Quantitative Multiphoton Microscopy in Cancer Research

Characterization of Nodule Capsule in Thyroid Pathology

  • Fig. 1: Traditional H&E histopathology images (upper row) acquired with a 20X objective and SHG (blue)/TPEF (red) microscopy images (lower row) for representative areas for the capsule invasion area, a PTC nodule capsule (cPTC) and the same nodule capsule near the invasion area (iPTC). Parameters computed from SHG datasets for the cPTC and iPTC cases. Under each parameter the value with which they were normalized is shown.Fig. 1: Traditional H&E histopathology images (upper row) acquired with a 20X objective and SHG (blue)/TPEF (red) microscopy images (lower row) for representative areas for the capsule invasion area, a PTC nodule capsule (cPTC) and the same nodule capsule near the invasion area (iPTC). Parameters computed from SHG datasets for the cPTC and iPTC cases. Under each parameter the value with which they were normalized is shown.

It has been recently proposed [1] the quantitative analysis of the fibrous capsule surrounding benign and many malignant thyroid nodules to support the pathologist in placing a diagnosis in the case of challenging thyroid pathologies examined only with the traditional histopathological procedures. This approach holds significant potential for application in the case of follicular thyroid carcinoma vs. follicular adenoma differential diagnosis.

Introduction

Laser scanning microscopy techniques that exploit nonlinear optical effects such as two photons excited fluorescence (TPEF) and second harmonic generation (SHG) have been proven to be useful tools in tissue imaging. SH signals are generated from specific endogenous molecules with a non-centrosymmetric structure, hence this contrast mechanism is intrinsic to collagen in tissue [2]. Due to the coherent nature of SH signals, the intensity dependence on the laser beam polarization is sensitive to the structure and arrangement of the molecules that produce the signal [3]. Hence, polarization-resolved SHG (PSHG) microscopy offers additional information beyond intensity-based SHG. Quantitative (P)SHG microscopy methods that either provide an estimate for the molecular structure of collagen in tissues by fitting theoretical models with experimental data at pixel-level or whole-image statistics are gaining increasing momentum. Such methods offer reliable diagnostic-relevant information that may complement traditional histological examination. The quantitative approach to SHG microscopy can use either different texture analysis procedures such as simple first level statistics [4], the Gray-level co-occurrence matrix (GLCM) [5], fractal analysis [6], or the structure tensor [7], or PSHG image stacks, theoretical models [8], [9] and different fitting algorithms to determine the coefficients of the second order nonlinear susceptibility tensor (χ(2)) at pixel level.

Thyroid nodules commonly arise within a normal thyroid gland and often they do not produce any symptoms. They cannot be regarded as the expression of a single disease but represent the clinical manifestation of a wide range of different pathologies [10].

They can be benign such as hyper-plastic, colloid nodules or follicular adenomas (FA). However, up to 10% of the lesions that come to medical attention are malignant. The standard treatment of thyroid tumors is the surgical excision of the entire or part of the thyroid gland. The diagnosis is confirmed by visualizing hematoxylin and eosin (H&E) stained thyroid sections in optical microscopy.

Although the thyroid nodule capsule has been neglected when placing a diagnosis until recently, capsular and/or vascular invasion has remained the main criterion for the diagnosis of malignancy in encapsulated well-differentiated thyroid tumors. Using traditional histology and cytology procedures, pathologist have difficulties in differentiating follicular adenoma vs. follicular thyroid carcinoma (FTC). The current routine is time-consuming and troublesome since it involves cutting serial sections from the tissue block and searching for the capsular invasion.

SHG microscopy has thus been used as a quantitative tool to extract the collagen organization in thyroid nodule capsule for capsular invasion detection. This can either be achieved with fresh tissue fragments, on fixed tissue sections or in a non-invasive manner, in vivo.

Method

The method involves the acquisition of (P)SHG datasets on thyroid nodule capsules. PSHG image stacks were acquired with a linear laser beam polarization obtained using an achromatic quarter-wave and an achromatic half-wave plate, mounted in motorized rotation stages and placed in the laser beam path before the microscope. The laser beam polarization was rotated by increments of 20º from 0º to 180º. 2D SHG images can either be computed from the PSHG stack by averaging the linear polarization-resolved images, resulting in a polarization-independent image or acquired under circular input polarization.

The collagen distribution at large-scale has been characterized (whole image-level 250 x 250 µm2) using texture analysis performed by computing parameters directly related to the gray level distribution of pixel intensities extracted from the 2D SHG images (mean, standard deviation, skewness and kurtosis), two parameters quantifying the collagen content in the image (TC-ration, S-mean) [1], parameters derived from the GLCM (contrast, inverse difference moment – IDM, angular second moment – ASM, entropy and correlation), fractal dimension and lacunarity computed on both binary and grayscale images and the average collagen orientation from Helmholtz analysis. The pixel-level information was obtained by fitting the PSHG image stacks with theoretical models [11] and extracting in a pixel-by-pixel approach ratios between  tensor elements (χ3115 and χ3315) and the collagen fiber orientation angle.

Results

By combining SHG microscopy with quantification parameters (fig. 1) for image texture analysis provided by the histogram analysis, gray level co-occurrence matrix and fractal analysis applied on the collagenous capsule surrounding papillary thyroid carcinoma nodules, it has been proven that the collagen distribution in the nodule capsules can provide information on the possibility of a capsular invasion. From the 18 tested parameters, 8 returned statistically relevant differences between the nodule capsule and near-invasion area. The results indicate that a unique collagen topology can be detected in the proximity of the invasion area based on SHG imaging. The parameter values (e.g. entropy) are correlated with a further stretching of the collagen capsule close to the capsular invasion area. It has been previously observed [1] that for the case of the malignant nodules compared with the benign ones a stretching of the capsule can be explained by the rapid growth of the PTC nodule.

