Quantifying Morphologic Phenotypes in Prostate Cancer - Developing Topological Descriptors for Machine Learning Algorithms

This page describes the scope and results funded by the following grant:

8/1/17 - 7/31/21

"QuBBD: Collaborative Research: Quantifying Morphologic Phenotypes in Prostate Cancer - Developing Topological Descriptors for Machine Learning Algorithms", National Science Foundation and National Institutes of Health, NSF-DMS 1664848, $479,293. Role: PI. Collaboration with Co-PIs Quincy Brown (Biomedical Engineering) and Brian Summa (Computer Science) at Tulane and with Brittany Fasy at Montana State University; $899,999 total grant amount.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Abstract

This research will develop quantitative topological descriptors (e.g., persistence diagrams and summaries) that describe natural histologic phenotypes in prostate cancer, in order to provide explanatory information to assist in providing improved diagnostics/prognostics and insight into the best course of treatment for the patient. This will be accomplished through developing graphical models via unsupervised machine learning that increase our understanding of prostate cancer subtypes. The long-term goal is to develop imaging biomarkers that better identify indolent from aggressive prostate cancer compared to existing, subjective, and variable human observer analyses (i.e., the Gleason score). This project takes steps towards a novel quantitative methodology for prostate cancer classification, as well as towards developing topological methods for statistically distinguishing different types of glandular architectures.

Project Leadership:

• Quincy Brown, Tulane University, Department of Biomedical Engineering
• Brian Summa, Tulane University, Department of Computer Science
• Brittany Fasy, Montana State University, Department of Computer Science
• John Sheppard, Montana State University, Department of Computer Science
• Carola Wenk, Tulane University, Department of Computer Science

Students:
 
Current:

• Max Rick, University of California at Los Angeles (undergraduate, Computer Science)
• Amaya Williams, Columbia University (undergraduate, Dance, Computer Science)
• Justin Phillips, Tulane University (undergraduate, Economics, Computer Science)
• Raphael Deykin, Tulane University (undergraduate, Computer Science)
• Demi Qin, Tulane University (PhD student, Computer Science)
• Jordan Schupbach, Montana State University (PhD student, Statistics)
• Robin Belton, Montana State University (PhD student, Mathematics)
• Anna Schenfisch, Montana State University (PhD student, Mathematics)
• Pete Lawson, Tulane University (PhD student, Bioinnovation)

• Joseph Allen, Tulane University (undergraduate, Computer Science)
• Nathan Stouffer, Montana State University (undergraduate, Computer Science and Mathematics; Undergraduate Scholars Program)
• Arash Ajamb, Montana State University (undergraduate, Computer Science; Undergraduate Scholars Program)
• Po-Yu (Timmy) Wu, Columbia University (undergraduate, Computer Science)
• Alan Cleary, Montana State University (PhD student, Computer Science)
• Eric Berry, Montana State University (PhD student, Mathematics)

Collaborators:

• Sharon E. Fox, Southeast Louisiana Veterans Health Care System
• Andrew Sholl, Ochsner Health System
• Greg Pritham, Bozeman Health Deaconess Hospital (Urology)
• Noura Faraj, Université Montpellier
• Guillaume Favelier and Julien Tierny (Sorbonne Université and CNRS, France)

Publications and Activities:

