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

Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

PURPOSE

To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis.

METHODS AND MATERIALS

In this institutional review board-approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT (18)F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy.

RESULTS

The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6).

CONCLUSION

Quantitative analysis identified novel (18)F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.

Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis

Purpose To identify quantitative imaging biomarkers at fluorine 18 ((18)F) positron emission tomography (PET) for predicting distant metastasis in patients with early-stage non-small cell lung cancer (NSCLC). Materials and Methods In this institutional review board-approved HIPAA-compliant retrospective study, the pretreatment (18)F fluorodeoxyglucose PET images in 101 patients treated with stereotactic ablative radiation therapy from 2005 to 2013 were analyzed. Data for 70 patients who were treated before 2011 were used for discovery purposes, while data from the remaining 31 patients were used for independent validation. Quantitative PET imaging characteristics including statistical, histogram-related, morphologic, and texture features were analyzed, from which 35 nonredundant and robust features were further evaluated. Cox proportional hazards regression model coupled with the least absolute shrinkage and selection operator was used to predict distant metastasis. Whether histologic type provided complementary value to imaging by combining both in a single prognostic model was also assessed. Results The optimal prognostic model included two image features that allowed quantification of intratumor heterogeneity and peak standardized uptake value. In the independent validation cohort, this model showed a concordance index of 0.71, which was higher than those of the maximum standardized uptake value and tumor volume, with concordance indexes of 0.67 and 0.64, respectively. The prognostic model also allowed separation of groups with low and high risk for developing distant metastasis (hazard ratio, 4.8; P = .0498, log-rank test), which compared favorably with maximum standardized uptake value and tumor volume (hazard ratio, 1.5 and 2.0, respectively; P = .73 and 0.54, log-rank test, respectively). When combined with histologic types, the prognostic power was further improved (hazard ratio, 6.9; P = .0289, log-rank test; and concordance index, 0.80). Conclusion PET imaging characteristics associated with distant metastasis that could potentially help practitioners to tailor appropriate therapy for individual patients with early-stage NSCLC were identified. (©) RSNA, 2016 Online supplemental material is available for this article.

Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study

Studying early response to cancer treatment is significant for patient treatment stratification and follow-up. Although recent advances in positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for evaluation of tumor response, a quantitative objective assessment of treatment-related effects offers localization and quantification of structural and functional changes in the tumor region. Radiomics, the process of computerized extraction of features from radiographic images, is a new strategy for capturing subtle changes in the tumor region that works by quantifying subvisual patterns which might escape human identification. The goal of this study was to demonstrate feasibility for performing radiomics analysis on integrated PET/MRI to characterize early treatment response in metastatic renal cell carcinoma (RCC) undergoing sunitinib therapy. Two patients with advanced RCC were imaged using an integrated PET/MRI scanner. [18 F] fluorothymidine (FLT) was used as the PET radiotracer, which can measure the degree of cell proliferation. Image acquisitions included test/retest scans before sunitinib treatment and one scan 3 weeks into treatment using [18 F] FLT-PET, T2-weighted (T2w), and diffusion-weighted imaging (DWI) protocols, where DWI yielded an apparent diffusion coefficient (ADC) map. Our framework to quantitatively characterize treatment-related changes involved the following analytic steps: 1) intraacquisition and interacquisition registration of protocols to allow voxel-wise comparison of changes in radiomic features, 2) correction and pseudoquantification of T2w images to remove acquisition artifacts and examine tissue-specific response, 3) characterization of information captured by T2w MRI, FLT-PET, and ADC via radiomics, and 4) combining multiparametric information to create a map of integrated changes from PET/MRI radiomic features. Standardized uptake value (from FLT-PET) and ADC textures ranked highest for reproducibility in a test/retest evaluation as well as for capturing treatment response, in comparison to high variability seen in T2w MRI. The highest-ranked radiomic feature yielded a normalized percentage change of 63% within the RCC region and 17% in a spatially distinct normal region relative to its pretreatment value. By comparison, both the original and postprocessed T2w signal intensity appeared to be markedly less sensitive and specific to changes within the tumor. Our preliminary results thus suggest that radiomics analysis could be a powerful tool for characterizing treatment response in integrated PET/MRI.

