Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma

A method for comparing intra-tumoural radioactivity uptake heterogeneity in preclinical positron emission tomography studies

Background

Non-uniformity influences the interpretation of nuclear medicine based images and consequently their use in treatment planning and monitoring. However, no standardised method for evaluating and ranking heterogeneity exists. Here, we have developed a general algorithm that provides a ranking and a visualisation of the heterogeneity in small animal positron emission tomography (PET) images.

Methods

The code of the algorithm was written using the Matrix Laboratory software (MATLAB). Parameters known to influence the heterogeneity (distances between deviating peaks, gradients and size compensations) were incorporated into the algorithm. All data matrices were mathematically constructed in the same format with the aim of maintaining overview and control. Histograms visualising the spread and frequency of contributions to the heterogeneity were also generated. The construction of the algorithm was tested using mathematically generated matrices and by varying post-processing parameters. It was subsequently applied in comparisons of radiotracer uptake in preclinical images in human head and neck carcinoma and endothelial and ovarian carcinoma xenografts.

Results

Using the developed algorithm, entire tissue volumes could be assessed and gradients could be handled in an indirect manner. Similar-sized volumes could be compared without modifying the algorithm. Analyses of the distribution of different tracers gave results that were generally in accordance with single plane preclinical images, indicating that it could appropriately handle comparisons of targeting vs. non-targeting tracers and also for different target levels. Altering the reconstruction algorithm, pixel size, tumour ROI volumes and lower cut-off limits affected the calculated heterogeneity factors in expected directions but did not reverse conclusions about which tumour was more or less heterogeneous.

Conclusions

The algorithm constructed is an objective and potentially user-friendly tool for one-to-one comparisons of heterogeneity in whole similar-sized tumour volumes in PET imaging.

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

Non-Small Cell Lung Cancer Treated with Erlotinib: Heterogeneity of (18)F-FDG Uptake at PET-Association with Treatment Response and Prognosis

PURPOSE

To determine if first-order and high-order textural features on fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET) images of non-small cell lung cancer (NSCLC) (a) at baseline, (b) at 6 weeks, or (c) the percentage change between baseline and 6 weeks can predict response or survival in patients treated with erlotinib.

MATERIALS AND METHODS

Institutional review board approval was obtained for post hoc analysis of data from a prospective single-center study for which informed consent was obtained. The study included 47 patients with NSCLC who underwent (18)F-FDG PET/computed tomography (CT) at baseline (n = 47) and 6 weeks (n = 40) after commencing treatment with erlotinib. First-order and high-order primary tumor texture features reflecting image heterogeneity, standardized uptake values, metabolic tumor volume, and total lesion glycolysis were measured for all (18)F-FDG PET studies. Response to erlotinib was assessed by using the Response Evaluation Criteria in Solid Tumors (RECIST) on CT images obtained at 12 weeks (n = 32). Associations between PET parameters, overall survival (OS), and RECIST-based treatment response were tested by Cox and logistic regression analyses, respectively.

RESULTS

Median OS was 14.1 months. According to CT RECIST at 12 weeks, there were 21 nonresponders and 11 responders. Response to erlotinib was associated with reduced heterogeneity (first-order standard deviation, P = .01; entropy, P = .001; uniformity, P = .001). At multivariable analysis, high-order contrast at 6 weeks (P = .002) and percentage change in first-order entropy (P = .03) were independently associated with survival. Percentage change in first-order entropy was also independently associated with treatment response (P = .01).

CONCLUSION

Response to erlotinib is associated with reduced heterogeneity at (18)F-FDG PET. Changes in first-order entropy are independently associated with OS and treatment response.

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

OBJECTIVES

Measuring tumour heterogeneity by textural analysis in (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) provides predictive and prognostic information but technical aspects of image processing can influence parameter measurements. We therefore tested effects of image smoothing, segmentation and quantisation on the precision of heterogeneity measurements.

METHODS

Sixty-four (18)F-FDG PET/CT images of oesophageal cancer were processed using different Gaussian smoothing levels (2.0, 2.5, 3.0, 3.5, 4.0 mm), maximum standardised uptake value (SUVmax) segmentation thresholds (45%, 50%, 55%, 60%) and quantisation (8, 16, 32, 64, 128 bin widths). Heterogeneity parameters included grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRL), neighbourhood grey-tone difference matrix (NGTDM), grey-level size zone matrix (GLSZM) and fractal analysis methods. The concordance correlation coefficient (CCC) for the three processing variables was calculated for each heterogeneity parameter.

