Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features.
OBJECTIVE
To investigate the utility of a computer-aided diagnosis (CAD) in the task of differentiating malignant nodules from benign nodules based on quantitative features extracted from volumetric thin section CT image data acquired before and after the injection of contrast media.


METHODS
35 volumetric CT datasets of solitary pulmonary nodules (SPN) with proven diagnoses (19 malignant/16 benign) were contoured by a thoracic radiologist. All studies had at least a baseline series obtained without contrast media and at least one series following an intravenous contrast injection at 45, 90, 180, and 360 seconds. Two separate contours were created for each nodule: one including only the solid portion and another including the ground-glass component, if any, of the nodule. For each contour 31 features were calculated that measured the attenuation, shape, and enhancement of the nodule due to the injection of contrast. These features were input into a feature selection step and three different classifiers to determine if the diagnosis could be predicted from the resulting feature vector. In addition, observer input was introduced to two of the classifiers as an a priori probability of malignancy and the resulting performance was compared. Training and testing was conducted in a resubstitution and leave-one-out fashion and performance was evaluated using ROC analysis.


RESULTS
In a leave-one-out testing methodology, the classifiers achieved areas under the ROC curves AZ that ranged from 0.69 to 0.92. A classifier based on logistic regression performed the best with an AZ of 0.92 while a classifier based on quadratic discriminant analysis performed the poorest (AZ, 0.69). The AZ increased when using a priori observer input in most cases reaching a maximum of 0.95.


CONCLUSIONS
Based on this initial work with a limited number of nodules in our dataset, it appears that CAD using volumetric and contrast-enhanced data has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients. Further studies with an increased number of patients are required to validate these results.
Shah
Sumit K
SK
Department of Radiology, David Geffen School of Medicine at The University of California Los Angeles, Thoracic Imaging Research Group, 924 Westwood Blvd. Suite 650, Box 957319, Los Angeles, CA 90095-7319, USA. shah@ucla.edu
McNitt-Gray
Michael F
MF
Rogers
Sarah R
SR
Goldin
Jonathan G
JG
Suh
Robert D
RD
Sayre
James W
JW
Petkovska
Iva
I
Kim
Hyun J
HJ
Aberle
Denise R
DR
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003287:Contrast Media; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging*
D003287:Contrast Media