Abstract

As a leading cause of death in women, breast cancer is a global health concern for which personalized therapy remains largely unrealized, resulting in over- or under-treatment. Recently, tumor stroma has been shown to carry important prognostic information, both in its relative abundance and morphology, but its current assessment methods are few and suboptimal. Herein, we present a novel stromal architecture signature (SAS) methodology based on polarized light imaging that quantifies patterns of tumor connective tissue. We demonstrate its ability to differentiate between myxoid and sclerotic stroma, two pathology-derived categories associated with significantly different patient outcomes. The results demonstrate a 97% sensitivity and 88% specificity for myxoid stroma identification in a pilot study of 102 regions of interest from human invasive ductal carcinoma breast cancer surgical specimens (20 patients). Additionally, the SAS numerical score is indicative of the wide range of stromal characteristics within these binary classes and highlights ambiguous mixed-morphology regions prone to misclassification. The enabling polarized light microscopy technique is inexpensive, fast, fully automatable, applicable to fresh or embedded tissue without the need for staining and thus potentially translatable into research and/or clinical settings. The SAS metric yields quantifiable and objective stromal characterization with promise for prognosis in many types of cancers beyond breast carcinoma, enabling researchers and clinicians to further investigate the emerging and important role of stromal architectural patterns in solid tumors.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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  37. P. P. Provenzano, K. W. Eliceiri, J. M. Campbell, D. R. Inman, J. G. White, and P. J. Keely, “Collagen reorganization at the tumor-stromal interface facilitates local invasion,” BMC Med. 4(1), 38 (2006).
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    [Crossref]
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  40. M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
    [Crossref]
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    [Crossref]
  43. 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]

2020 (2)

M. B. Hannouf, G. S. Zaric, P. Blanchette, C. Brezden-Masley, M. Paulden, C. McCabe, and M. Brackstone, “Cost-effectiveness analysis of multigene expression profiling assays to guide adjuvant therapy decisions in women with invasive early-stage breast cancer,” Pharmacogenomics J. 20(1), 27–46 (2020).
[Crossref]

H. Dano, et. al, “Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study,” Mod. Pathol. 33(3), 354–366 (2020).
[Crossref]

2019 (7)

J. Westreich, M. Khorasani, B. Jones, V. Demidov, S. Nofech-Mozes, and A. Vitkin, “Novel methodology to image stromal tissue and assess its morphological features with polarized light: towards a tumour microenvironment prognostic signature,” Biomed. Opt. Express 10(8), 3963–3973 (2019).
[Crossref]

M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
[Crossref]

A. Matikas, T. Foukakis, S. Swain, and J. Bergh, “Avoiding over-and undertreatment in patients with resected node-positive breast cancer with the use of gene expression signatures: are we there yet?” Ann. Oncol. 30(7), 1044–1050 (2019).
[Crossref]

C. J. H. Kramer, K. M. H. Vangangelt, G. W. van Pelt, T. J. A. Dekker, R. Tollenaar, and W. E. Mesker, “The prognostic value of tumour–stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review,” Breast Cancer Res. Treat. 173(1), 55–64 (2019).
[Crossref]

Ł. Rączkowski, M. Możejko, J. Zambonelli, and E. Szczurek, “ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning,” Sci. Rep. 9(1), 14347 (2019).
[Crossref]

Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, and A. E. Sisk, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
[Crossref]

M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
[Crossref]

2018 (5)

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]

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]

M. G. Wallis, “How do we manage overdiagnosis/overtreatment in breast screening?” Clin. Radiol. 73(4), 372–380 (2018).
[Crossref]

C. Jing, Y. Fu, J. Huang, M. Zhang, Y. Yi, W. Gan, and S. Zheng, “Prognostic nomogram based on histological characteristics of fibrotic tumor stroma in patients who underwent curative resection for intrahepatic cholangiocarcinoma,” Oncologist 23(12), 1482–1493 (2018).
[Crossref]

M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
[Crossref]

2017 (4)

Y. Dong, J. Qi, H. He, C. He, S. Liu, J. Wu, D. S. Elson, and H. Ma, “Quantitatively characterizing the microstructural features of breast ductal carcinoma tissues in different progression stages by Mueller matrix microscope,” Biomed. Opt. Express 8(8), 3643–3655 (2017).
[Crossref]

