Tumor Texture Analysis in ¹⁸F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis

Abstract

Texture indices are of growing interest for tumor characterization in 18 F-FDG PET. Yet, on the basis of results published in the literature so far, it is unclear which indices should be used, what they represent, and how they relate to conventional indices such as standardized uptake values (SUVs), metabolic volume (MV), and total lesion glycolysis (TLG). We investigated in detail 31 texture indices, 5 firstorder statistics (histogram indices) derived from the gray-level histogram of the tumor region, and their relationship with SUV, MV, and TLG in 3 different tumor types. Methods: Three patient groups corresponding to 3 cancer types at baseline were studied independently: patients with metastatic colorectal cancer (72 lesions), nonsmall cell lung cancer (24 lesions), and breast cancer (54 lesions). Thirty-one texture indices were studied in addition to SUVs, MV, and TLG, and 5 indices extracted from histogram analysis were also investigated. The relationships between indices were studied as well as the robustness of the various texture indices with respect to the parameters involved in the calculation method (sampling schemes and tumor volume of interest). Results: Regardless of the patient group, many indices were highly correlated (Pearson correlation coefficient jrj ≥ 0.80), making it desirable to focus on only a few uncorrelated indices. Three histogram indices were highly correlated with SUVs (jrj ≥ 0.84). Four texture indices were highly correlated with MV, and none was highly correlated with SUVs (jrj ≤ 0.55). The resampling formula used to calculate texture indices had a substantial impact, and resampling using at least 32 discrete values should be used for texture indices calculation. The texture indices change as a function of the segmentation method was higher than that of peak and maximum SUVs but less than mean SUV for 5 texture indices and was larger than that of MV for 14 texture indices and for the 5 histogram indices. All these results were extremely consistent across the 3 tumor types and explained many of the observations reported in the literature so far. Conclusion: None of the histogram indices and only 17 of 31 texture indices were robust with respect to the tumor-segmentation method. An appropriate resampling formula with at least 32 gray levels should be used to avoid introducing a misleading relationship between texture indices and SUV. Some texture indices are highly correlated or strongly correlate with MV whatever the tumor type.Such correlation should be accounted for when interpreting the usefulness of texture indices for tumor characterization, which might call for systematic multivariate analyses.

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

“…Patients with lesions in the lung who underwent wholebody 18 F-FDG PET/CT at the National University Hospital, Singapore, from February 2013 to May 2014 were retrospectively enrolled while excluding those patients with a lesion volume less than 5 cm 3 (14). Only the lesions located in the lung were included for analysis.…”

“…In addition, a manual adjustment to exclude neighboring nodes or metastases was made for each VOI if necessary. As in the study by Orlhac et al (14), only lesions with a volume greater than 5 cm 3 were included in the subsequent analysis, because the impact of the partial-volume effect and textural measurements may not be relevant in small regions. Thus, the impact of the partial-volume effect can be neglected.…”

“…To reduce image noise, resampling the original PET values is necessary. Orlhac et al (14) suggested that at least 32 gray levels should be used in the computation of TF. A bin size of 32, 64, and 128 was investigated in this study.…”

“…The 3-dimensional GLCM used in this study was extended from the original 2-dimensional GLCM by summing voxel triplet probabilities in a 2-dimensional image. Thirteen different directions and a spatial distance of 1 voxel displacement were selected (14). The average TF over the 13 directions was used in this study, and the same 13 directions were used to calculate GLRLM.…”

“…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).…”