期刊
ACADEMIC RADIOLOGY
卷 21, 期 1, 页码 41-51出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.acra.2013.09.021
关键词
Density feature; low-dose computed tomography; lung tumor; computer-aided diagnosis
资金
- Taiwan Department of Health, China Medical University Hospital Cancer Research Center of Excellence [DOH102-TD-C-111-005]
- National Science Council, Executive Yuan, Taiwan [NSC 98-2221-E-468-015]
Rationale and Objectives: Using low-dose computed tomography (LDCT), small and heterogeneous lung tumors are detected in screening. The criteria for assessing detected tumors are crucial for determining follow-up or resection strategies. The purpose of this study was to investigate the capacity of density features in differentiating lung tumors. Materials and Methods: From July 2008 to December 2011, 48 surgically confirmed tumors (29 malignancies, comprising 17 cases of adenocarcinoma and 12 cases of adenocarcinoma in situ [AdIs], and 19 benignancies, comprising 11 cases of atypical adenomatous hyperplasia [AAH] and eight cases of benign non-AAH) in 38 patients were retrospectively evaluated, indicating that the positive predictive value (PPV) of physicians is 60.4% (29/48). Three types of density features, tumor disappearance rate (TDR), mean, and entropy, were obtained from the CT values of detected tumors. Results: Entropy is capable of differentiating malignancy from benignancy but is limited in differentiating AdIs from benign non-AAH. The combination of entropy and TDR is effective for predicting malignancy with an accuracy of 87.5% (42/48) and a PPV of 89.7% (26/29), improving the PPV of physicians by 29.3%. The combination of entropy and mean adequately clarifies the four pathology groups with an accuracy of 72.9% (35/48). For tumors with a mean below -400 Hounsfield units, the criterion of an entropy larger than 5.4 might be appropriate for diagnosing malignancy. For others, the pathology is either benign non-AAH or adenocarcinoma; adenocarcinoma has a higher entropy than benign non-AAH, with the exception of tuberculoma. Conclusions: Combining density features enables differentiating heterogeneous lung tumors in LDCT.
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