RESULTS

Most parameters showed poor agreement between different bin widths (CCC median 0.08, range 0.004-0.99). Segmentation and smoothing showed smaller effects on precision (segmentation: CCC median 0.82, range 0.33-0.97; smoothing: CCC median 0.99, range 0.58-0.99).

CONCLUSIONS

Smoothing and segmentation have only a small effect on the precision of heterogeneity measurements in (18)F-FDG PET data. However, quantisation often has larger effects, highlighting a need for further evaluation and standardisation of parameters for multicentre studies.

KEY POINTS

• Heterogeneity measurement precision in (18) F-FDG PET is influenced by image processing methods. • Quantisation shows large effects on precision of heterogeneity parameters in (18) F-FDG PET/CT. • Smoothing and segmentation show comparatively smaller effects on precision of heterogeneity parameters.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R_D, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R_B, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R_B was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

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

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-of-flight 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 (SUVpeak), maximum SUV, and mean SUV (SUVmean).

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

SUVpeak, SUVmean, 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 SUVpeak and SUVmean 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 SUVpeak and SUVmean had a COV between 5% and 10%. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUVpeak and SUVmean, 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.

Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer?

PURPOSE

The aim of this retrospective study was to determine if some features of baseline (18)F-FDG PET images, including volume and heterogeneity, reflect clinical, histological or immunohistochemical characteristics in patients with stage II or III breast cancer (BC).

METHODS

Included in the present retrospective analysis were 171 prospectively recruited patients with stage II/III BC treated consecutively at Saint-Louis hospital. Primary tumour volumes were semiautomatically delineated on pretreatment (18)F-FDG PET images. The parameters extracted included SUVmax, SUVmean, metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and heterogeneity quantified using the area under the curve of the cumulative histogram and textural features. Associations between clinical/histopathological characteristics and (18)F-FDG PET features were assessed using one-way analysis of variance. Areas under the ROC curves (AUC) were used to quantify the discriminative power of the features significantly associated with clinical/histopathological characteristics.

RESULTS

T3 tumours (>5 cm) exhibited higher textural heterogeneity in (18)F-FDG uptake than T2 tumours (AUC <0.75), whereas there were no significant differences in SUVmax and SUVmean. Invasive ductal carcinoma showed higher SUVmax values than invasive lobular carcinoma (p = 0.008) but MATV, TLG and textural features were not discriminative. Grade 3 tumours had higher FDG uptake (AUC 0.779 for SUVmax and 0.694 for TLG), and exhibited slightly higher regional heterogeneity (AUC 0.624). Hormone receptor-negative tumours had higher SUV values than oestrogen receptor-positive (ER-positive) and progesterone receptor-positive tumours, while heterogeneity patterns showed only low-level variation according to hormone receptor expression. HER-2 status was not associated with any of the image features. Finally, SUVmax, SUVmean and TLG significantly differed among the three phenotype subgroups (HER2-positive, triple-negative and ER-positive/HER2-negative BCs), but MATV and heterogeneity metrics were not discriminative.

CONCLUSION

SUV parameters, MATV and textural features showed limited correlations with clinical and histopathological features. The three main BC subgroups differed in terms of SUVs and TLG but not in terms of MATV and heterogeneity. None of the PET-derived metrics offered high discriminative power.

A Segmentation Algorithm for Quantitative Analysis of Heterogeneous Tumors of the Cervix With ¹⁸F-FDG PET/CT

As positron-emission tomography (PET) images have low spatial resolution and much noise, accurate image segmentation is one of the most challenging issues in tumor quantification. Tumors of the uterine cervix present a particular challenge because of urine activity in the adjacent bladder. Here, we propose and validate an automatic segmentation method adapted to cervical tumors. Our proposed methodology combined the gradient field information of both the filtered PET image and the level set function into a level set framework by constructing a new evolution equation. Furthermore, we also constructed a new hyperimage to recognize a rough tumor region using the fuzzy c-means algorithm according to the tissue specificity as defined by both PET (uptake) and computed tomography (attenuation) to provide the initial zero level set, which could make the segmentation process fully automatic. The proposed method was verified based on simulation and clinical studies. For simulation studies, seven different phantoms, representing tumors with homogenous/heterogeneous-low/high uptake patterns and different volumes, were simulated with five different noise levels. Twenty-seven cervical cancer patients at different stages were enrolled for clinical evaluation of the method. Dice similarity coefficients (DSC) and Hausdorff distance (HD) were used to evaluate the accuracy of the segmentation method, while a Bland-Altman analysis of the mean standardized uptake value (SUVmean) and metabolic tumor volume (MTV) was used to evaluate the accuracy of the quantification. Using this method, the DSCs and HDs of the homogenous and heterogeneous phantoms under clinical noise level were 93.39 ±1.09% and 6.02 ±1.09 mm, 93.59 ±1.63% and 8.92 ±2.57 mm, respectively. The DSCs and HDs in patients measured 91.80 ±2.46% and 7.79 ±2.18 mm. Through Bland-Altman analysis, the SUVmean and the MTV using our method showed high correlation with the clinical gold standard. The results of both simulation and clinical studies demonstrated the accuracy, effectiveness, and robustness of the proposed method. Further assessment of the quantitative indices indicates the feasibility of this algorithm in accurate quantitative analysis of cervical tumors in clinical practice.

False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review

Purpose

A number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We report a systematic review of the type-I error inflation in such studies and review the evidence regarding associations between patient outcome and texture features derived from positron emission tomography (PET) or computed tomography (CT) images.

Methods

For study identification PubMed and Scopus were searched (1/2000–9/2013) using combinations of the keywords texture, prognostic, predictive and cancer. Studies were divided into three categories according to the sources of the type-I error inflation and the use or not of an independent validation dataset. For each study, the true type-I error probability and the adjusted level of significance were estimated using the optimum cut-off approach correction, and the Benjamini-Hochberg method. To demonstrate explicitly the variable selection bias in these studies, we re-analyzed data from one of the published studies, but using 100 random variables substituted for the original image-derived indices. The significance of the random variables as potential predictors of outcome was examined using the analysis methods used in the identified studies.

Results

Fifteen studies were identified. After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34–99%) was estimated with the majority of published results not reaching statistical significance. Only 3/15 studies used a validation dataset. For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to ROC and multiple hypothesis testing analysis.

Conclusions

We found insufficient evidence to support a relationship between PET or CT texture features and patient survival. Further fit for purpose validation of these image-derived biomarkers should be supported by appropriate biological and statistical evidence before their association with patient outcome is investigated in prospective studies.

State-Of-The-Art and Recent Advances in Quantification for Therapeutic Follow-Up in Oncology Using PET

¹⁸F-fluoro-2-deoxyglucose (¹⁸F-FDG) positron emission tomography (PET) is an important tool in oncology. Its use has greatly progressed from initial diagnosis to staging and patient monitoring. The information derived from ¹⁸F-FDG-PET allowed the development of a wide range of PET quantitative analysis techniques ranging from simple semi-quantitative methods like the standardized uptake value (SUV) to “high order metrics” that require a segmentation step and additional image processing. In this review, these methods are discussed, focusing particularly on the available methodologies that can be used in clinical trials as well as their current applications in international consensus for PET interpretation in lymphoma and solid tumors.

Heterogeneity in [18F]fluorodeoxyglucose positron emission tomography/computed tomography of non-small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging

To investigate the relationships between tumor heterogeneity, assessed by texture analysis of [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET) images, metabolic parameters, and pathologic staging in patients with non-small cell lung carcinoma (NSCLC). A retrospective analysis of 38 patients with histologically confirmed NSCLC who underwent staging FDG-PET/computed tomography was performed. Tumor images were segmented using a standardized uptake value (SUV) cutoff of 2.5. Five textural features, related to the heterogeneity of gray-level distribution, were computed (energy, entropy, contrast, homogeneity, and correlation). Additionally, metabolic parameters such as SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as pathologic staging, histologic subtype, and tumor diameter, were obtained. Finally, a correlation analysis was carried out. Of 38 tumors, 63.2% were epidermoid and 36.8% were adenocarcinomas. The mean ± standard deviation values of MTV and TLG were 30.47 ± 25.17 mL and 197.81 ± 251.11 g, respectively. There was a positive relationship of all metabolic parameters (SUVmax, SUVmean, MTV, and TLG) with entropy, correlation, and homogeneity and a negative relationship with energy and contrast. The T component of the pathologic TNM staging (pT) was similarly correlated with these textural parameters. Textural features associated with tumor heterogeneity were shown to be related to global metabolic parameters and pathologic staging.