S. Reis, P. Gazinska, J. H. Hipwell, T. Mertzanidou, K. Naidoo, N. Williams, and D. J. Hawkes, “Automated classification of breast cancer stroma maturity from histological images,” IEEE Trans. Biomed. Eng. 64(10), 2344–2352 (2017).
[Crossref]

I. Krop, N. Ismaila, F. Andre, R. C. Bast, W. Barlow, D. E. Collyar, and R. G. Mennel, “Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update,” J. Clin. Oncol. 35(24), 2838–2847 (2017).
[Crossref]

P. Aurello, G. Berardi, D. Giulitti, A. Palumbo, S. M. Tierno, G. Nigri, and G. Ramacciato, “Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients,” The Surg. 15(6), 329–335 (2017).
[Crossref]

2016 (2)

L. M. Wang, M. A. Silva, Z. D’Costa, R. Bockelmann, Z. Soonawalla, S. Liu, and R. Muschel, “The prognostic role of desmoplastic stroma in pancreatic ductal adenocarcinoma,” Oncotarget 7(4), 4183–4194 (2016).
[Crossref]

R. Bhargava and A. Madabhushi, “Emerging themes in image informatics and molecular analysis for digital pathology,” Annu. Rev. Biomed. Eng. 18(1), 387–412 (2016).
[Crossref]

2015 (1)

C. L. Downey, H. H. Thygesen, N. Sharma, and A. M. Shaaban, “Prognostic significance of tumour stroma ratio in inflammatory breast cancer,” SpringerPlus 4(1), 68 (2015).
[Crossref]

2014 (3)

F. J. A. Gujam, J. Edwards, Z. M. A. Mohammed, J. J. Going, and D. C. McMillan, “The relationship between the tumour stroma percentage, clinicopathological characteristics and outcome in patients with operable ductal breast cancer,” Br. J. Cancer 111(1), 157–165 (2014).
[Crossref]

C. L. Downey, S. A. Simpkins, J. White, D. L. Holliday, J. L. Jones, L. B. Jordan, and V. Speirs, “The prognostic significance of tumour-stroma ratio in oestrogen receptor-positive breast cancer,” Br. J. Cancer 110(7), 1744–1747 (2014).
[Crossref]

J. Liu, J. Liu, J. Li, Y. Chen, X. Guan, X. Wu, and X. Wang, “Tumor–stroma ratio is an independent predictor for survival in early cervical carcinoma,” Gynecol. Oncol. 132(1), 81–86 (2014).
[Crossref]

2013 (1)

M. R. Junttila and F. J. de Sauvage, “Influence of tumour micro-environment heterogeneity on therapeutic response,” Nature 501(7467), 346–354 (2013).
[Crossref]

2012 (5)

C. B. Weldon, J. R. Trosman, W. J. Gradishar, A. B. Benson, and J. C. Schink, “Barriers to the use of personalized medicine in breast cancer,” J. Oncol. Pharm. Pract. 8(4), e24–e31 (2012).
[Crossref]

M. W. Conklin and P. J. Keely, “Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers,” Cell Adhes. Migr. 6(3), 249–260 (2012).
[Crossref]

D. Hanahan and L. M. Coussens, “Accessories to the crime: functions of cells recruited to the tumor microenvironment,” Cancer Cell 21(3), 309–322 (2012).
[Crossref]

S. Ahn, J. Cho, J. Sung, J. E. Lee, S. J. Nam, K.-M. Kim, and E. Y. Cho, “The prognostic significance of tumor-associated stroma in invasive breast carcinoma,” Tumor Biol. 33(5), 1573–1580 (2012).
[Crossref]

X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012).
[Crossref]

2011 (3)

M. F. G. Wood, N. Vurgun, M. A. Wallenburg, and I. A. Vitkin, “Effects of formalin fixation on tissue optical polarization properties,” Phys. Med. Biol. 56(8), N115–N122 (2011).
[Crossref]

M. W. Conklin, J. C. Eickhoff, K. M. Riching, C. A. Pehlke, K. W. Eliceiri, P. P. Provenzano, and P. J. Keely, “Aligned collagen is a prognostic signature for survival in human breast carcinoma,” Am. J. Pathol. 178(3), 1221–1232 (2011).
[Crossref]

