The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer

Doumou, Georgia; Siddique, Musib; Tsoumpas, Charalampos; Goh, Vicky; Cook, Gary

doi:10.1007/s00330-015-3681-8

Cited by 71 publications

(70 citation statements)

References 29 publications

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“…Several studies (3)(4)(5)(20)(21)(22)(23)(24) investigated the robustness of texture indices as a function of various parameters (Supplemental Table 4). Given that there is no consensus on the most accurate tumor segmentation method, the robustness of texture indices as a function of the tumor delineation has to be known.…”

Section: Discussionmentioning

confidence: 99%

“…Given that there is no consensus on the most accurate tumor segmentation method, the robustness of texture indices as a function of the tumor delineation has to be known. For example, 4 studies demonstrated that entropy was either slightly (5,23,24) or moderately (20) influenced by the segmentation method. Our results also showed the impact of including edges in the VOI used to calculate the texture indices.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…quantization, [51][52][53] SUV discretization, 54 pre-processing steps 55 and the choice of respiration-averaged CT for attenuation correction. 56,57 The impact of tumour delineation on textural features computation was also assessed in PET images.…”

Section: Positron Emission Tomography Imagesmentioning

confidence: 99%

“…56,57 The impact of tumour delineation on textural features computation was also assessed in PET images. [50][51][52]55 Finally, an attempt of interpreting PET textural indices was carried out in a very recent work, where heterogeneity quantified by texture analysis was investigated on simulated patient data. 58 Here, the authors found that homogeneity, entropy, short-run emphasis and long-run emphasis were sensitive to the presence of uptake heterogeneity, whereas high grey-level zone emphasis and low grey-level zone emphasis were mostly sensitive to the average uptake.…”

Section: Positron Emission Tomography Imagesmentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

120

104

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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“…These results were consistent within the correlation groups previously identified (16), as reported in Supplemental Table 1. In one study (19), it was shown that the 6 texture indices were moderately influenced by the postreconstruction smoothing of PET images, excepting LGZE. These results were obtained not with our absolute resampling method but with relative resampling of discrete intensities between the minimum and maximum for each lesion.…”

Section: Robustness Of Texture Indices With Respect To Voxel Size Andmentioning

confidence: 99%

Multiscale Texture Analysis: From ¹⁸F-FDG PET Images to Histologic Images

Orlhac¹,

Thézé²,

Soussan³

et al. 2016

J Nucl Med

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Characterizing tumor heterogeneity using texture indices derived from PET images has shown promise in predicting treatment response and patient survival in some types of cancer. Yet, the relationship between PET-derived texture indices, precise tracer distribution, and biologic heterogeneity needs to be clarified. We investigated this relationship using PET images, autoradiographic images, and histologic images. Methods: Three mice bearing orthotopically implanted mammary tumors derived from transgenic MMTV-PyMT mice were scanned with 18 F-FDG PET/CT. The tumors were then sliced, and the slices were imaged with autoradiography and stained with hematoxylin and eosin. Six texture indices derived from the PET images, autoradiographic images, and histologic images were compared for their ability to capture heterogeneity on different scales. Results: The PET-derived indices correlated significantly with the autoradiography-derived ones (R 5 0.57-0.85), but the values differed in magnitude. The histology-derived indices correlated poorly with the autoradiography-and PET-derived ones (R 5 0.06-0.54). All indices were slightly to moderately influenced by the difference in voxel size and spatial resolution in the autoradiographic images. The autoradiography-derived indices differed significantly (P , 0.05) between regions with a high density of cells and regions with a low density and between regions with different spatial arrangements of cells. Conclusion: Heterogeneity derived in vivo from PET images accurately reflects the heterogeneity of tracer uptake derived ex vivo from autoradiographic images. Various tumor-cell densities and spatial cell distributions seen on histologic images can be distinguished using texture indices derived from autoradiographic images despite the difference in voxel size and spatial resolution. Yet, tumor texture derived from PET images only coarsely reflects the spatial distribution and density of tumor cells. A current challenge in oncology is to offer each patient a personalized treatment that accounts, at best, for the characteristics of the tumor or tumors. To achieve this goal, the specific histologic or genetic features of the tumor, including intratumoral heterogeneity, have to be determined before therapy is prescribed (1). This determination is usually based only on biopsy analysis, yet one study (2) reported that 63%-69% of all mutations were not detectable within a single tumor sample. Indeed, intratumoral heterogeneity reflects different mutations in cell subpopulations distributed in different regions of the tumor, some of which may be far from the biopsy site. PET provides molecular information about the whole tumor and may therefore be a relevant option to complete the characterization of the tumor. Indeed, PET imaging is noninvasive, can be repeated during the course of therapy, and allows for a comprehensive assessment of each tumor site. Using different tracers, PET can even yield information on the glucose metabolism of cells ( 18 F-FDG), tumor hypoxia ( 18 F-fluoroazomyc...

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The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer

Cited by 71 publications

References 29 publications

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

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

Texture analysis of medical images for radiotherapy applications

Multiscale Texture Analysis: From ¹⁸F-FDG PET Images to Histologic Images

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The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer

Cited by 71 publications

References 29 publications

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

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

Texture analysis of medical images for radiotherapy applications

Multiscale Texture Analysis: From 18F-FDG PET Images to Histologic Images

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Product

Resources

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Multiscale Texture Analysis: From ¹⁸F-FDG PET Images to Histologic Images