Impact of partial-volume effect correction on the predictive and prognostic value of baseline 18F-FDG PET images in esophageal cancer

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

PURPOSE

This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).

METHODS

We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.

RESULTS

From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.

CONCLUSION

Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Neural blind deconvolution for deblurring and supersampling PSMA PET

Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution.Approach. Blind deconvolution is a method of estimating the hypothetical 'deblurred' image along with the blur kernel (related to the point spread function) simultaneously. Traditionalmaximum a posterioriblind deconvolution methods require stringent assumptions and suffer from convergence to a trivial solution. A method of modelling the deblurred image and kernel with independent neural networks, called 'neural blind deconvolution' had demonstrated success for deblurring 2D natural images in 2020. In this work, we adapt neural blind deconvolution to deblur PSMA PET images while simultaneous supersampling to double the original resolution. We compare this methodology with several interpolation methods in terms of resultant blind image quality metrics and test the model's ability to predict accurate kernels by re-running the model after applying artificial 'pseudokernels' to deblurred images. The methodology was tested on a retrospective set of 30 prostate patients as well as phantom images containing spherical lesions of various volumes.Main results. Neural blind deconvolution led to improvements in image quality over other interpolation methods in terms of blind image quality metrics, recovery coefficients, and visual assessment. Predicted kernels were similar between patients, and the model accurately predicted several artificially-applied pseudokernels. Localization of activity in phantom spheres was improved after deblurring, allowing small lesions to be more accurately defined.Significance. The intrinsically low spatial resolution of PSMA PET leads to partial volume effects (PVEs) which negatively impact uptake quantification in small regions. The proposed method can be used to mitigate this issue, and can be straightforwardly adapted for other imaging modalities.

Prognostic Value of 18F-FDG PET/CT Volume-Based Metabolic Parameters in Patients with Node-Negative Stage II Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is a major cancer prevalent in Asian males. Pretreatment tumor burden can be prognostic for ESCC. We studied the prognostic value of metabolic parameters of 2-deoxy-2-[¹⁸F] fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and the serum squamous cell carcinoma antigen (SCC-Ag) level in node-negative stage II ESCC patients. Eighteen males underwent staging evaluation were included. The volume-based metabolic parameters derived from ¹⁸F-FDG PET/CT, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were obtained using the PET Volume Computer Assisted Reading application. The Spearman correlation coefficients were calculated to assess the relationship between metabolic parameters and pretreatment serum SCC-Ag levels. Based on the 5-year follow-up, patients were sub-divided into the demised and the stable groups. Potential prognostic value was assessed by independent t-test and the Mann–Whitney U test. The association of overall survival was assessed using univariate and multivariate Cox regression analyses. The demised group showed significant higher values in serum SCC-Ag, as well as in MTV and TLG, but not SUVmax and SUVmean. The SUVmax, MTV, TLG, and serum SCC-Ag showed significant association with overall survival. Our findings suggest potential usage of pretreatment volume-based metabolic parameters of ¹⁸F-FDG PET/CT and serum SCC-Ag as prognostic factors for node-negative stage II ESCC patients.

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

INTRODUCTION

With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [¹⁸F]-FDOPA PET imaging as a biomarker for the dopamine system.

METHODS

Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [¹⁸F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [¹⁸F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation.

RESULTS

The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets.

CONCLUSIONS

This feasibility study shows that it is possible to perform automatic QC of [¹⁸F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training.

Prognostic value of tumor metabolic imaging phenotype by FDG PET radiomics in HNSCC

OBJECTIVE

Tumor metabolic phenotype can be assessed with integrated image pattern analysis of 18F-fluoro-deoxy-glucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT), called radiomics. This study was performed to assess the prognostic value of radiomics PET parameters in head and neck squamous cell carcinoma (HNSCC) patients.