E. M. de Kruijf, J. G. H. van Nes, C. J. H. van de Velde, H. Putter, V. T. Smit, G. J. Liefers, and W. E. Mesker, “Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients,” Breast Cancer Res. Treat. 125(3), 687–696 (2011).
[Crossref]

2008 (1)

P. P. Provenzano, D. R. Inman, K. W. Eliceiri, J. G. Knittel, L. Yan, C. T. Rueden, and P. J. Keely, “Collagen density promotes mammary tumor initiation and progression,” BMC Med. 6(1), 11 (2008).
[Crossref]

2007 (1)

W. E. Mesker, J. Junggeburt, K. Szuhai, P. de Heer, H. Morreau, H. J. Tanke, and R. A. E. M. Tollenaar, “The carcinoma–stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage,” Anal. Cell. Pathol. 29(5), 387–398 (2007).
[Crossref]

2006 (1)

P. P. Provenzano, K. W. Eliceiri, J. M. Campbell, D. R. Inman, J. G. White, and P. J. Keely, “Collagen reorganization at the tumor-stromal interface facilitates local invasion,” BMC Med. 4(1), 38 (2006).
[Crossref]

2005 (1)

R. Laucirica, “Intraoperative assessment of the breast: guidelines and potential pitfalls,” Arch. Pathol. Lab. Med. 129(12), 1565–1574 (2005).
[Crossref]

2004 (2)

H. Ueno, A. M. Jones, K. H. Wilkinson, J. R. Jass, and I. C. Talbot, “Histological categorisation of fibrotic cancer stroma in advanced rectal cancer,” Gut 53(4), 581–586 (2004).
[Crossref]

M. M. Mueller and N. E. Fusenig, “Friends or foes - Bipolar effects of the tumour stroma in cancer,” Nat. Rev. Cancer 4(11), 839–849 (2004).
[Crossref]

1991 (1)

L. A. Liotta, P. S. Steeg, and W. G. Stetler-Stevenson, “Cancer metastasis and angiogenesis: an imbalance of positive and negative regulation,” Cell 64(2), 327–336 (1991).
[Crossref]

1983 (1)

B. U. Pauli, D. E. Schwartz, E. J. M. Thonar, and K. E. Kuettner, “Tumor invasion and host extracellular matrix,” Cancer Metastasis Rev. 2(2), 129–152 (1983).
[Crossref]

1980 (1)

B. B. Aaron and J. M. Gosline, “Optical properties of single elastin fibres indicate random protein conformation,” Nature 287(5785), 865–867 (1980).
[Crossref]

Aaron, B. B.

B. B. Aaron and J. M. Gosline, “Optical properties of single elastin fibres indicate random protein conformation,” Nature 287(5785), 865–867 (1980).
[Crossref]

Ahn, S.

S. Ahn, J. Cho, J. Sung, J. E. Lee, S. J. Nam, K.-M. Kim, and E. Y. Cho, “The prognostic significance of tumor-associated stroma in invasive breast carcinoma,” Tumor Biol. 33(5), 1573–1580 (2012).
[Crossref]

AlKawaz, A.

M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
[Crossref]

Andre, F.

I. Krop, N. Ismaila, F. Andre, R. C. Bast, W. Barlow, D. E. Collyar, and R. G. Mennel, “Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update,” J. Clin. Oncol. 35(24), 2838–2847 (2017).
[Crossref]

Aneja, R.

M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
[Crossref]

Aurello, P.

P. Aurello, G. Berardi, D. Giulitti, A. Palumbo, S. M. Tierno, G. Nigri, and G. Ramacciato, “Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients,” The Surg. 15(6), 329–335 (2017).
[Crossref]

Baldewijns, M.

M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
[Crossref]

Barlow, W.

I. Krop, N. Ismaila, F. Andre, R. C. Bast, W. Barlow, D. E. Collyar, and R. G. Mennel, “Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update,” J. Clin. Oncol. 35(24), 2838–2847 (2017).
[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]

Bast, R. C.

