PULMONARY NODULE CLASSIFICATION: SIZE DISTRIBUTION ISSUES

Automated nodule classification systems determine a model based on features extracted from documented databases of nodules. These databases cover a large size range and have an unequal distribution of malignant and benign nodules, leading to a high correlation between malignancy and size. For two re...

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Bibliographic Details
Main Authors: Jirapatnakul, A.C., Reeves, A.P., Apanasovich, T.V., Biancardi, A.M., Yankelevitz, D.F., Henschke, C.I.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical engineering
Cancer
Computed tomography
Educational institutions
Feature extraction
Industrial engineering
Lesions
Operations research
Sensitivity and specificity
Spatial databases
Online Access:Request full text
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Summary:Automated nodule classification systems determine a model based on features extracted from documented databases of nodules. These databases cover a large size range and have an unequal distribution of malignant and benign nodules, leading to a high correlation between malignancy and size. For two recent studies in the literature, much of the reported performance of the system may be derived from size based on analysis of their size distributions. We performed experiments to determine the effect of unequal size distribution on a nodule classification system's performance. Preliminary results indicate that the performance across the entire dataset (a sensitivity/specificity of 0.85/0.80) does not generalize to a subset of nodules (0.50/0.80), but performance can be improved by specifically training on that subset (0.60/0.80). Additional testing with larger datasets needs to be performed, but results reported in this area are overly optimistic.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2007.357085