METHODS

18F-fluoro-deoxy-glucose (FDG) PET/CT data of 215 patients from HNSCC collection free database in The Cancer Imaging Archive (TCIA), and 122 patients in Seoul St. Mary's Hospital with baseline FDG PET/CT for locally advanced HNSCC were reviewed. Data from TCIA database were used as a training cohort, and data from Seoul St. Mary's Hospital as a validation cohort. With the training cohort, primary tumors were segmented by Nestles' adaptive thresholding method. Segmental tumors in PET images were preprocessed using relative resampling of 64 bins. Forty-two PET parameters, including conventional parameters and texture parameters, were measured. Binary groups of homogeneous imaging phenotypes, clustered by K-means method, were compared for overall survival (OS) and disease-free survival (DFS) by log-rank test. Selected individual radiomics parameters were tested along with clinical factors, including age and sex, by Cox-regression test for OS and DFS, and the significant parameters were tested with multivariate analysis. Significant parameters on multivariate analysis were again tested with multivariate analysis in the validation cohort.

RESULTS

A total of 119 patients, 70 from training, and 49 from validation cohort, were included in the study. The median follow-up period was 62 and 52 months for the training and the validation cohort, respectively. In the training cohort. binary groups with different metabolic radiomics phenotypes showed significant difference in OS (p = 0.036), and borderline difference in DFS (p = 0.086). Gray-Level Non-Uniformity for zone (GLNUGLZLM) was the most significant prognostic factor for both OS (hazard ratio [HR] 3.1, 95% confidence interval [CI] 1.4-7.3, p = 0.008) and DFS (HR 4.5, CI 1.3-16, p = 0.020). Multivariate analysis revealed GLNUGLZLM as an independent prognostic factor for OS (HR 3.7, 95% CI 1.1-7.5, p = 0.032). GLNUGLZLM remained as an independent prognostic factor in the validation cohort (HR 14.8. 95% CI 3.3-66, p < 0.001).

CONCLUSIONS

Baseline FDG PET radiomics contain risk information for survival prognosis in HNSCC patients. The metabolic heterogeneity parameter, GLNUGLZLM, may assist clinicians in patient risk assessment as a feasible prognostic factor.

Effect of uncertainties in quantitative 18 F-FDG PET/CT imaging feedback for intratumoral dose-response assessment and dose painting by number

PURPOSE

Intratumoral dose response can be detected using serial fluoro-2-deoxyglucose-(FDG) positron emission tomography (PET)/computed tomography (CT) imaging feedback during treatment and used to guide adaptive dose painting by number (DPbN). However, to reliably implement this technique, the effect of uncertainties in quantitative PET/CT imaging feedback on tumor voxel dose-response assessment and DPbN needs to be determined and reduced.

METHODS

Three major uncertainties, induced by (a) PET imaging partial volume effect (PVE) and (b) tumor deformable image registration (DIR), and (c) variation of the time interval between FDG injection and PET image acquisition (TI), were determined using serial FDG-PET/CT images acquired during chemoradiotherapy of 18 head and neck cancer patients. PET imaging PVE was simulated using the discrepancy between with and without iterative deconvolution-based PVE corrections. Effect of tumor DIR uncertainty was simulated using the discrepancy between two DIR algorithms, including one with and one without soft-tissue mechanical correction for the voxel displacement. The effect of TI variation was simulated using linear interpolation on the dual-point PET/CT images. Tumor voxel pretreatment metabolic activity (SUV₀ ) and dose-response matrix (DRM) discrepancies induced by each of the three uncertainties were quantified, respectively. Adverse effects of tumor voxel SUV₀ and DRM discrepancies on tumor control probability (TCP) in DPbN were assessed.

RESULTS

Partial volume effect and TI variations of 10 mins induced a mean ± standard deviation (SD) of tumor voxel SUV₀ discrepancies to be -0.7% ± 9.2% and 0% ± 4.8%, respectively. Tumor voxel DRM discrepancies induced by PVE, tumor DIR discrepancy, and TI variations were 0.6% ± 8.9%, 1.7% ± 9.1%, and 0% ± 7%, respectively. Partial volume effect induced SUV₀ and DRM discrepancies correlated significantly with the tumor shape and FDG uptake heterogeneity. Tumor DIR uncertainty-induced DRM discrepancy correlated significantly with the tumor volume and shrinkage during treatment. Among the three uncertainties, PVE dominated the adverse effects on the TCP, with a mean ± SD of TCP reduction to be 12.7% ± 9.8% for all tumors if no compensation was applied for.

