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J Nucl Med. 2017 Mar;58(3):387-392. doi: 10.2967/jnumed.116.181859. Epub 2016 Oct 6.

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

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Imagerie Moléculaire In Vivo, IMIV, CEA, INSERM, CNRS, Université Paris-Sud, Université Paris Saclay, CEA-SHFJ, Orsay, France; and
Imagerie Moléculaire In Vivo, IMIV, CEA, INSERM, CNRS, Université Paris-Sud, Université Paris Saclay, CEA-SHFJ, Orsay, France; and.
Department of Nuclear Medicine, AP-HP, Avicenne Hospital, Bobigny, France.


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 18F-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.


PET; oncology; texture analysis; tumor heterogeneity

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