| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | August 14, 2017 |
| Latest Amendment Date: | May 19, 2020 |
| Award Number: | 1664848 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Pena Edsel
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 1, 2017 |
| End Date: | July 31, 2021 (Estimated) |
| Total Intended Award Amount: | $479,293.00 |
| Total Awarded Amount to Date: | $479,293.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
6823 SAINT CHARLES AVE NEW ORLEANS LA US 70118-5665 (504)865-4000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6823 St Charles Avenue New Orleans LA US 70118-5698 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CDS&E-MSS |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project developed methods to quantify prostate cancer progression from digital pathology slides. Using the structural features from topological data analysis, this work developed computational techniques to automatically detect the prevalence and severity of cancer in digital images using machine learning approaches. In particular, this work extracted a topological descriptor, called a persistence diagram, on regions of digital slides. Using expert annotations of the Gleason score (a medical measure of the severity) this project showed how discrete versions of these diagrams can be clustered by score or used to train convolutional neural networks to apply a severity labeling. The clustering was able to reveal a continuum of subpatterns within Gleason scores. In addition to the primary contributions, this work has also made advances in topological data analysis, computational geometry, data visualization, and applied machine learning. These advances include new representations that describe an image's connectedness, including a concise representation that is only as large as a single, integer number. In addition, the project has developed computational approaches to simulate prostate cancer nuclei location for various types of prostate cancer tissue. Moreover, new methods for storing images were developed, including a technique that allows data reduction that can vary precision and resolution throughout a digital image.
Last Modified: 01/26/2022
Modified by: Carola Wenk
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