Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET

Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis

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 first-order 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), non-small 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 |r| ≥ 0.80), making it desirable to focus on only a few uncorrelated indices. Three histogram indices were highly correlated with SUVs (|r| ≥ 0.84). Four texture indices were highly correlated with MV, and none was highly correlated with SUVs (|r| ≤ 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.

Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology

Image quantification studies in positron emission tomography/computed tomography (PET/CT) are of immense importance in the diagnosis and follow-up of variety of cancers. In this review we have described the current image quantification methodologies employed in (18)F-fluorodeoxyglucose ((18)F-FDG) PET in major oncological conditions with particular emphasis on tumor heterogeneity studies. We have described various quantitative parameters being used in PET image analysis. The main contemporary methodology is to measure tumor metabolic activity; however, analysis of other image-related parameters is also increasing. Primarily, we have identified the existing role of tumor heterogeneity studies in major cancers using (18)F-FDG PET. We have also described some newer radiopharmaceuticals other than (18)F-FDG being studied/used in the management of these cancers. Tumor heterogeneity studies are being performed in almost all major oncological conditions using (18)F-FDG PET. The role of these studies is very promising in the management of these conditions.

FDG uptake heterogeneity in FIGO IIb cervical carcinoma does not predict pelvic lymph node involvement

TRANSLATIONAL RELEVANCE

Many types of cancer are located and assessed via positron emission tomography (PET) using the 18F-fluorodeoxyglucose (FDG) radiotracer of glucose uptake. There is rapidly increasing interest in exploiting the intra-tumor heterogeneity observed in these FDG-PET images as an indicator of disease outcome. If this image heterogeneity is of genuine prognostic value, then it either correlates to known prognostic factors, such as tumor stage, or it indicates some as yet unknown tumor quality. Therefore, the first step in demonstrating the clinical usefulness of image heterogeneity is to explore the dependence of image heterogeneity metrics upon established prognostic indicators and other clinically interesting factors. If it is shown that image heterogeneity is merely a surrogate for other important tumor properties or variations in patient populations, then the theoretical value of quantified biological heterogeneity may not yet translate into the clinic given current imaging technology.

PURPOSE

We explore the relation between pelvic lymph node status at diagnosis and the visually evident uptake heterogeneity often observed in 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images of cervical carcinomas.

EXPERIMENTAL DESIGN

We retrospectively studied the FDG-PET images of 47 node negative and 38 node positive patients, each having FIGO stage IIb tumors with squamous cell histology. Imaged tumors were segmented using 40% of the maximum tumor uptake as the tumor-defining threshold and then converted into sets of three-dimensional coordinates. We employed the sphericity, extent, Shannon entropy (S) and the accrued deviation from smoothest gradients (ζ) as image heterogeneity metrics. We analyze these metrics within tumor volume strata via: the Kolmogorov-Smirnov test, principal component analysis and contingency tables.

RESULTS

We found no statistically significant difference between the positive and negative lymph node groups for any one metric or plausible combinations thereof. Additionally, we observed that S is strongly dependent upon tumor volume and that ζ moderately correlates with mean FDG uptake.

CONCLUSIONS

FDG uptake heterogeneity did not indicate patients with differing prognoses. Apparent heterogeneity differences between clinical groups may be an artifact arising from either the dependence of some image metrics upon other factors such as tumor volume or upon the underlying variations in the patient populations compared.

The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake

The number of studies in the literature involving quantification of the metabolic heterogeneity seen in (18)F-FDG PET images has increased sharply over recent years. We hypothesized that inclusion of very small regions of interest as unique data points will have deleterious effects on these studies.

METHODS

Using a combination of probability theory and clinical (18)F-FDG PET data, we numerically calculated the curve describing the probability a given tumor volume is large enough to adequately sample the underlying tumor biology assayed via a PET/CT scanner at a planar resolution of 4 mm and transaxial resolution of 4 mm (64 mm(3) voxel size). We then used a computer simulation to isolate the effects of tumor volume on the image local entropy.

RESULTS

We computed the underlying global intensity distribution for 70 cervical cancer tumors ranging from 4 to 248 cm(3)), which were ensemble-averaged over the same intensity scale. From this distribution, we determined that about 700 total voxels (45 cm(3)) are required to give 95% certainty that the global intensity distribution has been sufficiently sampled for common statistical comparisons of individual tumor intensity distributions to be made canonically. We demonstrated that one previously suggested measure of heterogeneity is dependent on tumor volume and that measurement of heterogeneity is about 5 times more sensitive to volume changes for volumes below the proposed minimum than for those above it.

