Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program

Samuel G. Armato, Arunabha S. Roy, Heber MacMahon, Feng Li, Kunio Doi, Shusuke Sone, Michael B. Altman

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Rationale and Objectives. The purpose of this study was to evaluate the performance of a fully automated lung nodule detection method in a large database of low-dose computed tomography (CT) scans from a lung cancer screening program. Because nodules demonstrate a spectrum of radiologic appearances, the performance of the automated method was evaluated on the basis of nodule malignancy status, size, subtlety, and radiographic opacity. Materials and Methods. A database of 393 thick-section (10 mm) low-dose CT scans was collected. Automated lung nodule detection proceeds in two phases: gray-level thresholding for the initial identification of nodule candidates, followed by the application of a rule-based classifier and linear discriminant analysis to distinguish between candidates that correspond to actual lung nodules and candidates that correspond to non-nodules. Free-response receiver operating characteristic analysis was used to evaluate the performance of the method based on a jackknife training/testing approach. Results. An overall nodule detection sensitivity of 70% (330 of 470) was attained with an average of 1.6 false-positive detections per section. At the same false-positive rate, 83% (57 of 69) of the malignant lung nodules in the database were detected. When the method was trained specifically for malignant nodules, a sensitivity of 80% (55 of 69) was attained with 0.85 false-positives per section. Conclusion. We have evaluated an automated lung nodule detection method with a large number of low-dose CT scans from a lung cancer screening program. An overall sensitivity of 80% for malignant nodules was achieved with 0.85 false-positive detections per section. Such a computerized lung nodule detection method is expected to become an important part of CT-based lung cancer screening programs.

Original languageEnglish
Pages (from-to)337-346
Number of pages10
JournalAcademic radiology
Volume12
Issue number3
DOIs
StatePublished - Mar 2005

Keywords

  • Cancer screening
  • Computed tomography (CT)
  • Computer-aided diagnosis (CAD)
  • Image processing
  • Lung CT
  • Lung neoplasms
  • Lung nodule

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