Bladder cancer organoid image analysis: textured-based grading

Seda Camalan; M. Khalid Khan Niazi; Mahesh Devarasetty; Shay Soker; Metin N. Gurcan

doi:10.1117/12.2582208

15 February 2021 Bladder cancer organoid image analysis: textured-based grading

Seda Camalan, M. Khalid Khan Niazi, Mahesh Devarasetty, Shay Soker, Metin N. Gurcan

Proceedings Volume 11603, Medical Imaging 2021: Digital Pathology; 1160305 (2021) https://doi.org/10.1117/12.2582208
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Bladder cancer has high recurrence rates, which leads to treatment difficulties and reduced survival. Field cancerization is the prevailing idea for why bladder cancers recur with high frequency, and it centers around genetic and epigenetic changes in tissue that lead to conditions favoring recurrence. However, the specifics of these alterations are not well understood or described. The tumor microenvironment (TME) has been implicated as a strong proponent of oncogenesis in many organ systems, including the bladder. The TME comprises stromal cells, paracrine factors, and extracellular matrix (ECM) components, which may contribute to field cancerization. As such, identifying the hallmarks of these alterations may expedite the prognosis of recurrence. For this purpose, we fabricated bladder cancer organoids of varied cancer grades, with which we developed a texture-based grading system. Image texture is characterized by filtering images and finding their similarity. The similar images are clustered, and the cumulative histogram of clusters is formed to find the closest training image. In two independent image data sets of 54 and 76 images, respectively, with different imaging protocols, 100% and 92% accuracy were achieved.

Conference Presentation

Citation Download Citation

Seda Camalan, M. Khalid Khan Niazi, Mahesh Devarasetty, Shay Soker, and Metin N. Gurcan "Bladder cancer organoid image analysis: textured-based grading", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 1160305 (15 February 2021); https://doi.org/10.1117/12.2582208

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