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Referenceless stratification of parenchymal lung abnormalities.

Author information

  • 1Mayo Clinic College of Medicine, Rochester, MN, USA. raghunath.sushravya@mayo.edu

Abstract

This paper introduces computational tools that could enable personalized, predictive, preemptive, and participatory (P4) Pulmonary medicine. We demonstrate approaches to (a) stratify lungs from different subjects based on the spatial distribution of parenchymal abnormality and (b) visualize the stratification through glyphs that convey both the grouping efficacy and an iconic overview of an individual's lung wellness. Affinity propagation based on regional parenchymal abnormalities is used in the referenceless stratification. Abnormalities are computed using supervised classification based on Earth Mover's distance. Twenty natural clusters were detected from 372 CT lung scans. The computed clusters correlated with clinical consensus of 9 disease types. The quality of inter- and intra-cluster stratification as assessed by ANOSIM R was 0.887 +/- 0.18 (pval < 0.0005). The proposed tools could serve as biomarkers to objectively diagnose pathology, track progression and assess pharmacologic response within and across patients.

PMID:
22003703
[PubMed - indexed for MEDLINE]
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