I. Krop, N. Ismaila, F. Andre, R. C. Bast, W. Barlow, D. E. Collyar, and R. G. Mennel, “Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update,” J. Clin. Oncol. 35(24), 2838–2847 (2017).
[Crossref]

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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).
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A. Matikas, T. Foukakis, S. Swain, and J. Bergh, “Avoiding over-and undertreatment in patients with resected node-positive breast cancer with the use of gene expression signatures: are we there yet?” Ann. Oncol. 30(7), 1044–1050 (2019).
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M. B. Hannouf, G. S. Zaric, P. Blanchette, C. Brezden-Masley, M. Paulden, C. McCabe, and M. Brackstone, “Cost-effectiveness analysis of multigene expression profiling assays to guide adjuvant therapy decisions in women with invasive early-stage breast cancer,” Pharmacogenomics J. 20(1), 27–46 (2020).
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McClatchy, D. M.

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).
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F. J. A. Gujam, J. Edwards, Z. M. A. Mohammed, J. J. Going, and D. C. McMillan, “The relationship between the tumour stroma percentage, clinicopathological characteristics and outcome in patients with operable ductal breast cancer,” Br. J. Cancer 111(1), 157–165 (2014).
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I. Krop, N. Ismaila, F. Andre, R. C. Bast, W. Barlow, D. E. Collyar, and R. G. Mennel, “Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update,” J. Clin. Oncol. 35(24), 2838–2847 (2017).
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S. Reis, P. Gazinska, J. H. Hipwell, T. Mertzanidou, K. Naidoo, N. Williams, and D. J. Hawkes, “Automated classification of breast cancer stroma maturity from histological images,” IEEE Trans. Biomed. Eng. 64(10), 2344–2352 (2017).
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Mesker, W. E.

C. J. H. Kramer, K. M. H. Vangangelt, G. W. van Pelt, T. J. A. Dekker, R. Tollenaar, and W. E. Mesker, “The prognostic value of tumour–stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review,” Breast Cancer Res. Treat. 173(1), 55–64 (2019).
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E. M. de Kruijf, J. G. H. van Nes, C. J. H. van de Velde, H. Putter, V. T. Smit, G. J. Liefers, and W. E. Mesker, “Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients,” Breast Cancer Res. Treat. 125(3), 687–696 (2011).
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W. E. Mesker, J. Junggeburt, K. Szuhai, P. de Heer, H. Morreau, H. J. Tanke, and R. A. E. M. Tollenaar, “The carcinoma–stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage,” Anal. Cell. Pathol. 29(5), 387–398 (2007).
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M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
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M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
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F. J. A. Gujam, J. Edwards, Z. M. A. Mohammed, J. J. Going, and D. C. McMillan, “The relationship between the tumour stroma percentage, clinicopathological characteristics and outcome in patients with operable ductal breast cancer,” Br. J. Cancer 111(1), 157–165 (2014).
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W. E. Mesker, J. Junggeburt, K. Szuhai, P. de Heer, H. Morreau, H. J. Tanke, and R. A. E. M. Tollenaar, “The carcinoma–stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage,” Anal. Cell. Pathol. 29(5), 387–398 (2007).
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Ł. Rączkowski, M. Możejko, J. Zambonelli, and E. Szczurek, “ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning,” Sci. Rep. 9(1), 14347 (2019).
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L. M. Wang, M. A. Silva, Z. D’Costa, R. Bockelmann, Z. Soonawalla, S. Liu, and R. Muschel, “The prognostic role of desmoplastic stroma in pancreatic ductal adenocarcinoma,” Oncotarget 7(4), 4183–4194 (2016).
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X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012).
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S. Reis, P. Gazinska, J. H. Hipwell, T. Mertzanidou, K. Naidoo, N. Williams, and D. J. Hawkes, “Automated classification of breast cancer stroma maturity from histological images,” IEEE Trans. Biomed. Eng. 64(10), 2344–2352 (2017).
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S. Ahn, J. Cho, J. Sung, J. E. Lee, S. J. Nam, K.-M. Kim, and E. Y. Cho, “The prognostic significance of tumor-associated stroma in invasive breast carcinoma,” Tumor Biol. 33(5), 1573–1580 (2012).
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M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
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M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
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P. Aurello, G. Berardi, D. Giulitti, A. Palumbo, S. M. Tierno, G. Nigri, and G. Ramacciato, “Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients,” The Surg. 15(6), 329–335 (2017).
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Palumbo, A.