The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer

A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation (18)F-FDG PET can improve the accuracy of predicting pathCR to preoperative chemoradiotherapy in esophageal cancer beyond clinical predictors.

METHODS

This retrospective study was approved by the institutional review board, and the need for written informed consent was waived. Clinical parameters along with subjective and quantitative parameters from baseline and postchemoradiation (18)F-FDG PET were derived from 217 esophageal adenocarcinoma patients who underwent chemoradiotherapy followed by surgery. The associations between these parameters and pathCR were studied in univariable and multivariable logistic regression analysis. Four prediction models were constructed and internally validated using bootstrapping to study the incremental predictive values of subjective assessment of (18)F-FDG PET, conventional quantitative metabolic features, and comprehensive (18)F-FDG PET texture/geometry features, respectively. The clinical benefit of (18)F-FDG PET was determined using decision-curve analysis.

RESULTS

A pathCR was found in 59 (27%) patients. A clinical prediction model (corrected c-index, 0.67) was improved by adding (18)F-FDG PET-based subjective assessment of response (corrected c-index, 0.72). This latter model was slightly improved by the addition of 1 conventional quantitative metabolic feature only (i.e., postchemoradiation total lesion glycolysis; corrected c-index, 0.73), and even more by subsequently adding 4 comprehensive (18)F-FDG PET texture/geometry features (corrected c-index, 0.77). However, at a decision threshold of 0.9 or higher, representing a clinically relevant predictive value for pathCR at which one may be willing to omit surgery, there was no clear incremental value.

CONCLUSION

Subjective and quantitative assessment of (18)F-FDG PET provides statistical incremental value for predicting pathCR after preoperative chemoradiotherapy in esophageal cancer. However, the discriminatory improvement beyond clinical predictors does not translate into a clinically relevant benefit that could change decision making.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI = 58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI = 69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC = 0.58, q-value = 0.33), while the differentiation was significant with other textures (AUC = 0.71-0.73, p < 0.009). Among the deformable algorithms, fast-demons (AUC = 0.68-0.70, q-value < 0.03) and fast-free-form (AUC = 0.69-0.74, q-value < 0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC = 0.72-0.78, q-value < 0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer

Purpose

Texture indices (TI) calculated from 18F-FDG PET tumor images show promise for predicting response to therapy and survival. Their calculation involves a resampling of standardized uptake values (SUV) within the tumor. This resampling can be performed differently and significantly impacts the TI values. Our aim was to investigate how the resampling approach affects the ability of TI to reflect tissue-specific pattern of metabolic activity.

Methods

18F-FDG PET were acquired for 48 naïve-treatment patients with non-small cell lung cancer and for a uniform phantom. We studied 7 TI, SUVmax and metabolic volume (MV) in the phantom, tumors and healthy tissue using the usual relative resampling (RR) method and an absolute resampling (AR) method. The differences in TI values between tissue types and cancer subtypes were investigated using Wilcoxon’s tests.

Results

Most RR-based TI were highly correlated with MV for tumors less than 60 mL (Spearman correlation coefficient r between 0.74 and 1), while this correlation was reduced for AR-based TI (r between 0.06 and 0.27 except for RLNU where r = 0.91). Most AR-based TI were significantly different between tumor and healthy tissues (pvalues <0.01 for all 7 TI) and between cancer subtypes (pvalues<0.05 for 6 TI). Healthy tissue and adenocarcinomas exhibited more homogeneous texture than tumor tissue and squamous cell carcinomas respectively.

Conclusion

TI computed using an AR method vary as a function of the tissue type and cancer subtype more than the TI involving the usual RR method. AR-based TI might be useful for tumor characterization.

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.

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.

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.

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas

PURPOSE

Amino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas.

METHODS

One hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival.

RESULTS

All FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation did not hold in multivariate analysis.

CONCLUSIONS

Determination of uptake heterogeneity in pre-therapeutic FET-PET using textural features proved valuable for the (sub-)grading of high-grade glioma as well as prediction of tumor progression and patient survival, and showed improved performance compared to standard parameters such as TBR and tumor volume. Our results underscore the importance of intratumoral heterogeneity in the biology of high-grade glial cell tumors and may contribute to individual therapy planning in the future, although they must be confirmed in prospective studies before incorporation into clinical routine.

Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors

PURPOSE

To determine whether quantitative imaging features from pretreatment positron emission tomography (PET) can enhance patient overall survival risk stratification beyond what can be achieved with conventional prognostic factors in patients with stage III non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

The institutional review board approved this retrospective chart review study and waived the requirement to obtain informed consent. The authors retrospectively identified 195 patients with stage III NSCLC treated definitively with radiation therapy between January 2008 and January 2013. All patients underwent pretreatment PET/computed tomography before treatment. Conventional PET metrics, along with histogram, shape and volume, and co-occurrence matrix features, were extracted. Linear predictors of overall survival were developed from leave-one-out cross-validation. Predictive Kaplan-Meier curves were used to compare the linear predictors with both quantitative imaging features and conventional prognostic factors to those generated with conventional prognostic factors alone. The Harrell concordance index was used to quantify the discriminatory power of the linear predictors for survival differences of at least 0, 6, 12, 18, and 24 months. Models were generated with features present in more than 50% of the cross-validation folds.

RESULTS

Linear predictors of overall survival generated with both quantitative imaging features and conventional prognostic factors demonstrated improved risk stratification compared with those generated with conventional prognostic factors alone in terms of log-rank statistic (P = .18 vs P = .0001, respectively) and concordance index (0.62 vs 0.58, respectively). The use of quantitative imaging features selected during cross-validation improved the model using conventional prognostic factors alone (P = .007). Disease solidity and primary tumor energy from the co-occurrence matrix were found to be selected in all folds of cross-validation.

CONCLUSION

Pretreatment PET features were associated with overall survival when adjusting for conventional prognostic factors in patients with stage III NSCLC.

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.

Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy

Background

Textural features in FDG-PET have been shown to provide prognostic information in a variety of tumor entities. Here we evaluate their predictive value for recurrence and prognosis in NSCLC patients receiving primary stereotactic radiation therapy (SBRT).

Methods

45 patients with early stage NSCLC (T1 or T2 tumor, no lymph node or distant metastases) were included in this retrospective study and followed over a median of 21.4 months (range 3.1–71.1). All patients were considered non-operable due to concomitant disease and referred to SBRT as the primary treatment modality. Pre-treatment FDG-PET/CT scans were obtained from all patients. SUV and volume-based analysis as well as extraction of textural features based on neighborhood gray-tone difference matrices (NGTDM) and gray-level co-occurence matrices (GLCM) were performed using InterView Fusion™ (Mediso Inc., Budapest). The ability to predict local recurrence (LR), lymph node (LN) and distant metastases (DM) was measured using the receiver operating characteristic (ROC). Univariate and multivariate analysis of overall and disease-specific survival were executed.

Results

7 out of 45 patients (16%) experienced LR, 11 (24%) LN and 11 (24%) DM. ROC revealed a significant correlation of several textural parameters with LR with an AUC value for entropy of 0.872. While there was also a significant correlation of LR with tumor size in the overall cohort, only texture was predictive when examining T1 (tumor diameter < = 3 cm) and T2 (>3 cm) subgroups. No correlation of the examined PET parameters with LN or DM was shown.

In univariate survival analysis, both heterogeneity and tumor size were predictive for disease-specific survival, but only texture determined by entropy was determined as an independent factor in multivariate analysis (hazard ratio 7.48, p = .016). Overall survival was not significantly correlated to any examined parameter, most likely due to the high comorbidity in our cohort.

Conclusions

Our study adds to the growing evidence that tumor heterogeneity as described by FDG-PET texture is associated with response to radiation therapy in NSCLC. The results may be helpful into identifying patients who might profit from an intensified treatment regime, but need to be verified in a prospective patient cohort before being incorporated into routine clinical practice.

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.

Low-order non-spatial effects dominate second-order spatial effects in the texture quantifier analysis of 18F-FDG-PET images

BACKGROUND

There is increasing interest in applying image texture quantifiers to assess the intra-tumor heterogeneity observed in FDG-PET images of various cancers. Use of these quantifiers as prognostic indicators of disease outcome and/or treatment response has yielded inconsistent results. We study the general applicability of some well-established texture quantifiers to the image data unique to FDG-PET.

