Format

Send to

Choose Destination
Sci Rep. 2017 Aug 24;7(1):9370. doi: 10.1038/s41598-017-08764-7.

Diagnostic classification of solitary pulmonary nodules using dual time 18F-FDG PET/CT image texture features in granuloma-endemic regions.

Author information

1
Department of Nuclear Medicine, The First Hospital of China Medical University, No. 155 North Nanjing Street, Heping District, Shenyang City, Liaoning Province, 110001, P. R. China.
2
Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, University of Wisconsin Carbone Cancer Center, 7033 WIMR, 1111 Highland Ave, Madison, WI, 53705, USA.
3
Department of Nuclear Medicine, The First Hospital of China Medical University, No. 155 North Nanjing Street, Heping District, Shenyang City, Liaoning Province, 110001, P. R. China. ymli2001@163.com.
4
Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, University of Wisconsin Carbone Cancer Center, 7033 WIMR, 1111 Highland Ave, Madison, WI, 53705, USA. rjeraj@wisc.edu.

Abstract

Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint is critical for improving the success of surgical resection and increasing 5-year survival rates. 18F-fluorodeoxyglucose (18F-FDG) PET/CT has demonstrated value for SPNs diagnosis with high sensitivity to detect malignant SPNs, but lower specificity in diagnosing malignant SPNs in populations with endemic infectious lung disease. This study aimed to determine whether quantitative heterogeneity derived from various texture features on dual time FDG PET/CT images (DTPI) can differentiate between malignant and benign SPNs in patients from granuloma-endemic regions. Machine learning methods were employed to find optimal discrimination between malignant and benign nodules. Machine learning models trained by texture features on DTPI images achieved significant improvements over standard clinical metrics and visual interpretation for discriminating benign from malignant SPNs, especially by texture features on delayed FDG PET/CT images.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center