Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image.
OBJECTIVE
To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image.


METHODS
Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights.


RESULTS
Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P<0.05) between benign and malignant small solitary pulmonary nodules.


CONCLUSIONS
Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.
Wang
Huan
H
Department of Epidemiology and Health Statistics, School of Public Health and Family Medicine, Capital Medical University. Beijing 100069, China.
Guo
Xiu-Hua
XH
Jia
Zhong-Wei
ZW
Li
Hong-Kai
HK
Liang
Zhi-Gang
ZG
Li
Kun-Cheng
KC
He
Qian
Q
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D055361:Multilevel Analysis; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed