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- W157213454 abstract "454 Objectives The purpose of this study was to evaluate the usefulness of texture analysis on 18F-FDG PET to distinguish tumor types of non-small cell lung carcinoma. Methods Thirty patients with non-small cell lung carcinoma who underwent diagnostic 18F-FDG PET/CT scans were included in this study. ROI was manually drawn on a representative axial PET image with the highest SUV value of lesion. After gray-level normalization of each ROI with image intensity limitation in the range of mean ± 3 standard deviation, texture features were extracted from histogram, co-occurrence matrix, run-length matrix, gradient, autoregressive model, and wavelet transform. Texture feature selection was based on three automated methods; 1) Fisher coefficient, 2) both minimization of classification error probabilty and average correlation (POE+ACC), and 3) the Mutual information coefficient (MI). Overall 25 texture features were included in analysis. Clustering and classification were performed with linear discriminant analysis (LDA) using the nearest neighbor method (k=1) after feature standardization. Tumor types were confirmed by histopathology of biopsy and surgery. Results There were 17 adenocarcinomas (ADC) and 13 squamous cell carcinomas (SqCC). LDA presented the most discriminating feature (MDF) value of each tumor. The result of PET texture analysis using LDA showed accurate discrimination between those two types of tumors, ADC (-0.096 ± 0.020) and SqCC (0.125 ± 0.024). Linear separability was 0.96 and Fisher coefficient was 99.4. There was no misclassified tumor on 18F-FDG PET images by LDA. Conclusions Automated texture analysis on 18F-FDG PET can accurately distinguish tumor type of non-small cell lung cancer." @default.
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- W157213454 date "2014-05-01" @default.
- W157213454 modified "2023-09-28" @default.
- W157213454 title "Discrimination of tumor types of non-small cell lung carcinoma on 18F-FDG PET using texture analysis: A preliminary result" @default.
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