Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology

Rahim, Muhammad Kashif; Kim, Sung Eun; So, Hyeongryul; Kim, Hyung Jun; Cheon, Gi Jeong; Lee, Eun Seong; Kang, Keon Wook; Lee, Dong Hoon

doi:10.1007/s13139-013-0260-2

Cited by 86 publications

(61 citation statements)

References 125 publications

(165 reference statements)

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“…18 F-FDG PET data allow a quantitative evaluation of glucose metabolism of tumor. Although a lot of previous studies have reported the clinical usefulness using SUV max and volumetric parameters, these conventional parameter-based analyses have several limitations [4,5,[11][12][13]. These parameters are classified into histogram-based parameters, so they also have the short-comings of histogram analysis.…”

Section: Patientsmentioning

confidence: 99%

“…Prior studies have shown the relationship between SUV max of 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) scan and histopathologic diagnosis [3]. Likewise, divergence of imaging analysis methods may lead to improved knowledge of tumor characterization [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The gray-level intensity is a representative value of glucose metabolism within the 18 F-FDG PET image, so gross texture of a lesion within the image may represent its histopathologic characteristics. Texture analysis is a mathematical pattern analysis technique and it quantifies the interrelationships of the pixels or voxels via complex and variable mathematical methods [4]. Although the human eye is a good tool for discriminating the texture characteristics of a lesion, it has disadvantages, including being hard to achieve objectification.…”

Section: Introductionmentioning

confidence: 99%

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

Choi

Cheon

et al. 2014

Nucl Med Mol Imaging

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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 histogrambased algorithms, absolute gradient-based algorithms, runlength 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 automateselected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUV max 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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Section: Patientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Choi

Cheon

et al. 2014

Nucl Med Mol Imaging

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“…Several institutes have reported quantitative analysis studies, with a focus on radiomic features, for different imaging modalities such as computed tomography (CT),1, 2, 3 and magnetic resonance imaging (MRI) 4, 5, 6. The investigation of positron emission tomography (PET) radiomics was first reported in 2009 7, 8, 9. In recent years, fluorine‐18‐labeled fluoro‐2‐deoxy‐ d ‐glucose positron emission tomography–computed tomography, 18 F–FDG (PET/CT) has become a major functional imaging technique in oncology due to its ability to evaluate tumor stage and metabolic characteristics with high specificity and sensitivity 10, 11…”

Section: Introductionmentioning

confidence: 99%

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Altazi

Zhang

Fernandez

et al. 2017

J Applied Clin Med Phys

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Site‐specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18Flourine–fluorodeoxyglucose (18F‐FDG) PET images for three parameters: manual versus computer‐aided segmentation methods, gray‐level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board‐certified radiation oncologists manually segmented the metabolic tumor volume (MTV1 and MTV2) for each patient. For comparison, we used a graphical‐based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down‐sampled the tumor volumes into three gray‐levels: 32, 64, and 128 from the original gray‐level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D‐reconstruction algorithms: maximum likelihood‐ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning‐ML‐OSEM (FOREIR), FORE‐filtered back projection (FOREFBP), and 3D‐Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray‐levels of down‐sampled volumes, and PET reconstruction algorithms. The features were extracted using gray‐level co‐occurrence matrices (GLCM), gray‐level size zone matrices (GLSZM), gray‐level run‐length matrices (GLRLM), neighborhood gray‐tone difference matrices (NGTDM), shape‐based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV1‐MTV2, MTV1‐GBSV, MTV2‐GBSV; gray‐levels: 64‐32, 64‐128, and 64‐256; reconstruction algorithms: OSEM‐FORE‐OSEM, OSEM‐FOREFBP, and OSEM‐3DRP). We used false|normald¯false| as a measure of radiomic feature reproducibility level, where any feature scored false|normald¯false| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland–Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High— ±1% ≤ U/LRL ≤ ±30%; Intermediate— ±30% < U/LRL ≤ ±45%; Low— ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic fe...

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“…Thus, previous studies have investigated the associations between these mutations and SUV measures from PET images; however, there have been conflicting findings (18)(19)(20)(21)(22). These inconsistent reports may be due to the fact that simple SUV measures fail to capture the spatial relationships between image voxels, which may be more informative of the biology of these mutations and describe the degree of tumor heterogeneity (23,24). Highly heterogeneous tumors are often NOS 5 not otherwise specified.…”

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confidence: 99%

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

et al. 2016

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PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18 F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18 F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR1] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC 5 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDR Wilcoxon , FDR Noether ) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDR Wilcoxon 5 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR1 vs. EGFR-negative, AUC 5 0.67, FDR Noether 5 0.0032), as well as differentiating between KRAS-positive and EGFR1 (AUC 5 0.65, FDR Noether 5 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC 5 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

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Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology

Cited by 86 publications

References 125 publications

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

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

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

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