CONCLUSION

Inclusion of tumor volumes below 45 cm(3) can profoundly bias comparisons of intratumoral uptake heterogeneity metrics derived from data from the current generation of whole-body (18)F-FDG PET scanners.

Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability

PURPOSE

Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. "Radiomics" features) are increasingly investigated for treatment monitoring, outcome prediction, or as potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim of our study was therefore to perform an integrated stability analysis of a large number of PET-derived features in non-small cell lung carcinoma (NSCLC), based on both a test-retest and an inter-observer setup.

METHODS

Eleven NSCLC patients were included in the test-retest cohort. Patients underwent repeated PET imaging within a one day interval, before any treatment was delivered. Lesions were delineated by applying a threshold of 50% of the maximum uptake value within the tumor. Twenty-three NSCLC patients were included in the inter-observer cohort. Patients underwent a diagnostic whole body PET-computed tomography (CT). Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Delineation was performed independently by five observers, blinded to each other. Fifteen first order statistics, 39 descriptors of intensity volume histograms, eight geometric features and 44 textural features were extracted. For every feature, test-retest and inter-observer stability was assessed with the intra-class correlation coefficient (ICC) and the coefficient of variability, normalized to mean and range. Similarity between test-retest and inter-observer stability rankings of features was assessed with Spearman's rank correlation coefficient.

RESULTS

Results showed that the majority of assessed features had both a high test-retest (71%) and inter-observer (91%) stability in terms of their ICC. Overall, features more stable in repeated PET imaging were also found to be more robust against inter-observer variability.

CONCLUSION

Results suggest that further research of quantitative imaging features is warranted with respect to more advanced applications of PET imaging as being used for treatment monitoring, outcome prediction or imaging biomarkers.

Quantification of heterogeneity observed in medical images

BACKGROUND

There has been much recent interest in the quantification of visually evident heterogeneity within functional grayscale medical images, such as those obtained via magnetic resonance or positron emission tomography. In the case of images of cancerous tumors, variations in grayscale intensity imply variations in crucial tumor biology. Despite these considerable clinical implications, there is as yet no standardized method for measuring the heterogeneity observed via these imaging modalities.

METHODS

In this work, we motivate and derive a statistical measure of image heterogeneity. This statistic measures the distance-dependent average deviation from the smoothest intensity gradation feasible. We show how this statistic may be used to automatically rank images of in vivo human tumors in order of increasing heterogeneity. We test this method against the current practice of ranking images via expert visual inspection.

RESULTS

We find that this statistic provides a means of heterogeneity quantification beyond that given by other statistics traditionally used for the same purpose. We demonstrate the effect of tumor shape upon our ranking method and find the method applicable to a wide variety of clinically relevant tumor images. We find that the automated heterogeneity rankings agree very closely with those performed visually by experts.

CONCLUSIONS

These results indicate that our automated method may be used reliably to rank, in order of increasing heterogeneity, tumor images whether or not object shape is considered to contribute to that heterogeneity. Automated heterogeneity ranking yields objective results which are more consistent than visual rankings. Reducing variability in image interpretation will enable more researchers to better study potential clinical implications of observed tumor heterogeneity.

Introduction to the analysis of PET data in oncology

Several reviews on specific topics related to positron emission tomography (PET) ranging in complexity from introductory to highly technical have already been published. This introduction to the analysis of PET data was written as a simple guide of the different phases of analysis of a given PET dataset, from acquisition to preprocessing, to the final data analysis. Although sometimes issues specific to PET in neuroimaging will be mentioned for comparison, most of the examples and applications provided will refer to oncology. Due to the limitations of space we couldn't address each issue comprehensively but, rather, we provided a general overview of each topic together with the references that the interested reader should consult. We will assume a familiarity with the basic principles of PET imaging.

Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography

Intra-tumour heterogeneity is a characteristic shared by all cancers. We explored the use of texture variables derived from images of [(18)F]fluorothymidine-positron emission tomography (FLT-PET), thus notionally assessing the heterogeneity of proliferation in individual tumours. Our aims were to study the range of textural feature values across tissue types, verify the repeatability of these image descriptors and further, to explore associations with clinical response to chemotherapy in breast cancer patients. The repeatability of 28 textural descriptors was assessed in patients who had two FLT-PET scans prior to therapy using relative differences and the intra-class correlation coefficient (ICC). We tested associations between features at baseline and clinical response measured in 11 patients after three cycles of chemotherapy, and explored changes in FLT-PET at one week after the start of therapy. A subset of eight features was characterized by low variations at baseline (<±30%) and high repeatability (0.7 ≤ ICC ≤ 1). The intensity distribution profile suggested fewer highly proliferating cells in lesions of non-responders compared to responders at baseline. A true increase in CV and homogeneity was measured in four out of six responders one week after the start of therapy. A number of textural features derived from FLT-PET are altered following chemotherapy in breast cancer, and should be evaluated in larger clinical trials for clinical relevance.

Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?

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 = 0.004) and higher contrast (mean, 0.11 vs. 0.044; P = 0.002) and busyness (mean, 0.76 vs. 0.37; P = 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 = 0.003; 12.6 vs. 25.8 mo, P = 0.002; and 12.9 vs. 20.5 mo, P = 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 = 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 = 0.015, and median not reached vs. 24 mo, P = 0.02; and for busyness: median of 20.5 vs. 12.6 mo, P = 0.01, and median not reached vs. 24 mo, P = 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.

An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results

Background

By providing estimates of tumor glucose metabolism, ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) can potentially characterize the response of breast tumors to treatment. To assess therapy response, serial measurements of FDG-PET parameters (derived from static and/or dynamic images) can be obtained at different time points during the course of treatment. However, most studies track the changes in average parameter values obtained from the whole tumor, thereby discarding all spatial information manifested in tumor heterogeneity. Here, we propose a method whereby serially acquired FDG-PET breast data sets can be spatially co-registered to enable the spatial comparison of parameter maps at the voxel level.

Methods

The goal is to optimally register normal tissues while simultaneously preventing tumor distortion. In order to accomplish this, we constructed a PET support device to enable PET/CT imaging of the breasts of ten patients in the prone position and applied a mutual information-based rigid body registration followed by a non-rigid registration. The non-rigid registration algorithm extended the adaptive bases algorithm (ABA) by incorporating a tumor volume-preserving constraint, which computed the Jacobian determinant over the tumor regions as outlined on the PET/CT images, into the cost function. We tested this approach on ten breast cancer patients undergoing neoadjuvant chemotherapy.

Results

By both qualitative and quantitative evaluation, our constrained algorithm yielded significantly less tumor distortion than the unconstrained algorithm: considering the tumor volume determined from standard uptake value maps, the post-registration median tumor volume changes, and the 25th and 75th quantiles were 3.42% (0%, 13.39%) and 16.93% (9.21%, 49.93%) for the constrained and unconstrained algorithms, respectively (p = 0.002), while the bending energy (a measure of the smoothness of the deformation) was 0.0015 (0.0005, 0.012) and 0.017 (0.005, 0.044), respectively (p = 0.005).

Conclusion

The results indicate that the constrained ABA algorithm can accurately align prone breast FDG-PET images acquired at different time points while keeping the tumor from being substantially compressed or distorted.

Trial registration

NCT00474604

Multiparametric PET/CT in oncology

The standardized uptake value (SUV) and other measurements of tumour uptake of fluorodeoxyglucose (FDG) on positron emission tomography (PET) can potentially be supplemented by additional imaging parameters derived either from the PET images or from the computed tomography (CT) component of integrated PET/CT examinations including tumour size, CT attenuation, texture (reflecting tumour heterogeneity) and blood flow. This article illustrates the emerging benefits of such a multiparametric approach. Example benefits include greater diagnostic accuracy in characterization of adrenal masses achieved by using both the SUV and measured CT attenuation. Tumour size combined with the SUV can potentially improve the prognostic information available from PET/CT in oesophageal and lung cancer. However, greater improvements may be realized through using CT measurements of texture instead of size. Studies in breast and lung cancer suggest that combined PET/CT measurements of glucose metabolism and blood flow provide correlates for tumour proliferation and angiogenesis, respectively. These combined measurements can be utilized to determine vascular-metabolic phenotypes, which vary with tumour type. Uncoupling of blood flow and metabolism suggests a poor prognosis for larger more advanced tumours, high-grade lesions and tumours responding poorly to treatment. Vascular-metabolic imaging also has the potential to subclassify tumour response to treatment. The additional biomarkers described can be readily incorporated in existing FDG-PET examinations thereby improving the ability of PET/CT to depict tumour biology, characterize potentially malignant lesions, and assess prognosis and therapeutic response.

PET imaging: implications for the future of therapy monitoring with PET/CT in oncology

Among the methods based on molecular imaging, the measure of the tracer uptake variation between a baseline and follow-up scan with the SUV and [(18)F]FDG-PET/CT is a very powerful tool for assessing response to treatment in oncology. However, the development of new targeted therapeutics and tissue pharmacokinetic evaluation of existing ones are increasingly requiring therapy monitoring with alternative tracers and indicators. In parallel, the potential predictive and prognostic value of other image-derived parameters, such as tumour volume and textural features, relating to tumoral heterogeneity, has recently emerged from several works.