ArticleNuclear medicine and molecular imaging2014
Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result.
Article in Nuclear medicine and molecular imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 58 citations in OpenAlex.
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- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Computed tomography-based radiomics approach in pancreatic tumors characterization.La Radiologia medica · 2021Review
- Use of radiomics based onEuropean journal of nuclear medicine and molecular imaging · 2021Article
- Machine learning based on clinico-biological features integratedEuropean journal of nuclear medicine and molecular imaging · 2021Article
- CT based radiomic approach on first line pembrolizumab in lung cancer.Scientific reports · 2021Article
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Histologic subtype classification of non-small cell lung cancer using PET/CT images.European journal of nuclear medicine and molecular imaging · 2021Article
- Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer.Cancer management and research · 2021Article
- Influencing Factors and Prognostic Value ofInternational journal of general medicine · 2021Article
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7 authors at 3 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTexture 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).
methodsDiagnostic (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.
resultsFifteen 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).
conclusionEach 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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