Prognostic value of positron emission tomography-assessed tumor heterogeneity in patients with thyroid cancer undergoing treatment with radiopeptide therapy

INTRODUCTION

Peptide receptor radionuclide therapy (PRRT) is a treatment option for both iodine-refractory differentiated and advanced medullary thyroid cancer (TC). It requires over-expression of somatostatin receptor subtype II (SSTR) that can be non-invasively assessed by positron emission tomography (PET). Assessment of tumor heterogeneity is increasingly used as a tool for prognostication prediction. We investigated the potential of SSTR-PET to assess intraindividual tumor heterogeneity and thereby treatment response prior to PRRT.

METHODS

12 patients with progressive radioiodine-refractory differentiated (1 papillary, 1 oxyphilic, 2 oncocytic, 4 follicular) or medullary (n=4) TC were enrolled. SSTR-PET was performed at baseline. Conventional PET parameters and heterogeneity parameters were analyzed regarding their potential to predict progression-free (PFS, mean, 221 days) and overall survival (OS, mean, 450 days). Parameters of a subgroup of lesions (n=23) were also correlated with morphological response according to modified RECIST criteria.

RESULTS

In patient-based analysis, all conventional parameters failed to predict PFS. Several textural parameters showed a significant capability to assess PFS. Thereby, "Grey level non uniformity" had the highest area under the curve (AUC, 0.93) in Receiver operating characteristics analysis followed by "Contrast" (AUC, 0.89). In lesion-based analysis, only "Entropy" revealed potential to evaluate disease progression. OS could not be assessed by any parameter investigated.

CONCLUSIONS

Tumor heterogeneity seems to be a predictor of response to PRRT in patients with iodine-refractory differentiated/advanced medullary thyroid cancer and outperforms conventional PET parameters like standardized uptake value. In a "theranostic" approach, assessment of textural parameters may help in selecting patients who might benefit from PRRT.

18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort

Intratumoral uptake heterogeneity in (18)F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural feature analysis is a promising method for its quantification. An open issue associated with textural features for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown to be a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types.

METHODS

A single database of 555 pretreatment (18)F-FDG PET images (breast, cervix, esophageal, head and neck, and lung cancer tumors) was assembled. Four robust and reproducible textural feature-derived parameters were considered. The issues associated with the calculation of textural features using co-occurrence matrices (such as the quantization and spatial directionality relationships) were also investigated. The relationship between these features and MATV, as well as among the features themselves, was investigated using Spearman rank coefficients for different volume ranges. The complementary prognostic value of MATV and textural features was assessed through multivariate Cox analysis in the esophageal and non-small cell lung cancer (NSCLC) cohorts.

RESULTS

A large range of MATVs was included in the population considered (3-415 cm(3); mean, 35; median, 19; SD, 50). The correlation between MATV and textural features varied greatly depending on the MATVs, with reduced correlation for increasing volumes. These findings were reproducible across the different cancer types. The quantization and calculation methods both had an impact on the correlation. Volume and heterogeneity were independent prognostic factors (P = 0.0053 and 0.0093, respectively) along with stage (P = 0.002) in non-small cell lung cancer, but in the esophageal tumors, volume and heterogeneity had less complementary value because of smaller overall volumes.

CONCLUSION

Our results suggest that heterogeneity quantification and volume may provide valuable complementary information for volumes above 10 cm(3), although the complementary information increases substantially with larger volumes.

Evaluating heterogeneity of primary tumor (18)F-FDG uptake in breast cancer with a dedicated breast PET (MAMMI): a feasibility study based on correlation with PET/CT

PURPOSE

The aim of the study was to evaluate the heterogeneity of primary tumor F-fluorodeoxyglucose ((18)F-FDG) uptake in breast cancer patients using a dedicated breast PET.

