Integrating PET and CT information to improve diagnostic accuracy for lung nodules: A semiautomatic computer-aided method.
Our objective was to develop and evaluate 3 semiautomatic computer-aided diagnostic (CAD) schemes for distinguishing between benign and malignant pulmonary nodules by use of features extracted from CT, 18F-FDG PET, and both CT and 18F-FDG PET.


METHODS
We retrospectively collected 92 consecutive cases of pulmonary nodules (<3 cm) in patients who underwent both thoracic CT and whole-body PET/CT. Forty-two of the nodules were malignant and 50 benign, as confirmed by pathologic examination and clinical follow-up. The interval between CT and PET was less than 1 mo. Four clinical parameters, including patient age, sex, smoking status, and history of previous malignancy, were used for the CAD schemes. Sixteen CT features based on size, shape, margin, and internal structure of nodules were independently rated subjectively by 2 chest radiologists. Four PET features were viewed on a PET/CT workstation. CAD schemes based on clinical parameters together with CT features, PET features, and both CT and PET features were then used to differentiate benign from malignant nodules. Finally, the output from the CAD schemes was evaluated by use of receiver-operating-characteristic analysis.


RESULTS
When we used clinical parameters and CT features as input units (CAD scheme 1), the area under the receiver-operating-characteristic curve (A(z) value) of the CAD scheme was 0.83. When we used clinical parameters and PET features as input units (CAD scheme 2), the A(z) value for the computer output was 0.91. However, when we used all data as input units (CAD scheme 3), the A(z) value for the computer output was 0.95. The performance of CAD scheme 3 was better than that of CAD scheme 1 or 2. A statistically significant difference existed between the A(z) values of CAD schemes 3 and 2 (P = 0.037) and between those of CAD schemes 3 and 1 (P = 0.015).


CONCLUSIONS
Our CAD scheme based on both PET and CT was better able to differentiate benign from malignant pulmonary nodules than were the CAD schemes based on PET alone and CT alone.
Nie
Yongkang
Y
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Chicago, Illinois 60637, USA.
Li
Qiang
Q
Li
Feng
F
Pu
Yonglin
Y
Appelbaum
Daniel
D
Doi
Kunio
K
D000368:Aged; D001331:Automation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000494:pharmacology; D006801:Humans; D008168:Lung / Q000473:pathology; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D049268:Positron-Emission Tomography / Q000379:methods*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*
D019788:Fluorodeoxyglucose F18