Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions

Abstract. In order to elucidate the value of positron emission tomography (PET)/computed tomography (CT) in the clinical diagnosis and treatment of osseous and soft tissue malignancies, two authors independently searched the PubMed, Medline, Elsevier, Embase and Cochrane Library databases for literature published between January 2003 and February 2016, using the key words 'PET/CT', 'positron emission tomography/computed tomography', 'osseous sarcoma', 'bone tumor', 'soft tissue sarcoma' and 'neoadjuvant', to identify prospective and retrospective studies on the applicability of PET/CT on the clinical diagnosis of bone and soft tissue lesions, and evaluation of their response to neoadjuvant therapies. Data were independently extracted by the two authors and any disagreements were resolved by a third author when necessary. Extracted data were analyzed by Meta-Disc 1.6 software. As a result, 16 trials with a total of 883 patients and 2,214 lesions were included in the present study. The overall diagnostic accuracy of PET/CT exhibited a sensitivity and specificity of 0.90 (0.86-0.92) and 0.89 (0.85-0.92), respectively, and the effect of neoadjuvant therapy was assessed with a sensitivity and specificity of 0.79 (0.30-0.93) and 0.79 (0.69-0.89), respectively. Thus, it may be concluded from the present study that PET/CT is a reliable imaging method to be applied in the diagnosis and treatment of osseous and soft tissue malignancies.

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“…Of the 1,310 articles screened, 16 (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) were selected for the final analysis (Fig. 1).…”

Section: Resultsmentioning

confidence: 99%

Positron emission tomography/computed tomography for osseous and soft tissue sarcomas: A systematic review of the literature and meta-analysis

Muheremu

Amudong

et al. 2017

Molecular and Clinical Oncology

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“…Spheres 1 (mean of distribution [m] 5 8, SD 5 2) and 2 (m 5 12, SD 5 2.5) had homogeneous uptake of different levels. Spheres 3-10 included internal macro metabolic heterogeneities, either as cold spheric subregions coarsely modeling hypometabolic areas (such as necrotic zones in a tumor on 18 F-FDG PET) or as hot spheric subregions that could correspond to clones of the most 18 F-FDG-avid cells on 18 F-FDG PET. All images were smoothed using a gaussian filter to model a 7-mm PET image resolution.…”

Section: Numeric Modelmentioning

confidence: 99%

“…To allow for texture index value interpretation, we investigated the changes in value of 6 texture indices computed from simulated and real patient data. Methods: Ten sphere models mimicking different activity distribution patterns and the 18 F-FDG PET images from 54 patients with breast cancer were used. For each volume of interest, 6 texture indices were measured.…”

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confidence: 99%

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

et al. 2016

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The use of texture indices to characterize tumor heterogeneity from PET images is being increasingly investigated in retrospective studies, yet the interpretation of PET-derived texture index values has not been thoroughly reported. Furthermore, the calculation of texture indices lacks a standardized methodology, making it difficult to compare published results. To allow for texture index value interpretation, we investigated the changes in value of 6 texture indices computed from simulated and real patient data. Methods: Ten sphere models mimicking different activity distribution patterns and the 18 F-FDG PET images from 54 patients with breast cancer were used. For each volume of interest, 6 texture indices were measured. The values of texture indices and how they changed as a function of the activity distribution were assessed and compared with the visual assessment of tumor heterogeneity. Results: Using the sphere models and real tumors, we identified 2 sets of texture indices reflecting different types of uptake heterogeneity. Set 1 included homogeneity, entropy, short-run emphasis, and long-run emphasis, all of which were sensitive to the presence of uptake heterogeneity but did not distinguish between hyper-and hyposignal within an otherwise uniform activity distribution. Set 2 comprised high-gray-level-zone emphasis and low-gray-level-zone emphasis, which were mostly sensitive to the average uptake rather than to the uptake local heterogeneity. Four of 6 texture indices significantly differed between homogeneous and heterogeneous lesions as defined by 2 nuclear medicine physicians (P , 0.05). All texture index values were sensitive to voxel size (variations up to 85.8% for the most homogeneous sphere models) and edge effects (variations up to 29.1%). Conclusion: Unlike a previous report, our study found that variations in texture indices were intuitive in the sphere models and real tumors: the most homogeneous uptake distribution exhibited the highest homogeneity and lowest entropy. Two families of texture index reflecting different types of uptake patterns were identified. Variability in texture index values as a function of voxel size and inclusion of tumor edges was demonstrated, calling for a standardized calculation methodology. This study provides guidance for nuclear medicine physicians in interpreting texture indices in future studies and clinical practice. Ani ncreasing number of studies focus on the characterization of tumor heterogeneity based on texture analysis from CT, MRI, ultrasound images, and, since 2009, PET images (1). The calculation of tumor heterogeneity indices from medical images offers the benefits of being noninvasive, accounting for the whole tumor, and being repeatable during treatment. These indices, if proven, could be used to reliably identify the existence of subpopulations of cells with distinct genomic alterations and could guide the choice of treatment, especially for targeted therapeutics (2). In PET, several retrospective studies suggest that texture indices reflect ...

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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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Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions

Abstract: The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.

Cited by 77 publications

References 21 publications

Positron emission tomography/computed tomography for osseous and soft tissue sarcomas: A systematic review of the literature and meta-analysis

Positron emission tomography/computed tomography for osseous and soft tissue sarcomas: A systematic review of the literature and meta-analysis

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

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

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Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions

Abstract: The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.

Cited by 77 publications

References 21 publications

Positron emission tomography/computed tomography for osseous and soft tissue sarcomas: A systematic review of the literature and meta-analysis

Positron emission tomography/computed tomography for osseous and soft tissue sarcomas: A systematic review of the literature and meta-analysis

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

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

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