, “Intraoperative assessment of margins in breast conserving therapy: A systematic review,” The Breast 23(2), 112–119 (2014).
[Crossref]

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Figures (9)

Fig. 1.
Fig. 1. Hyperspectral data. a) The tissue was imaged with both the VIS and NIR camera. Thereby, two 3D hypercubes were created that contain both spectral and spatial information of the imaged scene. Therefore, each vector in the 3D HS image contains an entire spectrum over a broad wavelength range, as shown in (b). The red and cyan diffuse reflectance spectra shown in (b) correspond to the red and cyan selected pixels in (a). In this example, the VIS image was resized to match the resolution of the NIR image, as described in Section 2.4.
Fig. 2.
Fig. 2. Data acquisition of breast tissue slices. The breast specimen before (a) and after (b) inking with histopathologic ink. The specimen was sliced (c, d), and one slice was selected and placed in a macrocassette for optical measurements. The tissue slice was imaged with both HS imaging systems (e). To allow for a reproducible location for each measurement and an accurate registration between both cameras, the macrocassette with the tissue was fixed with a casing on a frame that fitted the translational stage of both systems.
Fig. 3.
Fig. 3. Selection of regions that contain a single tissue class. From the original H&E image (a), first adipose tissue (d) was selected by thresholding all RGB channels so that the remaining tissue (b) contained the malignant tissue types, connective tissue, and healthy glandular ducts. By thresholding the red channel of the H&E image, a differentiation was made between tissue with a high and a low nuclei density. Since the nuclei density in connective tissue is low, this enabled us to select connective tissue (e). On the remaining tissue (c), we selected IC, DCIS and healthy glandular tissue using the annotations of the pathologist (f), and grouped the glandular tissue in the connective class. Finally, for each tissue class, the edges of a tissue class area (1 mm) were removed to remain only with the RIGHT dataset (i) from the ALL dataset (h). In addition, pixels that were contaminated with histopathology ink, as indicated with the arrows in (i) and the white light image (g), were removed from this RIGHT dataset.
Fig. 4.
Fig. 4. Architecture of U-Net for tissue segmentation.
Fig. 5.
Fig. 5. Averaged diffuse reflectance spectra for each tissue type in the test set before (a) and after (b) SNV normalization. The error bars indicate the standard deviation.
Fig. 6.
Fig. 6. Intensity differences between spectra. (a) Hyperspectral images obtained with the VIS and NIR camera. In the top and bottom row, the tissue was illuminated from the left and the right, respectively. The colored squares in the HS images are located at the same position in the tissue and correspond to the diffuse reflectance spectra in (b). Spectra obtained with different cameras (VIS and NIR) did not connect due to differences in the measurement setup of both cameras in combination with the rough surface of the tissue slices. This might cause a spectral variability that can be observed as a baseline shift of the spectra. Even when diffuse reflectance spectra were taken with the same camera, at the same location in a tissue slice, their intensity varied when the tissue was illuminated from a different point of view.
Fig. 7.
Fig. 7. (a) The classification accuracy of all pixels in the ALL dataset with respect to the distance to a tissue transition. Both the VIS and NIR wavelength range were used as input for classification. (b) shows an example of a tissue slice with an IC-adipose tissue transition. The circles in the images (b) are taken in the middle of IC (location 1), in the middle of adipose tissue (location 3) and at the IC-adipose tissue transition (location 2). The three diffuse reflectance spectra (c) correspond to these circles. The colors in (b) represent IC (red), connective tissue (dark blue) and adipose tissue (cyan).
Fig. 8.
Fig. 8. For both classification algorithms, LDA and U-Net, the percentage of spectra in the ALL dataset classified as a tissue class within the malignant (IC + DCIS) and healthy (connective + adipose) tissue class. The entire bar corresponds to the percentage correctly classified as malignant or healthy tissue. The color in the bars corresponds to the tissue type as which the pixel was correctly classified with the classification algorithms: red = IC, magenta = DCIS, dark blue = connective, cyan = adipose.
Fig. 9.
Fig. 9. The difference between pixel-based classification without (b, e, h) and with (c, f, i) adding contextual context in two tissue slices from the test set. a, d, g) shows the histopathology annotations. When adding contextual context, better differentiation between different tissue classes within the malignant (top row) and healthy classes (bottom two rows) can be made. The orange arrows point at smaller branches of connective tissue that were detected with U-Net (f) but classified as adipose tissue with LDA (e).

Tables (6)

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Table 1. Data Description

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Table 2. Overview of Usage of RIGHT and ALL Dataset

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Table 3. Evaluation of Optimal Wavelength Range: Recall for each Tissue Type, Tumor and Healthy

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Table 4. Performance Metrics averaged over Patients for the Discrimination of Tumor from Healthy Tissue (mean ± std.)

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Table 5. Spectral versus Spectral-Spatial Classification Results: Recall for each Tissue Type, Tumor and Healthy

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Table 6. Comparison of Currently Available Margin Assessment Techniques and HS Imaging

Equations (3)

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

R ( x , λ ) = 1 R r e f ( λ ) I t i s s u e ( x , λ ) I d a r k ( x , λ ) I w h i t e ( x , λ ) I d a r k ( x , λ ) 100 % ,
L o s s ( y , y ) = α n k ω k y n k log y n k + ( 1 α ) ( 1 k ω k n y n k y n k n y n k + n y n k )
M C C = T P T N F P F N ( T P + F P ) ( T P + F N ) ( T N + F P ) ( T N + F N ) ,