Presentation
Wide-field Phase-Contrast Micro-CT and Computational Topology for the 3D Exploration of Prostate Cancer and other Soft Tissue Tumors
DescriptionThe diagnosis and grading of cancer rely on the examination of abnormal tissue and the morphology of the cells within. For example, the clinical evaluation of prostate cancer requires the assessment of glandular and cellular morphology from histopathology images. However, prostate cancer patients suffer from high rates of inter-observer variability among pathologists in the clinic. Additionally, recent studies have shown that the angle and depth of slide sectioning also contribute to significant variation in tumor grading, further illustrating the need for a quantitative, 3-dimensional, volumetric approach to prostate cancer whole biopsy imaging. We present the development of a propagation-based phase-contrast micro-CT approach that produces volumetric images of whole prostate needle core biopsies without the addition of contrast-enhancing stain. We cross-validate these images by comparing diagnostic features visible via x-ray imaging with those observed by clinically trained pathologists using conventional histopathology slides collected from matching samples. We then adapt techniques from topological data analysis (TDA) to quantify the variation in glandular architecture associated with depth within the sample, as well as age and comorbidity of the patient. Formalin-fixed, paraffin-embedded (FFPE), unstained prostate cancer samples containing phenotypes from each Gleason pattern were imaged in 3D at Lawrence Berkeley National Lab (LBNL). Hematoxylin and eosin-stained histopathology slides were obtained and scanned from each of these samples as a control reference for the X-ray images. All images scanned and analyzed in these experiments will be de-identified and made publicly available via a customized version of the open-source web viewer Neuroglancer. We aim to democratize the results from this work and subsequent similar experiments such that other scientists and clinicians might use our data to develop and train new models for the measurement of tumor phenotype and heterogeneity. In summary, this study reports the identification of reproducible imaging parameters for the non-destructive 3D reconstruction of soft-tissue tumor biopsies at cellular resolution without the addition of contrast-enhancing stain – a significant step towards advancing the clinical diagnosis of prostate cancer. Further, we also report an interpretable computational model for the quantification of glandular shape and its variation – the key diagnostic feature in prostate cancer and a crucial marker for disease severity. This advancement of 3D histopathology and computational topology will serve public health needs by improving the diagnosis of prostate cancer and other soft-tissue malignancies.