Computerized comprehensive data analysis of lung imaging database consortium (LIDC).
OBJECTIVE
Lung Image Database Consortium (LIDC) is the largest public CT image database of lung nodules. In this study, the authors present a comprehensive and the most updated analysis of this dynamically growing database under the help of a computerized tool, aiming to assist researchers to optimally use this database for lung cancer related investigations.


METHODS
The authors developed a computer scheme to automatically match the nodule outlines marked manually by radiologists on CT images. A large variety of characteristics regarding the annotated nodules in the database including volume, spiculation level, elongation, interobserver variability, as well as the intersection of delineated nodule voxels and overlapping ratio between the same nodules marked by different radiologists are automatically calculated and summarized. The scheme was applied to analyze all 157 examinations with complete annotation data currently available in LIDC dataset.


RESULTS
The scheme summarizes the statistical distributions of the abovementioned geometric and diagnosis features. Among the 391 nodules, (1) 365 (93.35%) have principal axis length < or =20 mm; (2) 120, 75, 76, and 120 were marked by one, two, three, and four radiologists, respectively; and (3) 122 (32.48%) have the maximum volume overlapping ratios -80% for the delineations of two radiologists, while 198 (50.64%) have the maximum volume overlapping ratios <60%. The results also showed that 72.89% of the nodules were assessed with malignancy score between 2 and 4, and only 7.93% of these nodules were considered as severely malignant (malignancy > or =4).


CONCLUSIONS
This study demonstrates that LIDC contains examinations covering a diverse distribution of nodule characteristics and it can be a useful resource to assess the performance of the nodule detection and/or segmentation schemes.
Tan
Jun
J
Department of Radiology, Imaging Research Division, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA. tanj@upmc.edu
Pu
Jiantao
J
Zheng
Bin
B
Wang
Xingwei
X
Leader
Joseph K
JK
D002114:Calcinosis / Q000150:complications; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000150:complications / Q000000981:diagnostic imaging*; D015588:Observer Variation; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed