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Ha S, Choi H, Cheon GJ, et al. Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result. Nucl Med Mol Imaging. 2014;48(4):278-86doi: 10.1007/s13139-014-0283-3.
Ha, S., Choi, H., Cheon, G. J., Kang, K. W., Chung, J. K., Kim, E. E., & Lee, D. S. (2014). Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result. Nuclear medicine and molecular imaging, 48(4), 278-86. https://doi.org/10.1007/s13139-014-0283-3
Ha, Seunggyun, et al. "Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result." Nuclear medicine and molecular imaging vol. 48,4 (2014): 278-86. doi: https://doi.org/10.1007/s13139-014-0283-3
Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE, Lee DS. Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result. Nucl Med Mol Imaging. 2014 Dec;48(4):278-86. doi: 10.1007/s13139-014-0283-3. Epub 2014 Jun 11. PMID: 26396632; PMCID: PMC4571663.
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Nucl Med Mol Imaging. 2014 Dec;48(4):278-86. doi: 10.1007/s13139-014-0283-3. Epub 2014 Jun 11.

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

Nuclear medicine and molecular imaging

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

Affiliations

  1. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Department 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.
  2. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea ; Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu Seoul, 110-744 Korea.
  3. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea.
  4. Department 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 ; Department of Radiological Science, University of California at Irvine, Irvine, CA USA.

PMID: 26396632 PMCID: PMC4571663 DOI: 10.1007/s13139-014-0283-3

Abstract

PURPOSE: Texture analysis on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-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 (18)F-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.

Keywords: Carcinoma; Cluster analysis; F-18 Fluorodeoxyglucose; Non-small cell lung; Positron emission tomography; Texture analysis

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