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Nuclear Medicine and Molecular Imaging
Published in: Nuclear Medicine and Molecular Imaging 4/2014

01-12-2014 | Original Article

Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result

Authors: Seunggyun Ha, Hongyoon Choi, Gi Jeong Cheon, Keon Wook Kang, June-Key Chung, Euishin Edmund Kim, Dong Soo Lee

Published in: Nuclear Medicine and Molecular Imaging | Issue 4/2014

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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| ≤ 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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Metadata
Title
Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result
Authors
Seunggyun Ha
Hongyoon Choi
Gi Jeong Cheon
Keon Wook Kang
June-Key Chung
Euishin Edmund Kim
Dong Soo Lee
Publication date
01-12-2014
Publisher
Springer Berlin Heidelberg
Published in
Nuclear Medicine and Molecular Imaging / Issue 4/2014
Print ISSN: 1869-3474
Electronic ISSN: 1869-3482
DOI
https://doi.org/10.1007/s13139-014-0283-3

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