CONCLUSIONS

Effect of uncertainties in quantitative FDG-PET/CT imaging feedback on intratumoral dose-response quantification was not negligible. These uncertainties primarily caused by PVE and tumor DIR were highly dependent on individual tumor shape, volume, shrinkage during treatment, and pretreatment SUV heterogeneity, which can be managed individually. The adverse effects of these uncertainties could be minimized by using proper PVE corrections and DIR methods and compensated for in the clinical implementation of DPbN.

Effects of different anesthesia and analgesia on cellular immunity and cognitive function of patients after surgery for esophageal cancer

BACKGROUND

The study intends to analyze influences of different anesthesia and analgesia on cellular immune and cognitive functions of patients undergoing thoracotomy for esophageal cancer (EsC).

METHODS

Patients undergoing thoracotomy for EsC were divided into four groups: Group A (received general anesthesia [GA]) and postoperative intravenous analgesia); B (received GA and postoperative epidural analgesia); C (received GA combined with thoracic epidural anesthesia [TEA]) and postoperative intravenous analgesia); D (received GA combined with TEA and postoperative epidural analgesia). The T-lymphocyte subsets were determined at 30 min before anesthesia induction (T<inf>0</inf>), 2 h after skin incision (T<inf>1</inf>), and at 4 h (T<inf>2</inf>), 24 h (T<inf>3</inf>), and 48 h (T<inf>4</inf>) after operation. Besides, visual analogue scale (VAS) and mini-mental state examination (MMSE) were assessed.

RESULTS

The percentage of CD3⁺ and CD4⁺ cells in groups B and C were higher than group A from T<inf>1</inf> to T<inf>3</inf>. The ratio of CD4⁺/CD8⁺ in group B and C were higher than in group A at T<inf>3</inf>. Compared with group A, group D had increased percentages of CD3⁺ and CD4⁺ from T<inf>1</inf> to T<inf>4</inf>, and elevated ratio of CD4⁺/CD8⁺ from T<inf>2</inf> to T<inf>4</inf> VAS scores were lower and MMSE scores were higher in groups B, C, and D than in group A, and group D had relatively lower VAS and higher MMSE scores as compared to group B.

CONCLUSIONS

The intraoperative general anesthesia combined with thoracic epidural anesthesia and postoperative epidural analgesia may reduce adverse effect on cellular immune and cognitive functions of patients undergoing thoracotomy for EsC.

Design and evaluation of an accurate CNR-guided small region iterative restoration-based tumor segmentation scheme for PET using both simulated and real heterogeneous tumors

Tumor delineation accuracy directly affects the effectiveness of radiotherapy. This study presents a methodology that minimizes potential errors during the automated segmentation of tumors in PET images. Iterative blind deconvolution was implemented in a region of interest encompassing the tumor with the number of iterations determined from contrast-to-noise ratios. The active contour and random forest classification-based segmentation method was evaluated using three distinct image databases that included both synthetic and real heterogeneous tumors. Ground truths about tumor volumes were known precisely. The volumes of the tumors were in the range of 0.49-26.34 cm³, 0.64-1.52 cm³, and 40.38-203.84 cm³ respectively. Widely available software tools, namely, MATLAB, MIPAV, and ITK-SNAP were utilized. When using the active contour method, image restoration reduced mean errors in volumes estimation from 95.85 to 3.37%, from 815.63 to 17.45%, and from 32.61 to 6.80% for the three datasets. The accuracy gains were higher using datasets that include smaller tumors for which PVE is known to be more predominant. Computation time was reduced by a factor of about 10 in the smaller deconvolution region. Contrast-to-noise ratios were improved for all tumors in all data. The presented methodology has the potential to improve delineation accuracy in particular for smaller tumors at practically feasible computational times. Graphical abstract Evaluation of accurate lesion volumes using CNR-guided and ROI-based restoration method for PET images.