• "A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces" (Y. Qin, B.T. Fasy, C. Wenk, B. Summa), arXiv:2105.12208, 2021.
• Tulane Histopathology Image Annotation Platform
• "Adaptive Compositing and Navigation of Variable Resolution Images", (C. Licorish, N. Faraj, B. Summa), Computer Graphics Forum, conditionally accepted, 2020.
• "Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution", (D. Hoang, B. Summa, P. Klacansky, W. Usher, H. Bhatia, P. Lindstrom, P.-T. Bremer, V. Pascucci), Transactions on Visualization and Computer Graphics (Proceedings of IEEE VIS 2020), 2020.
• "Reconstructing Embedded Graphs from Persistence Diagrams", (R. L. Belton, B. T. Fasy, R. Mertz, S. Micka, D. L. Millman, D. Salinas, A. Schenfisch, J. Schupbach, and L. Williams), Invited article to a special issue Journal of Computational Geometry: Theory and Applications (CGTA) 90, 2020.
• ROI data repository containing the data utilized in the publication "Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology", comprising 5,182 regions of interest (ROIs) derived from radical prostatectomy cases at Tulane Medical Center. 2020.
• Multiscale three-dimensional pathology findings of COVID-19 diseased lung using high-resolution cleared tissue microscopy'', (G. Li, S.E. Fox, B. Summa, B. Hu, C. Wenk, A. Akmatbekov, J.L. Harbert, R.S. Vander Heide, J.Q. Brown), bioRxiv 2020.04.11.037473, 2020.
• "Functional summaries of persistence diagrams", (E. Berry, Y.-C. Chen, J. Cisewski-Kehe, B.T. Fasy), Journal of Applied and Computational Topology 4: 211-262, 2020.
• "On Moduli Spaces of Morse Functions for Persistence", (M. Catanzaro, J. Curry, J. Lazovskis, G. Malen, H. Riess, B. Wang, and M. Zabka), Journal of Applied and Computational Topology 4: 353-385, 2020.
• MSU doctoral student earns prestigious fellowship for computational geometry
• "Assessment of Sampling Adequacy Using Persistent Homology for the Evaluation of Heterogeneity in 3D Histology Acquired Through Inverted Selective Plane Illumination Microscopy (iSPIM)" (P. Lawson, B. Hu, B.T. Fasy, B. Summa, C. Wenk, J.Q.Brown), SPIE European Conferences on Biomedical Optics (ECBO), invited paper, 2019.
• "Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images", (P. Lawson, J. Schupbach, J.W. Sheppard), Medical Imaging 2019: Digital Pathology, 2019.
• "Variable Resolution Panorama Stitching and Navigation", (C. Licorish, N. Faraj, B. Summa), in submission, 2019.
• "Interactive Visualization of Large Voxel Grids under Deformation for EMF Exposure Simulations", (N. Faraj, J.-M. Tierny, B. Summa, T. Boubekeur), in submission, 2019
• "Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology", (P.J. Lawson, A. Sholl, J.Q. Brown, B.T. Fasy, C. Wenk), Scientific Reports 9: 2045-2322, 2019.
• "Persistence Atlas for Critical Point Variability in Ensembles", (Guillaume Favelier, Noura Faraj, Brian Summa, Julien Tierny), IEEE Transactions on Visualization and Computer Graphics 25: 1152 - 1162, 2019. (IEEE Visualization 2018).
• "A perfect storm: How disruptive imaging and data technologies are poised to transform cancer diagnosis and treatment", (Q. Brown), invited seminar at the LSU-HSC/Louisiana Tech Research and Industry Day (RAID) Conference, Shreveport, LA in October 2018.
• "High throughput optical sectioning microscopy and computational topology for a new digital pathology workflow", (Q. Brown), invited seminar at the Department of Physics, University of Miami at Ohio, Oxford, OH, in September 2018.
• "Challenges in Reconstructing Shapes from Euler Characteristic Curves", (B.T. Fasy, S. Micka, D.L. Millman, A. Schenfish, L. Williams), Fall Workshop on Computational Geometry, Queens College, CUNY, 2018.
• "Learning Simplicial Complexes from Persistence Diagrams", (R. L. Belton, B. T. Fasy, R. Mertz, S. Micka, D. L. Millman, D. Salinas, A. Schenfisch, J. Schupbach, and L. Williams), Proc. Canadian Conference on Computational Geometry, 2018.
• Applications of Persistent Homology to Low-Dimensional Data, (C. Wenk), invited talk, TGDA@OSU TRIPODS Center Workshop on "Theory and Foundations of TGDA", Ohio State University, 05/2018.
• "Flexible Live-Wire: Image Segmentation with Floating Anchors", (B. Summa, N. Faraj, C. Licorish, V. Pascucci), Computer Graphics Forum 37: 321 - 328, 2018
• "Functional Summaries of Persistence Diagrams", (Eric Berry, Yen-Chi Chen, Jessi Cisewski-Kehe, Brittany Terese Fasy), https://arxiv.org/abs/1804.01618 (preprint, currently in submission)
• "Curvature Estimates of Point Clouds as a Tool in Quantitative Prostate Cancer Classification", (A. Schenfisch and B.T. Fasy), Computational Geometry: Young Researchers Forum, 2018.
• "Quantifying prostate cancer morphology in 3D using light sheet microscopy and persistent homology", conference presentation, (P.J. Lawson, B. Hu, B.T. Fasy, C. Wenk, J.Q. Brown), Proc. SPIE 10472, Diagnosis and Treatment of Diseases in the Breast and Reproductive System IV, 1047209 (14 March 2018); doi: 10.1117/12.2290994.

Related Projects:

• Towards Automated Quantitative Prostate Cancer Diagnosis