Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT.
We investigated the possibility of using computer analysis of high-resolution CT images to radiologically classify the shape of pulmonary nodules. From a total of 107 HRCT images of solid, solitary pulmonary nodules with prior differentiation as benign (n=55) or malignant (n=52), we extracted the desired pulmonary nodules and calculated two quantitative parameters for characterizing nodules: circularity and second central moment. Using discriminant analysis for two thresholds in differentiating malignant from benign states resulted in a sensitivity of 76.9%, a specificity of 80%, a positive predictive value of 78.4%, and a negative predictive value of 78.6%.
Iwano
Shingo
S
Department of Radiology, Nagoya University, School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya 466-8550, Japan. iwano45@med.nagoya-u.ac.jp
Nakamura
Tatsuya
T
Kamioka
Yuko
Y
Ikeda
Mitsuru
M
Ishigaki
Takeo
T
D000465:Algorithms; D001185:Artificial Intelligence; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; 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 / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*