Prognostic Significance of Metabolic Parameters and Textural Features on 18F-FDG PET/CT in Invasive Ductal Carcinoma of Breast

To investigate the prognostic significance of metabolic parameters and texture analysis on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in patients with breast invasive ductal carcinoma (IDC), from August 2005 to May 2015, IDC patients who had undergone pre-treatment FDG PET/CT were enrolled. The metabolic parameters, including maximal standardized uptake value of breast tumor (SUVbt) and ipsilateral axillary lymph node (SUVln), metabolic tumor volume (MTVbt) and total lesion glycolysis (TLGbt) of breast tumor, whole-body MTV (MTVwb) and whole-body TLG (TLGwb) were recorded. Nine textural features of tumor (four co-occurrence matrices and five SUV-based statistics) were measured. The prognostic significance of above parameters and clinical factors was assessed by univariate and multivariate analyses. Thirty-five patients were enrolled. Patients with low and high MTVwb had 5-year progression-free survival (PFS) of 81.0 and 14.3% (p < 0.0001). The 5-year overall survival for low and high MTVwb was 88.5% and 43.6% (p = 0.0005). Multivariate analyses showed MTVwb was an independent prognostic factor for PFS (HR: 8.29, 95% CI: 2.17–31.64, p = 0.0020). The SUV, TLG and textural features were not independently predictive. Elevated MTVwb was an independent predictor for shorter PFS in patients with breast IDC.

qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using ⁶⁸Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic ⁶⁸Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUV_mean, and PSMA SUV_max Twenty ⁶⁸Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUV_mean, and SUV_max were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in ⁶⁸Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

Radiotherapy for esophageal carcinoma: dose, response and survival

Esophageal cancer (EC) is an extremely aggressive, lethal malignancy that is increasing in incidence worldwide. At present, definitive chemoradiotherapy is accepted as the standard treatment for locally advanced EC. The EC guidelines recommend a radiation dose of 50.4 Gy for definitive treatment, yet the outcomes for patients who have received standard-dose radiotherapy remain unsatisfactory. However, some studies indicate that a higher radiation dose could improve local tumor control, and may also confer survival benefits. Some studies, however, suggest that high-dose radiotherapy does not bring survival benefit. The available data show that most failures occurred in the gross target volume (especially in the primary tumor) after definitive chemoradiation. Based on those studies, we hypothesize that at least for some patients, more intense local therapy may lead to better local control and survival. The aim of this review is to evaluate the radiation dose, fractionation strategies, and predictive factors of response to therapy in functional imaging for definitive chemoradiotherapy in esophageal carcinoma, with an emphasis on seeking the predictive model of response to CRT and trying to individualize the radiation dose for EC patients.

Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis

Purpose

Positron-emission tomography can be useful in oncology for diagnosis, (re)staging, determining prognosis, and response assessment. However, partial-volume effects hamper accurate quantification of lesions <2–3× the PET system’s spatial resolution, and the clinical impact of this is not evident. This systematic review provides an up-to-date overview of studies investigating the impact of partial-volume correction (PVC) in oncological PET studies.

Methods

We searched in PubMed and Embase databases according to the PRISMA statement, including studies from inception till May 9, 2016. Two reviewers independently screened all abstracts and eligible full-text articles and performed quality assessment according to QUADAS-2 and QUIPS criteria. For a set of similar diagnostic studies, we statistically pooled the results using bivariate meta-regression.

Results

Thirty-one studies were eligible for inclusion. Overall, study quality was good. For diagnosis and nodal staging, PVC yielded a strong trend of increased sensitivity at expense of specificity. Meta-analysis of six studies investigating diagnosis of pulmonary nodules (679 lesions) showed no significant change in diagnostic accuracy after PVC (p = 0.222). Prognostication was not improved for non-small cell lung cancer and esophageal cancer, whereas it did improve for head and neck cancer. Response assessment was not improved by PVC for (locally advanced) breast cancer or rectal cancer, and it worsened in metastatic colorectal cancer.

