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Integrating PET and CT Information to Improve Diagnostic Accuracy for Lung Nodules: A Semiautomatic Computer-Aided Method

¹ Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Chicago, Illinois; and ² Department of Radiology, General Hospital of PLA, Beijing, China

Correspondence: For correspondence or reprints contact: Qiang Li, PhD, Department of Radiology, MC-2026, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637. E-mail: qiangli{at}uchicago.edu

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). Conclusion: 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.

Key Words: PET/CT • lung cancer • pulmonary nodule • computer-aided diagnosis

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