Dr. Kamdin Mirsanaye Profile

Dr. Kamdin Mirsanaye

at Univ of Toronto

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Publications (3)

Proceedings Article | 30 May 2022 Presentation

Investigation of melanoma tissue with nonlinear multimodal polarimetric microscopy using texture analysis and machine learning

Martynas Riauka, Viktoras Mazeika, Mykolas Maciulis, Lukas Kontenis, Edvardas Zurauskas, Mehdi Alizadeh, Kamdin Mirsanaye, Virginijus Barzda

Proceedings Volume PC12144, PC121440S (2022) https://doi.org/10.1117/12.2621489

KEYWORDS: Polarimetry, Tissues, Melanoma, Collagen, Polarization, Microscopy, Second-harmonic generation, Machine learning, Statistical analysis, Microscopes

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Development and metastasis of cancer are known to change the structure of extracellular matrix (ECM), which affect the tumor's further growth and spread. A substantial part of ECM is comprised of collagen, which is a noncentrosymmetric structure. As a result, it generates second harmonic signals, dependent on the polarization of incoming light. This property of collagen led to the applications of polarization-resolved second-harmonic generation (P-SHG) microscopy in investigating collagen ultrastructure changes in different cancers. In this work, multiphoton absorption fluorescence (MPF), third-harmonic generation (THG) and polarimetric second-harmonic generation (P-SHG) measurements were performed on various types and staging of human melanoma histological sections. Reduced polarimetry techniques, employing linear and circular polarization states, were used to obtain polarimetric SHG parameters of collagen in both normal and cancerous tissues. These parameters provide important information about the structural properties of collagen. The parameter distributions were analyzed using a grey-level co-occurrence matrix (GLCM), which allows to obtain statistical parameters, such as correlation, contrast, entropy, angular second moment and inverse difference moment. Statistical tests were performed on polarimetric and texture analysis data in order to determine whether parameter distribution differences in normal and cancerous tissues are statistically significant. Furthermore, a machine learning classifier algorithm was trained to distinguish normal tissues from cancerous using aforementioned polarimetric and texture parameters as predictors. Firstly, separate training and testing datasets were formed from each sample and classification was carried out for each of them individually and afterwards, a common training dataset was used for all samples. The results suggest that normal and cancerous skin tissues can be distinguished from each other with the help of multimodal nonlinear polarimetric microscopy. Also, depending on the type and stage of melanoma, the differences in some polarimetric and texture parameters are more pronounced, suggesting its possible application in melanoma diagnostics and differentiation.

Proceedings Article | 30 May 2022 Presentation

Investigation of tumor extracellular matrix using polarimetric second-harmonic generation microscopy and texture analysis

Viktoras Mažeika, Mykolas Maciulis, Lukas Kontenis, Edvardas Zurauskas, Martynas Riauka, Mehdi Alizadeh, Kamdin Mirsanaye, Virginijus Barzda

Proceedings Volume PC12144, PC121440R (2022) https://doi.org/10.1117/12.2621392

KEYWORDS: Polarimetry, Second-harmonic generation, Tumors, Microscopy, Collagen, Polarization, Signal detection, Cancer, Pancreas, Microscopes

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Extracellular matrix (ECM) has important functions in cell proliferation, differentiation, and migration, which influence the development and progression of cancer. ECM in tumor microenvironment experiences changes in composition and structure that can appear early in tumor development and could serve as a biomarker for cancer diagnostics. In addition, some changes in ECM may correlate with the rate of tumor progression or its tendency to form metastases and would allow to predict future tumor development [1]. Collagen is an important structural protein found in ECM. It has a non-centrosymmetric structure, and, thus, can be easily visualized using second harmonic generation (SHG) microscopy. SHG microscopy employs certain polarimetric techniques to gain detailed information about the organization of collagen in various tissues [2]. In this work, polarimetric SHG microscopy is used to acquire collagen images from normal and cancerous regions of human colon and pancreas histological samples. Texture analysis is performed on SHG intensity and polarization images to characterize the distribution of ultrastructure parameters in the tissue. Significant differences are observed in collagen ultrastructure between normal and tumor areas. Further, collagen structures of colon and pancreas tumor microenvironments are compared to investigate relative differences in ECM organization between the tissues. Finally, a machine learning classifier is used to group the acquired images in tumor and normal groups. The results show potential for development of novel cancer diagnostic technique using polarimetric second harmonic generation microscopy and texture analysis. [1] Winkler, J. et al., “Concepts of extracellular matrix remodelling in tumour progression and metastasis”, Nat Commun 11, 5120 (2020). [2] Golaraei, A. et al., “Polarimetric second-harmonic generation microscopy of the hierarchical structure of collagen in stage I-III non-small cell lung carcinoma,” Biomed. Opt. Express 11, 1851-1863 (2020).

Proceedings Article | 7 December 2021 Paper

Wide-field polarization-resolved SHG microscopy in biomedical imaging

Kamdin Mirsanaye, Leonardo Uribe Castano, Yasmeen Kamaliddin, Ahmad Golaraei, Lukas Kontenis, Susan Done, Edvardas Žurauskas, Brian Wilson, Virginijus Barzda

Proceedings Volume 11922, 119220M (2021) https://doi.org/10.1117/12.2615714

KEYWORDS: Tissues, Second-harmonic generation, Microscopy, Collagen, Tumors, Cancer, Biomedical optics, Polarization, Polarimetry, Diagnostics

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