Towards Automated Quantitative Prostate Cancer Diagnosis

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

9/15/15 - 8/31/17

"QuBBD: Collaborative Research: Towards Automated Quantitative Prostate Cancer Diagnosis", National Science Foundation, NSF-DMS 1557750, $52,931. PI: Carola Wenk, Co-PI Quincy Brown (Biomedical Engineering); collaboration with Brittany Fasy at Montana State University; $99,570 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

The current standard for prostate cancer grading is the Gleason score, which is a subjective rating system based on an analysis of high-level tissue architecture and glandular shape and organization. However, Gleason scoring is variable between pathology reviewers, and may not capture all of the potentially prognostic information contained in glandular growth patterns. In this project, new topological descriptors will be developed that capture architectural features of prostate glands in pathology images. These descriptors can then be used to aid pathologists by providing more quantitative and more reproducible analogs to the traditional Gleason scores, and they may have independent prognostic value. They can also be used to classify slides in order to distinguish between different types of cancerous architectures of glands, compared to the current gold-standard histopathological and molecular characterization. In particular, the aim of this project is to demonstrate effectiveness of using computational methods based on tools from computational geometry and topology to recognize and quantify glandular architectural features. Glandular density will be the first architectural feature quantified in this collaborative work. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.

Collaborators:

• Quincy Brown, Tulane University, Department of Biomedical Engineering
• Brittany Fasy, Montana State University University
• Andrew Sholl, Tulane University, Department of Pathology
• Brian Summa, Tulane University, Department of Computer Science

Students:

• Robin Belton, Montana State University (PhD student, Mathematics)
• Anna Schenfisch, Montana State University (PhD student, Mathematics)
• Eric Berry, Montana State University (PhD student, Mathematics)
• Alan Cleary, Montana State University (PhD student, Computer Science)
• Pete Lawson, Tulane University (PhD student, Bioinnovation)
• Sushovan Majhi, Tulane University (PhD student, Mathematics)
• Christopher Miller, Tulane University (undergraduate, Biomedical Engineering; graduated 2016)
• Sawyer Payne, Montana State University (undergraduate, Computer Science)

Publications:

• "Architectural features in prostate cancer histology: A case study using persistent homology" (P. Lawson, J.Q. Brown, B.T. Fasy, and C. Wenk), in preparation.
• "Topological Descriptors for Quantitative Prostate Cancer Morphology Analysis", poster, (P. Lawson, E. Berry, J.Q. Brown, B.T. Fasy, C. Wenk), Conference on Digital Pathology, part of SPIE Medical Imaging, 2017. Poster award (honorable mention).
• "Towards an Automated Quantitative Diagnosis of Prostate Cancer", poster, (P. Lawson, C. Miller, B. Fasy, Q. Brown, C. Wenk), BD2K All-Hands Grantee Meeting, NIH, November 2015.

Miscellaneous:

• Screenshot of our visualization dashboard, showing a Gleason-3 and a Gleason-4 image.