Improving the Diagnostic Specificity of CT for Early Detection of Lung Cancer: 4D CT-Based Pulmonary Nodule Elastometry

Abstract

In this study we propose to develop and validate pulmonary nodule elastometry imaging, a method complementary to CT that has the potential to increase the specificity of screening for early detection of lung cancer. We propose to address the need for thegreater specificity in lung cancer screening by characterizing a mechanical property of pulmonary lesions, specifically pulmonary nodule (PN)elasticity, in addition to standard anatomic features. We hypothesize that malignant and benign PN can be distinguished more specifically by different elasticities determined from 4D CT images. The specific aims of the study were the development of pulmonary nodule elastometryalgorithms based on deformable image processing of 4D CT images and their validation in an animal model and in a retrospective review ofover 200 4D CT scans from patients with small malignant pulmonary nodules previously treated with radiation in our department. We have successfully developed algorithms, and in a first validation we have demonstrated proof of principles that elastometry can distinguish malignant PNs from surrounding lung tissue (a manuscript is in preparation). The validation in animal models and the retrospective analysis of the human data is ongoing.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2015
Accession Number
AD1016369

Entities

People

  • Billy W Loo
  • Peter G Maxim

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Cancer
  • Cancer Screening
  • Department Of Defense
  • Detection
  • Detectors
  • Elastic Properties
  • Image Processing
  • Image Registration
  • Lung Cancer
  • Mechanical Properties
  • Neoplasms
  • Oncology
  • Radiation Oncology
  • Statistical Analysis
  • Tomography
  • X-Ray Computed Tomography

Fields of Study

  • Medicine

Readers

  • Medical Imaging.
  • Neural Network Machine Learning.