Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology

dc.contributor.authorLawson, Peter
dc.contributor.authorSholl, Andrew B.
dc.contributor.authorBrown, J. Quincy
dc.contributor.authorFasy, Brittany T.
dc.contributor.authorWenk, Carola
dc.description.abstractThe current system for evaluating prostate cancer architecture is the Gleason grading system which divides the morphology of cancer into five distinct architectural patterns, labeled 1 to 5 in increasing levels of cancer aggressiveness, and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability amongst pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We evaluate prostate cancer for architectural subtypes using techniques from topological data analysis applied to prostate cancer glandular architecture. In this work we demonstrate the use of persistent homology to capture architectural features independently of Gleason patterns. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns 3,4 and 5, and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through a ranked persistence vector. Our results indicate the ability of persistent homology to cluster prostate cancer histopathology images into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. The capability of these topological representations to segregate prostate cancer by architecture makes them an ideal candidate for use as inputs to future machine learning approaches with the intent of augmenting traditional approaches with topological features for improved diagnosis and prognosis.en_US
dc.description.sponsorshipNational Institutes of Health/National Science Foundation grants NIH/NSF DMS-1664848, 1557750 , NIH/NSF DMS-1664858, and 1557716; National Cancer Institute of the National Institutes of Health NIH NCI R33CA196457en_US
dc.identifier.citationLawson, Peter, Andrew B. Sholl, J. Quincy Brown, Brittany Terese Fasy, and Carola Wenk. "Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology." Scientific Reports 9 (February 2019). DOI:10.1038/s41598-018-36798-y.en_US
dc.rightsCC BY: This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and useen_US
dc.titlePersistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histologyen_US
mus.citation.journaltitleScientific Reportsen_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.categoryHealth & Medical Sciencesen_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentComputer Science.en_US
mus.relation.universityMontana State University - Bozemanen_US


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