Regarding the pixel-level analysis statistical differences were obtained when comparing both χ3115 (0.862 away from invasion site and 0.819 close to the invasion site) and χ3315 (2.177 away from invasion site and 2.027 close to the invasion site). We compared median values of the parameters in the considered ROI, because their distributions failed the normality tests. Significantly higher χ3315 values (p < 0.001) has been obtained for the nodule capsule away from the invasion area than closer to it. Changes in the χ3315 values were previously interpreted as possible alterations in the ultrastructure of collagen fibrils and fibers. An increase of χ33/χ15 was attributed to a redistribution of collagen fibrils and fibers in the laser focal volume [12]. Hence, the higher χ3315 values may indicate randomly organized collagen fibrils in the capsule away from the invasion site. These results are consistent with the ones obtained for collagen fibers in nodule capsules when using SHG microscopy and texture analysis. The efficiency of fitting, which has been recently defined [13] also indicates a modification of collagen ultrastructure.

Conclusion

Our results point towards both pixel-level and large-scale modification of the collagen fibers in the nodule capsule in the proximity of the capsular invasion area. These results indicate that quantitative SHG microscopy is a reliable method to detect near-invasion or even pre-invasion areas in the collagen capsule surrounding the thyroid nodules. This is important because it paves the way for quantitative SHG microscopy approaches that can be used in a clinical setting for the investigation of thyroid nodule capsules to provide important information that can be of help to a pathologist for consolidating the diagnostic decision.

Acknowledgements

This work was supported by the Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) under grants PN-III-P1-1.1-TE-2016-1756 (NANOSHG) and PN-III-P2-2.1-PED-2016-1252 (MICAND).
 

Authors
Radu Hristu1, Lucian G. Eftimie1,2, Bogdan Paun1,3, Stefan G. Stanciu1, Denis E. Tranca1, George A. Stanciu1

Affiliations
1Center for Microcopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Romania
2Department of Pathology, Central University Emergency Military Hospital, Romania
3Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Romania

Contact
Prof. Dr. George A. Stanciu

Department of Physics
Director of Center for Microscopy- Microanalysis and Information Processing
University “Politehnica” of Bucharest
București, Rumänien
stanciu@physics.pub.ro
 

References
[1] R. Hristu et al., “Quantitative second harmonic generation microscopy for the structural characterization of capsular collagen in thyroid neoplasms,” Biomed. Opt. Express, vol. 9, no. 8, p. 3923, 2018.
[2] R. M. Williams, W. R. Zipfel, and W. W. Webb, “Interpreting second-harmonic generation images of collagen I fibrils,” Biophys. J., 2005.
[3] X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc., vol. 7, no. 4, pp. 654–669, 2012.
[4] L. B. Mostaço-Guidolin et al., “Collagen morphology and texture analysis: From statistics to classification,” Sci. Rep., 2013.
[5] R. Cicchi et al., “Scoring of collagen organization in healthy and diseased human dermis by multiphoton microscopy,” J. Biophotonics, 2010.
[6] K. E. Frisch, S. E. Duenwald-Kuehl, H. Kobayashi, C. S. Chamberlain, R. S. Lakes, and R. Vanderby, “Quantification of collagen organization using fractal dimensions and Fourier transforms,” Acta Histochem., 2012.
[7] F. J. Avila and J. M. Bueno, “Analysis and quantification of collagen organization with the structure tensor in second harmonic microscopy images of ocular tissues,” Appl. Opt., 2015.
[8] S. Psilodimitrakopoulos, S. I. C. O. Santos, I. Amat-Roldan, A. K. N. Thayil, D. Artigas, and P. Loza-Alvarez, “In vivo, pixel-resolution mapping of thick filaments’ orientation in nonfibrilar muscle using polarization-sensitive second harmonic generation microscopy.,” J. Biomed. Opt., 2011.
[9] J. Duboisset, D. Aït-Belkacem, M. Roche, H. Rigneault, and S. Brasselet, “Generic model of the molecular orientational distribution probed by polarization-resolved second-harmonic generation,” Phys. Rev. A - At. Mol. Opt. Phys., 2012.
[10] R. Harris et al., “Modern Approach to Surgical Intervention of the Thyroid and Parathyroid Glands,” Semin. Ultrasound, CT MRI, 2012.
[11] R. Hristu, B. Paun, L. Eftimie, S. G. Stanciu, D. E. Tranca, and G. A. Stanciu, “Changes in the Collagen Structure of Thyroid Nodule Capsules Determined by Polarization-Resolved Second Harmonic Generation Microscopy,” in International Conference on Transparent Optical Networks, 2018.
[12] D. Tokarz, R. Cisek, A. Golaraei, S. L. Asa, V. Barzda, and B. C. Wilson, “Ultrastructural features of collagen in thyroid carcinoma tissue observed by polarization second harmonic generation microscopy,” Biomed. Opt. Express, 2015.
[13] B. Paun, R. Hristu, S. G. Stanciu, A. V. Dumitru, M. Costache, and G. A. Stanciu. "Strategies for Optimizing the Determination of Second-Order Nonlinear Susceptibility Tensor Coefficients for Collagen in Histological Samples." IEEE Access 7 (2019): 135210-135219.

 

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77206 Bucharest
Romania

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