METHODS

We first created computer-simulated test images with statistical properties consistent with clinical image data for cancers of the uterine cervix. We specifically isolated second-order statistical effects from low-order effects and analyzed the resulting variation in common texture quantifiers in response to contrived image variations. We then analyzed the quantifiers computed for FIGOIIb cervical cancers via receiver operating characteristic (ROC) curves and via contingency table analysis of detrended quantifier values.

RESULTS

We found that image texture quantifiers depend strongly on low-effects such as tumor volume and SUV distribution. When low-order effects are controlled, the image texture quantifiers tested were not able to discern only the second-order effects. Furthermore, the results of clinical tumor heterogeneity studies might be tunable via choice of patient population analyzed.

CONCLUSION

Some image texture quantifiers are strongly affected by factors distinct from the second-order effects researchers ostensibly seek to assess via those quantifiers.

Prognostic significance of metabolic tumor burden by positron emission tomography/computed tomography in patients with relapsed/refractory diffuse large B-cell lymphoma

The aim of the present study was to investigate the feasibility of measuring metabolic tumor burden using [F-18] fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) in patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL) treated with bendamustine–rituximab. Because the standardized uptake value is a critical parameter of tumor characterization, we carried out a phantom study of ¹⁸F-FDG PET/CT to ensure quality control for 28 machines in the 24 institutions (Japan, 17 institutions; Korea, 7 institutions) participating in our clinical study. Fifty-five patients with relapsed or refractory DLBCL were enrolled. The ¹⁸F-FDG PET/CT was acquired before treatment, after two cycles, and after the last treatment cycle. Treatment response was assessed after two cycles and after the last cycle using the Lugano classification. Using this classification, remission was complete in 15 patients (27%) and incomplete in 40 patients (73%) after two cycles of therapy, and remission was complete in 32 patients (58%) and incomplete in 23 patients (42%) after the last treatment cycle. The percentage change in all PET/CT parameters except for the area under the curve of the cumulative standardized uptake value–volume histogram was significantly greater in complete response patients than in non-complete response patients after two cycles and the last cycle. The Cox proportional hazard model and best subset selection method revealed that the percentage change of the sum of total lesion glycolysis after the last cycle (relative risk, 5.24; P = 0.003) was an independent predictor of progression-free survival. The percent change of sum of total lesion glycolysis, calculated from PET/CT, can be used to quantify the response to treatment and can predict progression-free survival after the last treatment cycle in patients with relapsed or refractory DLBCL treated with bendamustine–rituximab.

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.

Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions

OBJECTIVE

The purpose is to develop and evaluate the ability of the computer-aided diagnosis (CAD) methods that apply texture analysis and pattern classification to differentiate malignant and benign bone and soft-tissue lesions on 18F-fluorodeoxy-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) images.

METHODS

Subjects were 103 patients with 59 malignant and 44 benign bone and soft tissue lesions larger than 25 mm in diameter. Variable texture parameters of standardized uptake values (SUV) and CT Hounsfield unit values were three-dimensionally calculated in lesional volumes-of-interest segmented on PET/CT images. After selection of a subset of the most optimal texture parameters, a support vector machine classifier was used to automatically differentiate malignant and benign lesions. We developed three kinds of CAD method. Two of them utilized only texture parameters calculated on either CT or PET images, and the other one adopted the combined PET and CT texture parameters. Their abilities of differential diagnosis were compared with the SUV method with an optimal cut-off value of the maximum SUV.

RESULTS

The CAD methods utilizing only optimal PET (or CT) texture parameters showed sensitivity of 83.05 % (81.35 %), specificity of 63.63 % (61.36 %), and accuracy of 74.76 % (72.82 %). Although the ability of differential diagnosis by PET or CT texture analysis alone was not significantly different from the SUV method whose sensitivity, specificity, and accuracy were 64.41, 61.36, and 63.11 % (the optimal cut-off SUVmax was 5.4 ± 0.9 in the 10-fold cross-validation test), the CAD method with the combined PET and CT optimal texture parameters (PET: entropy and coarseness, CT: entropy and correlation) exhibited significantly better performance compared with the SUV method (p = 0.0008), showing a sensitivity of 86.44 %, specificity of 77.27 %, and accuracy of 82.52 %.

CONCLUSIONS

The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Predicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

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.

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.