Textural Features of Pretreatment <sup>18</sup>F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

Cheng, Nai Ming; Fang, Yiqun; Chang, Joseph T.; Huang, Chung Guei; Tsan, Din-Li; Ng, Shu Hang; Lin, Chien Yu; Liao, Chun Ta; Yen, Tzu Chen

doi:10.2967/jnumed.112.119289

Cited by 136 publications

(135 citation statements)

References 39 publications

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“…In the first study (Cheng et al, 2013), including 70 oropharyngeal cancers, TLG and texture uniformity were correlated with OS (HR 5.85 and 0.46 respectively). A 3-point risk scale for DFS and OS was proposed, according to the presence of a uniformity ≤ 0.138 and a TLG > 122.9 g. One point was given for each factor.…”

Section: Texture and Shape Analysismentioning

confidence: 99%

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Castelli

Bari

Depeursinge

et al. 2016

Critical Reviews in Oncology/Hematology

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International audience18 F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) allows to quantify the metabolic activity of a tumor (glycolysis) and has become a reference tool in oncology for the staging, restaging, radiotherapy planning and monitoring response in many cancers. Quantitative analyses have been introduced in order to overcome some of the limits of the visual methods, allowing an easier and more objective comparison of the inter- and intra-patients variations. The aims of this review were to report available evidences on the clinical value of quantitative PET/CT parameters in HNC. Forty-five studies, for a total of 2928 patients, were analyzed. Most of the data available dealt with the intensity of the metabolism, calculated from the Standard Uptake Value (SUV). Metabolic Tumor Volume (MTV) was well correlated with overall survival and disease free survival, with a higher predictive value than the maximum SUV. Spatial distribution of metabolism and textural analyses seems promising

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Section: Texture and Shape Analysismentioning

confidence: 99%

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Castelli

Bari

Depeursinge

et al. 2016

Critical Reviews in Oncology/Hematology

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“…The coarseness from NGTDM can be used not only to predict response of chemoradiotherapy for non-small cell lung cancer (10) and esophageal cancer (18) but also to differentiate normal tissues from head and neck tumors and lymph nodes (8). However, it was found to be sensitive to reconstruction settings with a small variation (5% , COV # 10%) over the 4 default reconstruction settings, intermediate variation (10% , COV # 20%) for both impact of iteration number and FWHM, and large variation (COV .…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, there are increasing numbers of publications related to the application of TFs in PET tumor imaging, mostly with 18 F-FDG (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). These previous studies mainly focused on the predictive and prognostic value (5-10,13-25) of TF and also the potential use in radiotherapy planning (20,21).…”

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confidence: 99%

Impact of Image Reconstruction Settings on Texture Features in ¹⁸F-FDG PET

et al. 2015

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Evaluation of tumor heterogeneity based on texture parameters has recently attracted much interest in the PET imaging community. However, the impact of reconstruction settings on texture parameters is unclear, especially relating to time-offlight and point-spread function modeling. Their effects on 55 texture features (TFs) and 6 features based on first-order statistics (FOS) were investigated. Standardized uptake value (SUV) measures were also evaluated as peak SUV (SUV peak ), maximum SUV, and mean SUV (SUV mean ). Methods: This study retrospectively recruited 20 patients with lesions in the lung who underwent whole-body 18 F-FDG PET/CT. The coefficient of variation (COV) of each feature across different reconstructions was calculated. Results: SUV peak , SUV mean , 18 TFs, and 1 FOS were the most robust (COV # 5%) whereas skewness, cluster shade, and zone percentage were the least robust (COV . 20%) with respect to reconstruction algorithms using default settings. Heterogeneity parameters had different sensitivities to iteration number. Twenty-four parameters including SUV peak and SUV mean exhibited variation with a COV less than 5%; 28 parameters, including maximum SUV, showed variation with a COV in the range of 5%-10%. In addition, skewness, cluster shade, and zone percentage were the most sensitive to iteration number. In terms of sensitivity to full width at half maximum (FWHM), 15 TFs and 1 FOS had the best performance with a COV less than 5%, whereas SUV peak and SUV mean had a COV between 5% and 10%. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUV peak and SUV mean , having a COV less than 10%. Conclusion: Different image features have different sensitivities to reconstruction settings. Iteration number and FWHM of the gaussian filter have a similar impact on the image features. Grid size has a larger impact on the features than iteration number and FWHM. The features that exhibited large variations such as skewness in FOS, cluster shade, and zone percentage should be used with caution. The entropy in FOS, difference entropy, inverse difference normalized, inverse difference moment normalized, low gray-level run emphasis, high gray-level run emphasis, and low gray-level zone emphasis are the most robust features.

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“…Radiomic features were also reported as a significant tool to stage cervical cancer based on tumor heterogeneity information 28. Along the same line, Cheng et al29 reported that uniformity, a GLCM feature, might serve as an independent prognostic predictor as well as risk stratification descriptor for patients with oropharyngeal squamous cell carcinoma. Another study investigated the physiologic reproducibility of textural features by characterizing the tumor F18‐FDG uptake heterogeneity in the PET scans of 41 esophageal cancer patients 30…”

Section: Introductionmentioning

confidence: 95%

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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Textural Features of Pretreatment ¹⁸F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

Cited by 136 publications

References 39 publications

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Impact of Image Reconstruction Settings on Texture Features in ¹⁸F-FDG PET

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

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Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

Cited by 136 publications

References 39 publications

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET

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

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Textural Features of Pretreatment ¹⁸F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

Impact of Image Reconstruction Settings on Texture Features in ¹⁸F-FDG PET