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[LOGO] 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.


The long-term goal of this project is to develop quantitative methodology for detecting geometric and topological features in point clouds extracted from (histology) images. Of particular relevance, this project considers the setting of prostate cancer classification, which is based on a pathologist grading of histology slides using the Gleason grading system. These pathology slides are a source of biomedical big data that are increasingly available as archived material. Developing these quantitative methods will be a significant advance towards a (semi-)automated quantification of prostate cancer aggressiveness. This award supports an interdisciplinary team of investigators in computational mathematics, computer science, biomedical engineering, and pathology to develop mathematical and computational tools based on topological descriptors and machine learning in order to distinguish between different morphological types of prostate cancer.

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.

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Last modified by Carola Wenk,   cwenk  -at-   tulane  -dot-   edu , 08/26/2015 12:58:10