Nucl Med Mol Imaging.
2014 Dec;48(4):278-286. 10.1007/s13139-014-0283-3.
Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result
- Affiliations
-
- 1Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea. larrycheon@gmail.com
- 2Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea.
- 3Cancer Research Institute, Seoul National University, Seoul, Korea.
- 4Department of Radiological Science, University of California at Irvine, Irvine, CA, USA.
- 5Department of NuclearMedicine, Seoul National University College of Medicine, 101 Daehak-RoJongno-Gu Seoul 110-744, Korea.
- KMID: 2313210
- DOI: http://doi.org/10.1007/s13139-014-0283-3
Abstract
- PURPOSE
Texture analysis on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scan is a relatively new imaging analysis tool to evaluate metabolic heterogeneity. We analyzed the difference in textural characteristics between non-small cell lung carcinoma (NSCLC) subtypes, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC).
METHODS
Diagnostic 18F-FDG PET/computed tomography (CT) scans of 30y patients (median age, 67; range, 42-88) with NSCLC (17 ADC and 13 SqCC) were retrospectively analyzed. Regions of interest were manually determined on selected transverse image containing the highest SUV value in tumors. Texture parameters were extracted by histogram-based algorithms, absolute gradient-based algorithms, run-length matrix-based algorithms, co-occurrence matrix-based algorithms, and autoregressive model-based algorithms. Twenty-four out of hundreds of texture features were selected by three algorithms: Fisher coefficient, minimization of both classification error probability and average correlation, and mutual information. Automated clustering of tumors was based on the most discriminating feature calculated by linear discriminant analysis (LDA). Each tumor subtype was determined by histopathologic examination after biopsy and surgery.
RESULTS
Fifteen texture features had significant different values between ADC and SqCC. LDA with 24 automate-selected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUVmax and texture parameters (|r|< or =0.62).
CONCLUSION
Each subtype of NSCLC tumor has different metabolic heterogeneity. The results of this study support the potential of textural parameters on FDG PET as an imaging biomarker.
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