P. Aurello, G. Berardi, D. Giulitti, A. Palumbo, S. M. Tierno, G. Nigri, and G. Ramacciato, “Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients,” The Surg. 15(6), 329–335 (2017).
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M. B. Hannouf, G. S. Zaric, P. Blanchette, C. Brezden-Masley, M. Paulden, C. McCabe, and M. Brackstone, “Cost-effectiveness analysis of multigene expression profiling assays to guide adjuvant therapy decisions in women with invasive early-stage breast cancer,” Pharmacogenomics J. 20(1), 27–46 (2020).
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B. U. Pauli, D. E. Schwartz, E. J. M. Thonar, and K. E. Kuettner, “Tumor invasion and host extracellular matrix,” Cancer Metastasis Rev. 2(2), 129–152 (1983).
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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).
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M. W. Conklin, J. C. Eickhoff, K. M. Riching, C. A. Pehlke, K. W. Eliceiri, P. P. Provenzano, and P. J. Keely, “Aligned collagen is a prognostic signature for survival in human breast carcinoma,” Am. J. Pathol. 178(3), 1221–1232 (2011).
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M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
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X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012).
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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).
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M. W. Conklin, J. C. Eickhoff, K. M. Riching, C. A. Pehlke, K. W. Eliceiri, P. P. Provenzano, and P. J. Keely, “Aligned collagen is a prognostic signature for survival in human breast carcinoma,” Am. J. Pathol. 178(3), 1221–1232 (2011).
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P. P. Provenzano, D. R. Inman, K. W. Eliceiri, J. G. Knittel, L. Yan, C. T. Rueden, and P. J. Keely, “Collagen density promotes mammary tumor initiation and progression,” BMC Med. 6(1), 11 (2008).
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Raczkowski, L.

Ł. Rączkowski, M. Możejko, J. Zambonelli, and E. Szczurek, “ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning,” Sci. Rep. 9(1), 14347 (2019).
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M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
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P. Aurello, G. Berardi, D. Giulitti, A. Palumbo, S. M. Tierno, G. Nigri, and G. Ramacciato, “Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients,” The Surg. 15(6), 329–335 (2017).
[Crossref]

Reis, S.

S. Reis, P. Gazinska, J. H. Hipwell, T. Mertzanidou, K. Naidoo, N. Williams, and D. J. Hawkes, “Automated classification of breast cancer stroma maturity from histological images,” IEEE Trans. Biomed. Eng. 64(10), 2344–2352 (2017).
[Crossref]

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M. W. Conklin, J. C. Eickhoff, K. M. Riching, C. A. Pehlke, K. W. Eliceiri, P. P. Provenzano, and P. J. Keely, “Aligned collagen is a prognostic signature for survival in human breast carcinoma,” Am. J. Pathol. 178(3), 1221–1232 (2011).
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Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, and A. E. Sisk, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
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P. P. Provenzano, D. R. Inman, K. W. Eliceiri, J. G. Knittel, L. Yan, C. T. Rueden, and P. J. Keely, “Collagen density promotes mammary tumor initiation and progression,” BMC Med. 6(1), 11 (2008).
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C. B. Weldon, J. R. Trosman, W. J. Gradishar, A. B. Benson, and J. C. Schink, “Barriers to the use of personalized medicine in breast cancer,” J. Oncol. Pharm. Pract. 8(4), e24–e31 (2012).
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B. U. Pauli, D. E. Schwartz, E. J. M. Thonar, and K. E. Kuettner, “Tumor invasion and host extracellular matrix,” Cancer Metastasis Rev. 2(2), 129–152 (1983).
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Shaaban, A. M.

C. L. Downey, H. H. Thygesen, N. Sharma, and A. M. Shaaban, “Prognostic significance of tumour stroma ratio in inflammatory breast cancer,” SpringerPlus 4(1), 68 (2015).
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Sharma, N.

C. L. Downey, H. H. Thygesen, N. Sharma, and A. M. Shaaban, “Prognostic significance of tumour stroma ratio in inflammatory breast cancer,” SpringerPlus 4(1), 68 (2015).
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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).
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L. M. Wang, M. A. Silva, Z. D’Costa, R. Bockelmann, Z. Soonawalla, S. Liu, and R. Muschel, “The prognostic role of desmoplastic stroma in pancreatic ductal adenocarcinoma,” Oncotarget 7(4), 4183–4194 (2016).
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C. L. Downey, S. A. Simpkins, J. White, D. L. Holliday, J. L. Jones, L. B. Jordan, and V. Speirs, “The prognostic significance of tumour-stroma ratio in oestrogen receptor-positive breast cancer,” Br. J. Cancer 110(7), 1744–1747 (2014).
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Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, and A. E. Sisk, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
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M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
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M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
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E. M. de Kruijf, J. G. H. van Nes, C. J. H. van de Velde, H. Putter, V. T. Smit, G. J. Liefers, and W. E. Mesker, “Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients,” Breast Cancer Res. Treat. 125(3), 687–696 (2011).
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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).
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Soonawalla, Z.

