Identification of early-stage usual interstitial pneumonia from low-dose chest CT scans using fractional high-density lung distribution

Yiting Xie, Mary Salvatore, Shuang Liu, Artit Jirapatnakul, David F. Yankelevitz, Claudia I. Henschke, Anthony P. Reeves

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A fully-automated computer algorithm has been developed to identify early-stage Usual Interstitial Pneumonia (UIP) using features computed from low-dose CT scans. In each scan, the pre-segmented lung region is divided into N subsections (N = 1, 8, 27, 64) by separating the lung from anterior/posterior, left/right and superior/inferior in 3D space. Each subsection has approximately the same volume. In each subsection, a classic density measurement (fractional high-density volume h) is evaluated to characterize the disease severity in that subsection, resulting in a feature vector of length N for each lung. Features are then combined in two different ways: concatenation (2∗N features) and taking the maximum in each of the two corresponding subsections in the two lungs (N features). The algorithm was evaluated on a dataset consisting of 51 UIP and 56 normal cases, a combined feature vector was computed for each case and an SVM classifier (RBF kernel) was used to classify them into UIP or normal using ten-fold cross validation. A receiver operating characteristic (ROC) area under the curve (AUC) was used for evaluation. The highest AUC of 0.95 was achieved by using concatenated features and an N of 27. Using lung partition (N = 27, 64) with concatenated features had significantly better result over not using partitions (N = 1) (p-value < 0.05). Therefore this equal-volume partition fractional high-density volume method is useful in distinguishing early-stage UIP from normal cases.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
EditorsNicholas A. Petrick, Samuel G. Armato
PublisherSPIE
ISBN (Electronic)9781510607132
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: 13 Feb 201716 Feb 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10134
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2017: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityOrlando
Period13/02/1716/02/17

Keywords

  • Automated computer algorithm
  • CT scans
  • Early-stage UIP
  • Fractional high-density distribution
  • Interstitial lung disease (ILD)
  • Usual Interstitial Pneumonia (UIP)

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