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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Published on: December 19, 2020

Quantitative stratification of diffuse parenchymal lung diseases.

Sushravya Raghunath¹, Srinivasan Rajagopalan¹, Ronald A Karwoski¹

¹Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, United States of America.

|March 29, 2014

Summary

This study introduces a new quantitative method to classify diffuse parenchymal lung diseases (DPLDs) based on imaging. This approach aids in personalized patient management and predicting outcomes for DPLD.

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Area of Science:

Pulmonary Medicine
Radiology
Biomarker Discovery

Background:

Diffuse parenchymal lung diseases (DPLDs) present diagnostic and prognostic challenges due to variable progression.
Current diagnostic tools lack objective biomarkers for patient-specific prediction and phenotype identification.
Individualized management and staging for DPLD patients are critically needed.

Purpose of the Study:

To develop a quantitative stratification paradigm for identifying DPLD patient subsets with distinct radiologic patterns.
To correlate these self-organized disease groups with clinically relevant endpoints.
To establish a reproducible technique for improved DPLD diagnosis, staging, and prognostication.

Main Methods:

Unsupervised learning techniques applied to radiologic data for patient stratification.
Quantitative analysis of parenchymal changes to identify distinct DPLD phenotypes.
Correlation analysis between identified radiologic patterns and clinical surrogate endpoints.

Main Results:

Successfully identified distinct subsets of DPLD patients using a quantitative stratification paradigm.
Demonstrated significant correlations between these radiologic-based groups and established clinical endpoints.
The technique offers a reproducible and consistent approach to DPLD classification.

Conclusions:

The proposed quantitative stratification paradigm offers a novel approach to DPLD classification.
This method has the potential to transform diagnostic staging, clinical management, and prognostication for DPLD patients.
It may also facilitate objective disease monitoring, treatment response assessment, and mortality prediction.