L. M. Wang, M. A. Silva, Z. D’Costa, R. Bockelmann, Z. Soonawalla, S. Liu, and R. Muschel, “The prognostic role of desmoplastic stroma in pancreatic ductal adenocarcinoma,” Oncotarget 7(4), 4183–4194 (2016).
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C. L. Downey, S. A. Simpkins, J. White, D. L. Holliday, J. L. Jones, L. B. Jordan, and V. Speirs, “The prognostic significance of tumour-stroma ratio in oestrogen receptor-positive breast cancer,” Br. J. Cancer 110(7), 1744–1747 (2014).
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S. Ahn, J. Cho, J. Sung, J. E. Lee, S. J. Nam, K.-M. Kim, and E. Y. Cho, “The prognostic significance of tumor-associated stroma in invasive breast carcinoma,” Tumor Biol. 33(5), 1573–1580 (2012).
[Crossref]

Swain, S.

A. Matikas, T. Foukakis, S. Swain, and J. Bergh, “Avoiding over-and undertreatment in patients with resected node-positive breast cancer with the use of gene expression signatures: are we there yet?” Ann. Oncol. 30(7), 1044–1050 (2019).
[Crossref]

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Ł. Rączkowski, M. Możejko, J. Zambonelli, and E. Szczurek, “ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning,” Sci. Rep. 9(1), 14347 (2019).
[Crossref]

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W. E. Mesker, J. Junggeburt, K. Szuhai, P. de Heer, H. Morreau, H. J. Tanke, and R. A. E. M. Tollenaar, “The carcinoma–stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage,” Anal. Cell. Pathol. 29(5), 387–398 (2007).
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H. Ueno, A. M. Jones, K. H. Wilkinson, J. R. Jass, and I. C. Talbot, “Histological categorisation of fibrotic cancer stroma in advanced rectal cancer,” Gut 53(4), 581–586 (2004).
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W. E. Mesker, J. Junggeburt, K. Szuhai, P. de Heer, H. Morreau, H. J. Tanke, and R. A. E. M. Tollenaar, “The carcinoma–stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage,” Anal. Cell. Pathol. 29(5), 387–398 (2007).
[Crossref]

Thonar, E. J. M.

B. U. Pauli, D. E. Schwartz, E. J. M. Thonar, and K. E. Kuettner, “Tumor invasion and host extracellular matrix,” Cancer Metastasis Rev. 2(2), 129–152 (1983).
[Crossref]

Thygesen, H. H.

C. L. Downey, H. H. Thygesen, N. Sharma, and A. M. Shaaban, “Prognostic significance of tumour stroma ratio in inflammatory breast cancer,” SpringerPlus 4(1), 68 (2015).
[Crossref]

Tierno, S. M.

P. Aurello, G. Berardi, D. Giulitti, A. Palumbo, S. M. Tierno, G. Nigri, and G. Ramacciato, “Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients,” The Surg. 15(6), 329–335 (2017).
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Tollenaar, R.

C. J. H. Kramer, K. M. H. Vangangelt, G. W. van Pelt, T. J. A. Dekker, R. Tollenaar, and W. E. Mesker, “The prognostic value of tumour–stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review,” Breast Cancer Res. Treat. 173(1), 55–64 (2019).
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Tollenaar, R. A. E. M.

W. E. Mesker, J. Junggeburt, K. Szuhai, P. de Heer, H. Morreau, H. J. Tanke, and R. A. E. M. Tollenaar, “The carcinoma–stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage,” Anal. Cell. Pathol. 29(5), 387–398 (2007).
[Crossref]

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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]

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M. S. Toss, I. M. Miligy, K. L. Gorringe, A. AlKawaz, K. Mittal, R. Aneja, and E. A. Rakha, “Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study,” Mod. Pathol. 32(10), 1473–1485 (2019).
[Crossref]

Trosman, J. R.

