Automated Radiation Targeting in Head-and-Neck Cancer Using Region-Based Texture Analysis of PET and CT Images

Yu, Haiming; Caldwell, Curtis; Mah, Kandice; Poon, Ian; Balogh, J; MacKenzie, Robert; Khaouam, Nader; Tirona, R.

doi:10.1016/j.ijrobp.2009.04.043

Cited by 130 publications

(106 citation statements)

References 14 publications

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“…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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confidence: 99%

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

et al. 2015

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“…Beyond the relatively simple measurements of the level of tumor uptake or size, there is increasing recognition that measurement of the spatial heterogeneity of 18 F-FDG PET image characteristics can give predictive information on baseline, pretherapy, imaging in several solid tumors including sarcoma (15), head and neck and cervix tumors (16)(17)(18), and esophageal carcinoma (19). In NSCLC, a potential heterogeneity parameter using SUV volume histograms has been described but not clinically tested (20).…”

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confidence: 99%

“…Several different textural parameters have been reported in 18 F-FDG PET, but parameters derived from neighborhood gray-tone (intensity) difference matrices (NGTDM), describing features such as coarseness, contrast, busyness, and complexity (22), have shown the ability to differentiate primary and nodal tumor from normal tissue in head and neck cancer (17) and allow the delineation of radiotherapy plans (18). In addition, coarseness has been reported as one of the textural parameters that can predict response to chemoradiotherapy in esophageal cancer (19).…”

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Are Pretreatment ¹⁸F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?

et al. 2012

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There is evidence in some solid tumors that textural features of tumoral uptake in 18 F-FDG PET images are associated with response to chemoradiotherapy and survival. We have investigated whether a similar relationship exists in non-small cell lung cancer (NSCLC). Methods: Fifty-three patients (mean age, 65.8 y; 31 men, 22 women) with NSCLC treated with chemoradiotherapy underwent pretreatment 18 F-FDG PET/CT scans. Response was assessed by CT Response Evaluation Criteria in Solid Tumors (RECIST) at 12 wk. Overall survival (OS), progression-free survival (PFS), and local PFS (LPFS) were recorded. Primary tumor texture was measured by the parameters coarseness, contrast, busyness, and complexity. The following parameters were also derived from the PET data: primary tumor standardized uptake values (SUVs) (mean SUV, maximum SUV, and peak SUV), metabolic tumor volume, and total lesion glycolysis. Results: Compared with nonresponders, RECIST responders showed lower coarseness (mean, 0.012 vs. 0.027; P 5 0.004) and higher contrast (mean, 0.11 vs. 0.044; P 5 0.002) and busyness (mean, 0.76 vs. 0.37; P 5 0.027). Neither complexity nor any of the SUV parameters predicted RECIST response. By Kaplan-Meier analysis, OS, PFS, and LPFS were lower in patients with high primary tumor coarseness (median, 21.1 mo vs. not reached, P 5 0.003; 12.6 vs. 25.8 mo, P 5 0.002; and 12.9 vs. 20.5 mo, P 5 0.016, respectively). Tumor coarseness was an independent predictor of OS on multivariable analysis. Contrast and busyness did not show significant associations with OS (P 5 0.075 and 0.059, respectively), but PFS and LPFS were longer in patients with high levels of each (for contrast: median of 20.5 vs. 12.6 mo, P 5 0.015, and median not reached vs. 24 mo, P 5 0.02; and for busyness: median of 20.5 vs. 12.6 mo, P 5 0.01, and median not reached vs. 24 mo, P 5 0.006). Neither complexity nor any of the SUV parameters showed significant associations with the survival parameters. Conclusion: In NSCLC, baseline 18 F-FDG PET scan uptake showing abnormal texture as measured by coarseness, contrast, and busyness is associated with nonresponse to chemoradiotherapy by RECIST and with poorer prognosis. Measurement of tumor metabolic heterogeneity with these parameters may provide indices that can be used to stratify patients in clinical trials for lung cancer chemoradiotherapy.

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“…Some authors have already shown the predictive value of baseline texture parameters and resulting changes in chemotherapy for renal cell carcinoma [24]. Similarly, texture analysis has been used to classify lung tumors [25] and head and neck tumors [26], so these techniques might also be useful in pancreatic cancer. Another direction for future study would be to investigate changes in the correlation of DECT with PET/CT over time, looking specifically at patients with high SUVs who are treated with chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

Pilot Study: Evaluation of Dual-Energy Computed Tomography Measurement Strategies for Positron Emission Tomography Correlation in Pancreatic Adenocarcinoma

Oldan

et al. 2014

J Digit Imaging

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We sought to determine whether dual-energy computed tomography (DECT) measurements correlate with positron emission tomography (PET) standardized uptake values (SUVs) in pancreatic adenocarcinoma, and to determine the optimal DECT imaging variables and modeling strategy to produce the highest correlation with maximum SUV (SUV max ). We reviewed 25 patients with unresectable pancreatic adenocarcinoma seen at Mayo Clinic, Scottsdale, Arizona, who had PET-computed tomography (PET/CT) and enhanced DECT performed the same week between March 25, 2010 and December 9, 2011. For each examination, DECT measurements were taken using one of three methods: (1) average values of three tumor regions of interest (ROIs) (method 1); (2) one ROI in the area of highest subjective DECT enhancement (method 2); and (3) one ROI in the area corresponding to PET SUV max (method 3). There were 133 DECT variables using method 1, and 89 using the other methods. Univariate and multivariate analysis regression models were used to identify important correlations between DECT variables and PET SUV max . Both R 2 and adjusted R 2 were calculated for the multivariate model to compensate for the increased number of predictors. The average SUV max was 5 (range, 1.8-12.0). Multivariate analysis of DECT imaging variables outperformed univariate analysis (r=0.91; R 2 =0.82; adjusted R 2 =0.75 vs r<0.58; adjusted R 2 <0.34). Method 3 had the highest correlation with PET SUV max (R 2 =0.82), followed by method 1 (R 2 =0.79) and method 2 (R 2 =0.57).DECT thus has clinical potential as a surrogate for, or as a complement to, PET in patients with pancreatic adenocarcinoma.

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Automated Radiation Targeting in Head-and-Neck Cancer Using Region-Based Texture Analysis of PET and CT Images

Cited by 130 publications

References 14 publications

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

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

Are Pretreatment ¹⁸F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?

Pilot Study: Evaluation of Dual-Energy Computed Tomography Measurement Strategies for Positron Emission Tomography Correlation in Pancreatic Adenocarcinoma

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Automated Radiation Targeting in Head-and-Neck Cancer Using Region-Based Texture Analysis of PET and CT Images

Cited by 130 publications

References 14 publications

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

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

Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?

Pilot Study: Evaluation of Dual-Energy Computed Tomography Measurement Strategies for Positron Emission Tomography Correlation in Pancreatic Adenocarcinoma

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

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

Are Pretreatment ¹⁸F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?