Characterization of solid pulmonary nodules using three-dimensional features

Artit C. Jirapatnakul, Anthony P. Reeves, Tatiyana V. Apanasovich, Matthew D. Cham, David F. Yankelevitz, Claudia I. Henschke

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

7 Scopus citations


With the development of high-resolution, multirow-detector CT scanners, the prospects for diagnosing and treating lung cancer at an early stage are much improved. However, it is often difficult to determine whether a nodule, especially a small nodule, is malignant from a single CT scan. We developed a computer-aided diagnostic algorithm to distinguish benign from malignant solid nodules based on features that can be extracted from a single CT scan. Our method uses 3D geometric and densitometry moment analysis of a segmented nodule image and surface curvature from a polygonal surface model of the nodule. After excluding features directly related to size, we computed a total of 28 features. Prior to classification, the number of features was reduced through stepwise feature selection. The features are used by two classifiers, k-nearest-neighbors (k-NN) and logistic regression. We used 48 malignant nodules whose status was determined by biopsy or resection, and 55 benign nodules determined to be clinically stable through two years of no change or biopsy. The k-NN classifier achieved a sensitivity of 0.81 with a specificity of 0.76, while the logistic regression classifier achieved a sensitivity of 0.85 and a specificity of 0.80.

Original languageEnglish
Title of host publicationMedical Imaging 2007
Subtitle of host publicationComputer-Aided Diagnosis
EditionPART 2
StatePublished - 2007
Externally publishedYes
EventMedical Imaging 2007: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: 20 Feb 200722 Feb 2007

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
NumberPART 2
ISSN (Print)1605-7422


ConferenceMedical Imaging 2007: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego, CA


  • CT
  • Computer-aided diagnosis
  • Nodule characterization
  • Pulmonary nodules


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