C. B. Weldon, J. R. Trosman, W. J. Gradishar, A. B. Benson, and J. C. Schink, “Barriers to the use of personalized medicine in breast cancer,” J. Oncol. Pharm. Pract. 8(4), e24–e31 (2012).
[Crossref]

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H. Ueno, A. M. Jones, K. H. Wilkinson, J. R. Jass, and I. C. Talbot, “Histological categorisation of fibrotic cancer stroma in advanced rectal cancer,” Gut 53(4), 581–586 (2004).
[Crossref]

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M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
[Crossref]

M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
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E. M. de Kruijf, J. G. H. van Nes, C. J. H. van de Velde, H. Putter, V. T. Smit, G. J. Liefers, and W. E. Mesker, “Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients,” Breast Cancer Res. Treat. 125(3), 687–696 (2011).
[Crossref]

Van Dorpe, J.

M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
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E. M. de Kruijf, J. G. H. van Nes, C. J. H. van de Velde, H. Putter, V. T. Smit, G. J. Liefers, and W. E. Mesker, “Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients,” Breast Cancer Res. Treat. 125(3), 687–696 (2011).
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Van Ongeval, C.

M. Van Bockstal, K. Lambein, A. Smeets, L. Slembrouck, P. Neven, I. Nevelsteen, and C. Van Ongeval, “Stromal characteristics are adequate prognosticators for recurrence risk in ductal carcinoma in situ of the breast,” Eur. J. Surg. Oncol. 45(4), 550–559 (2019).
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C. J. H. Kramer, K. M. H. Vangangelt, G. W. van Pelt, T. J. A. Dekker, R. Tollenaar, and W. E. Mesker, “The prognostic value of tumour–stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review,” Breast Cancer Res. Treat. 173(1), 55–64 (2019).
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Van Renterghem, S.

M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
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M. Van Bockstal, M. Baldewijns, C. Colpaert, H. Dano, G. Floris, C. Gallant, K. Lambein, D. Peters, S. Van Renterghem, A.-S. Van Rompuy, S. Verbek, S. Verschuere, and J. Van Dorpe, “Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance,” Histopathology 73(6), 923–932 (2018).
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C. J. H. Kramer, K. M. H. Vangangelt, G. W. van Pelt, T. J. A. Dekker, R. Tollenaar, and W. E. Mesker, “The prognostic value of tumour–stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review,” Breast Cancer Res. Treat. 173(1), 55–64 (2019).
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NameDescription
» Code 1       Github repository for processing code

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Figures (7)