PATIENTS AND METHODS

A positron emission tomography/computed tomography (PET/CT) of the thorax was performed 60 min after administration of 180-240 MBq of (18)F-FDG in patients with breast cancer. Subsequently, 110 min after injection, a scan was taken with a dedicated high-resolution breast PET [MAMmography with Molecular Imaging (MAMMI)]. Both procedures were performed with the patients in the prone position. Four-point scores were used to compare the intensity (0: none; 1: mild; 2: moderate; 3: high) and heterogeneity (0: none; 1: mild; 2: moderate; 3: high) of (18)F-FDG uptake between PET/CT and MAMMI images.

RESULTS

Thirty-five patients in whom the primary tumor was visualized on both scans were included in this analysis. The mean primary tumor size was 35.1 mm (range 10-108 mm). The mean intensity score was similar on both devices (2.4 for PET/CT and 2.3 for MAMMI; P=0.439), but the mean heterogeneity score on MAMMI images was significantly higher (PET/CT 1.9 vs. MAMMI 2.3; P=0.005). MAMMI showed a higher heterogeneity score in 11 (31%) of 35 patients, especially in tumors with moderate or high intensity. Significantly higher heterogeneity scores on both PET/CT and MAMMI were seen in large tumors (P=0.005 and 0.014, respectively) and in tumors with high intensity scores (P=0.012 and P<0.001, respectively).

CONCLUSION

Heterogeneous tumor (18)F-FDG uptake in breast cancer is frequently observed, particularly in large tumors with intense (18)F-FDG uptake. It is more often seen on MAMMI PET than on conventional PET/CT. Although the observed heterogeneity should be proven histopathologically, this finding offers a rationale for (18)F-FDG-guided biopsies.

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

PURPOSE

The question as to whether the regional textural features extracted from PET images predict prognosis in oropharyngeal squamous cell carcinoma (OPSCC) remains open. In this study, we investigated the prognostic impact of regional heterogeneity in patients with T3/T4 OPSCC.

METHODS

We retrospectively reviewed the records of 88 patients with T3 or T4 OPSCC who had completed primary therapy. Progression-free survival (PFS) and disease-specific survival (DSS) were the main outcome measures. In an exploratory analysis, a standardized uptake value of 2.5 (SUV 2.5) was taken as the cut-off value for the detection of tumour boundaries. A fixed threshold at 42 % of the maximum SUV (SUVmax 42 %) and an adaptive threshold method were then used for validation. Regional textural features were extracted from pretreatment (18)F-FDG PET/CT images using the grey-level run length encoding method and grey-level size zone matrix. The prognostic significance of PET textural features was examined using receiver operating characteristic (ROC) curves and Cox regression analysis.

RESULTS

Zone-size nonuniformity (ZSNU) was identified as an independent predictor of PFS and DSS. Its prognostic impact was confirmed using both the SUVmax 42 % and the adaptive threshold segmentation methods. Based on (1) total lesion glycolysis, (2) uniformity (a local scale texture parameter), and (3) ZSNU, we devised a prognostic stratification system that allowed the identification of four distinct risk groups. The model combining the three prognostic parameters showed a higher predictive value than each variable alone.

CONCLUSION

ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review

BACKGROUND

Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice.

METHODOLOGY

The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared.

PRINCIPAL FINDINGS

Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description.

CONCLUSIONS

In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.

Review of clinical practice utility of positron emission tomography with 18F-fluorodeoxyglucose in assessing tumour response to therapy

Positron emission tomography, most commonly with 18F-fluorodeoxyglucose, is being used for evaluation of tumour response to therapy. Limitations of this method are associated with (1) fluorodeoxyglucose pharmacokinetic properties, (2) the detection system, (3) discrepancies between metabolic and anatomic images, and (4) acquisition standardization. Response to therapy may be evaluated with qualitative (Deauville score), semiquantitative (standardised uptake value), and quantitative methods (European Organization for Research and Treatment of Cancer; Positron Emission Tomography Response Criteria in Solid Tumours). Methods under evaluation include metabolic tumour volume, total lesion glycolysis, and heterogeneity of fluorodeoxyglucose uptake. The development of positron emission tomography scanners that have larger fields of view may facilitate tumour assessment based on kinetic modelling. Increased clinical use of these methods will depend on the development and validation of intuitive and simple analytic tools.

Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer

METHODS

Thirty patients with proven colorectal cancer prospectively underwent integrated 18F-FDG PET/DCE-CT to assess the metabolic-flow phenotype. Both CT blood flow parametric maps and PET images were analyzed. Correlations between PET heterogeneity and perfusion CT were assessed by Spearman's rank correlation analysis.

RESULTS

Blood flow visualization provided by DCE-CT images was significantly correlated with 18F-FDG PET metabolically active tumor volume as well as with uptake heterogeneity for patients with stage III/IV tumors (|ρ|:0.66 to 0.78; p-value<0.02).

CONCLUSION

The positive correlation found with tumor blood flow indicates that intra-tumor heterogeneity of 18F-FDG PET accumulation reflects to some extent tracer distribution and consequently indicates that 18F-FDG PET intra-tumor heterogeneity may be associated with physiological processes such as tumor vascularization.

Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer

The goal of this study was to compare visual assessment of intratumor (18)F-FDG PET uptake distribution with a textural-features (TF) automated quantification and to establish their respective prognostic value in non-small cell lung cancer (NSCLC).

METHODS

The study retrospectively included 102 consecutive patients. Only primary tumors were considered. Intratumor heterogeneity was visually scored (3-level scale [Hvisu]) by 2 nuclear medicine physicians. Tumor volumes were automatically delineated, and heterogeneity was quantified with TF. Mean and maximum standardized uptake value were also included. Visual interobserver agreement and correlations with quantitative assessment were evaluated using the κ test and Spearman rank (ρ) coefficient, respectively. Association with overall survival and recurrence-free survival was investigated using the Kaplan-Meier method and Cox regression models.

RESULTS

Moderate correlations (0.4 < ρ < 0.6) between TF parameters and Hvisu were observed. Interobserver agreement for Hvisu was moderate (κ = 0.64, discrepancies in 27% of the cases). High standardized uptake value, large metabolic volumes, and high heterogeneity according to TF were associated with poorer overall survival and recurrence-free survival and remained an independent prognostic factor of overall survival with respect to clinical variables.

CONCLUSION

Quantification of (18)F-FDG uptake heterogeneity in NSCLC through TF was correlated with visual assessment by experts. However, TF also constitutes an objective heterogeneity quantification, with reduced interobserver variability, and independent prognostic value potentially useful for patient stratification and management.

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.

Effects of reusing baseline volumes of interest by applying (non-)rigid image registration on positron emission tomography response assessments

Objectives

Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classifications based on various types of image registration with those based on (semi)-automatic tumour delineation.

Methods

Baseline (n = 13), early (n = 12) and late (n = 9) response (after one and three cycles of treatment, respectively) whole body [¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) scans were acquired in subjects with advanced gastrointestinal malignancies. Lesions were identified for early and late response scans. VOI were drawn independently on all scans using an adaptive 50% threshold method (A50). In addition, various types of (non-)rigid image registration were applied to PET and/or CT images, after which baseline VOI were projected onto response scans. Response was classified using PET Response Criteria in Solid Tumors for maximum standardized uptake value (SUV_max), average SUV (SUV_mean), peak SUV (SUV_peak), metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and the area under a cumulative SUV-volume histogram curve (AUC).

Results

Non-rigid PET-based registration and non-rigid CT-based registration followed by non-rigid PET-based registration (CTPET) did not show differences in response classifications compared to A50 for SUV_max and SUV_peak,, however, differences were observed for MATV, SUV_mean, TLG and AUC. For the latter, these registrations demonstrated a poorer performance for small lung lesions (<2.8 ml), whereas A50 showed a poorer performance when another area with high uptake was close to the target lesion. All methods were affected by lesions with very heterogeneous tracer uptake.

Conclusions

Non-rigid PET- and CTPET-based image registrations may be used to classify response based on SUV_max and SUV_peak. For other quantitative measures future studies should assess which method is valid for response evaluations by correlating with survival data.

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.