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Proceedings Paper

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset
Author(s): Matthew C. Hancock; Jerry F. Magnan
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Paper Abstract

To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capabilities of statistical learning methods for classifying nodule malignancy, utilizing the Lung Image Database Consortium (LIDC) dataset, and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that is achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)% which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included, along with the accuracy to 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically-derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features, and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Paper Details

Date Published: 3 March 2017
PDF: 12 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013425 (3 March 2017); doi: 10.1117/12.2254446
Show Author Affiliations
Matthew C. Hancock, Florida State Univ. (United States)
Jerry F. Magnan, Florida State Univ. (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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