Fig. 1.
Fig. 1. Diagram of the PLM imaging system.
Fig. 2.
Fig. 2. Outline of the imaging and image analysis pipeline used to calculate the SAS. During the imaging phase, both a) White light image of unstained slide and b) PLM cross-polarized stack imaged at 18 angular increments of 5° between 0° and 90° are acquired. c) Light intensity pattern for pixels corresponding to birefringent and non-birefringent regions (blue and red symbols). The corresponding image regions from which these pixels were chosen are highlighted with blue and red squares in b), d), e) and f). The former exhibit an oscillating intensity pattern characteristic of a birefringent material while the latter, originating from less birefringent structures such as tumor cells, exhibit no significant angular intensity variation. The normalized theoretical intensity oscillation (Eq. (2)) is shown by the dotted-purple line. From these intensity curves, three parametric images are derived. d) Maximum Intensity Range (MIR) image, correlating with the maximum peak-to-trough height of curves in c) at each pixel, indicated by the grey arrow in c). e) Mean Angular Difference (MAD) image, where the intensity values are indicative of the mean difference in fiber directions over a 5 × 5 pixel sliding analysis window (fiber directions were calculated from the angular location of the maximum (indicated by the yellow arrow) in c). f) R2 image, where the intensity values are indicative of the goodness-of-fit of c) to Eq. (2) (purple-dotted line in c)). g) SAS image, derived from the product of the three preceding images. Pixels corresponding to greater degree of stromal organization have higher SAS values indicated by lighter colors. h) In parallel, an adjacent H&E stained slide is scanned at 20X and registered with the polarimetry images for pathologist’s labelling (indicated scale bar same for all images). For implementation see Code 1 [29].
Fig. 3.
Fig. 3. A comparison of SAS parametric images (top row) and the resulting SAS scores with H&E histology (bottom row), for representative regions of the three stromal categories: a & d) = myxoid, b & e) = mildly myxoid and c & f) = sclerotic. The SAS scores, shown in white text, were computed for the entire regions shown in a), b) and c) respectively. Note the consistent trend of increasing SAS scores (higher values indicate more sclerotic stroma) and the pathologist assessment categories. The slightly lower resolution exhibited by the polarized light parametric images is due to 11 × 11 pixel sliding window used to calculate the SAS scores. Scale bar shown in d) is same for all images.
Fig. 4.
Fig. 4. Box plots for the three intermediate metrics scores (left) and the final SAS score (right) calculated for each of the three pathologist-labelled categories for this study. For each box plot, the central line shows the mean, the box denotes the 2nd and 3rd quartiles, the whiskers indicate the 1st and 4th quartile respectively, and outliers are demarcated by symbols. Outliers on the top border of a graph indicate that they occur above the maximum value on the axes but were compressed to enable visualization. a) Intensity scores b) Alignment scores c) Density scores d) SAS scores. e) Table showing the SAS summary statistics. The median corresponds to the position of the central line in d), while min and max correspond to the positions of the bottom and top whiskers respectively; mean and standard deviation were calculated with outliers included. Of note is the greater separation between myxoid and sclerotic classes after incorporation all three metrics into the SAS score, as well as the significant reduction in variance of the myxoidal categories as seen in d).
Fig. 5.
Fig. 5. A visual and numerical representation of the overlap (orange) between myxoid (blue) and sclerotic (green) ROIs classified by a) Intensity b) Alignment c) Density and d) SAS scores. The numbers in the blue and green circles are the amounts of correctly identified myxoid and sclerotic ROIs respectively; those in the middle orange regions indicate the misclassifications, or, overlap of the two groups. Of particular interest is the substantially improved separation afforded by the SAS score d), compared to any of the individual a) – c) metrics. Alongside each Venn diagram is a confusion matrix displaying the correct classifications (blue and green) and the misclassifications (orange). e) Table indicating the sensitivity, specificity and total accuracy of each of the metrics and the composite SAS. The thresholds were as follows: Intensity = 1255, Alignment = 0.490, Density = 0.486 and SAS = 123, each calculated to maximize the sensitivity to myxoid stroma. Note the resultant poor specificity and total accuracy for each of the individual metrics, and the significantly better SAS results.
Fig. 6.
Fig. 6. H&E stained IDC sample slides labelled as a) highly myxoid b) slightly myxoid c) slightly sclerotic d) highly sclerotic by a pathologist. Calculated SAS scores are shown in the middle row, and overall pathologist classification categories are on the bottom. Of particular interest is the continuous numerical SAS spectrum of stromal signatures, potentially more informative than the binary two-broad-categories subjective approach currently employed in pathology.
Fig. 7.
Fig. 7. Comparison of the H&E and SAS parametric images for two pathology-categorized sclerotic regions that were SAS-misclassified as myxoid. a, b) A region of edematous collagen (red box), labelled sclerotic by a pathologist but SAS-misidentified as myxoid. Although the brightness of the fibers would suggest the correct (sclerotic) class, the relatively low density reduces the resultant SAS score to the myxoid levels. c, d) A region containing elastin (red box) that was erroneously identified as myxoid by its SAS score. For comparison, note high SAS values shown for the collagen region (black box). Scale is the same across all images.

Equations (7)

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

I n t e n s i t y = sin 2 ( Δ n π t λ ) sin 2 ( 2 τ )
I n t e n s i t y sin 2 ( 2 τ )
M I R ( i , j ) z = 1 Z ( y ( i , j , z ) y ¯ ( i , j ) ) 2 z 1
M A D i = 1 n 1 j = i n B ( i ) B ( j ) n ( n 1 ) 2
R 2 1 z = 1 Z ( y ( i , j , z ) f ( i , j , z ) ) z = 1 Z ( y ( i , j , z ) y ¯ ( i , j ) )
D e n s i t y  Score # p i x e l s   w i t h   R 2 > 0.75 t o t a l   #  pixels
S A S = I n t e n s i t y A l i g n m e n t D e n s i t y