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Proceedings Paper

Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer
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Paper Abstract

Glandular features play an important role in the evaluation of prostate cancer. There has been significant interest in the use of 2D pathomics (feature extraction) approaches for detection, diagnosis, and characterization of prostate cancer on digitized tissue slide images. With the development of 3D microscopy techniques, such as open-top light-sheet (OTLS), there is an opportunity for rapid 3D imaging of large tissue specimens such as whole biopsies. In this study, we sought to investigate whether 3D features of gland morphology, namely volume and surface curvature, from OTLS images offer superior discrimination between malignant and benign glands compared to the traditional 2D gland features, namely area and curvature, alone. In this study, a cohort of 8 de-identified fresh prostate biopsies comprehensively imaged in 3D via the OTLS platform. A total of 367 glands were segmented from these images, of which 79 were identified as benign and 288 were identified as malignant. Glands were segmented using a 3D watershed algorithm followed by post-processing steps to filter out falsepositive regions. The 2D and 3D features were compared quantitatively and qualitatively. Our experiments demonstrated that a model using 3D features outperformed one using 2D features in differentiating benign and malignant glands. In 3D, both features, gland volume (p = 1.45 × 10−3) and surface curvature (p = 3.2 × 10−3), were found to be informative whereas in 2D, only gland area (p = 9 × 10−18) was found to be discriminating (p = 0.79 for 2D curvature). Notable visual and quantitative differences between 3D benign/malignant glands encourage the development of additional more sophisticated features in the future.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200G (16 March 2020); doi: 10.1117/12.2549726
Show Author Affiliations
Can F. Koyuncu, Case Western Reserve Univ. (United States)
Andrew Janowczyk, Case Western Reserve Univ. (United States)
Cheng Lu, Case Western Reserve Univ. (United States)
Patrick Leo, Case Western Reserve Univ. (United States)
Mehdi Alilou, Case Western Reserve Univ. (United States)
Adam K. Glaser, Univ. of Washington (United States)
Nicholas P. Reder, Univ. of Washington (United States)
Jonathan T. C. Liu, Univ. of Washington (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)
Louis Stokes Cleveland Veterans Administration Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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