Conclusions

The accumulated evidence to date does not support routine application of PVC in standard clinical PET practice. Consensus on the preferred PVC methodology in oncological PET should be reached. Partial-volume-corrected data should be used as adjuncts to, but not yet replacement for, uncorrected data.

Electronic supplementary material

The online version of this article (doi:10.1007/s00259-017-3775-4) contains supplementary material, which is available to authorized users.

Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial

PURPOSE

In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC and to recommend imaging biomarkers appropriate for routine clinical applications.

METHODS

Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean for the whole-body, primary tumor, and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)).

RESULTS

The study included 196 patients. Pretreatment whole-body and primary tumor MTV, TLG, and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm³ for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm³ for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut-off points in Kaplan-Meier curves and in time-dependent ROC analysis.

CONCLUSION

We demonstrated that pretreatment whole-body and primary tumor volumetric FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, are strongly prognostic for OS in patients with locally advanced NSCLC, and have similar discriminatory ability. Therefore, we believe that, after validation in future trials, the derived optimal cut-off points for primary tumor volumetric FDG-PET/CT parameters, or their more refined versions, could be incorporated into routine clinical practice, and may provide more accurate prognostication and staging based on tumor metabolic features.

The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies

OBJECTIVES

The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings.

METHODS

Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV).

RESULTS

Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively.

CONCLUSIONS

Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies.

KEY POINTS

• PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake. • Radiomic features robustness is an important issue over different image reconstruction settings. • Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent. • Robust radiomic features can be considered as good candidates for tumour quantification.

Impact of PET/CT on radiation treatment in patients with esophageal cancer: A systematic review

PURPOSE

With the advances in radiotracers, positron emission tomography/computed tomography (PET/CT) is recognized as a useful adjunct to anatomic imaging with CT, MRI and endoscopic ultrasonography (EUS). The objective of this review was to comprehensively analyze the roles of PET/CT for the radiotherapy of esophageal cancer.

METHODS

In this review, we focused on issues concerning the application of PET/CT in TNM staging, target volume delineation and response to therapy, both for the primary tumor and regional lymph nodes. Furthermore, the following questions were addressed: how does PET/CT guide appropriate treatment protocols, how does it allow accurate tumor delineation and how does it guide prognosis and future treatment decisions.

RESULTS AND CONCLUSION

For the staging of esophageal cancer, PET/CT played a crucial role in exploring distant malignant lymph nodes and metastasis with high sensitivity, specificity and accuracy. PET/CT using different radiotracer provided a serial of thresholding methods based on standardized uptake value (SUV) to assist in auto-contouring the gross tumor volume (GTV). The change in SUV may offer a potential paradigm of personalized treatment to definitive chemoradiotherapy (CRT). In total, PET/CT has sought to further optimize radiotherapy treatment planning for patients with esophageal cancer.

Total Lesion Glycolysis Using ¹⁸F-FDG PET/CT as a Prognostic Factor for Locally Advanced Esophageal Cancer

Standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) have been considered prognostic factors for survival in many cancers. However, their prognostic value for radiotherapy-treated squamous esophageal cancer has not been evaluated. In this study, SUV, MTV, and TLG were measured to predict their prognostic role in overall survival (OS) in 38 esophageal cancer patients who had undergone (18)F-FDG PET/CT before radiotherapy. TLG demonstrated higher sensitivity and specificity for predicting OS than MTV and SUV; and a better OS was observed in patients with low TLG compared to those with high TLG in locally advanced disease (OS, 46.9 months; 95% confidence interval [CI], 33.50-60.26 vs. 25.3 months; 95% CI, 8.37-42.28; P=0.003). Multivariate analyses in these patients determined that TLG and the use of combination chemotherapy were the independent prognostic factors for OS (hazard ratio [HR], 7.12; 95% CI, 2.038-24.857; P=0.002 and HR, 6.76; 95% CI, 2.149-21.248; P=0.001, respectively). These results suggest that TLG is an independent prognostic factor for OS and a better predictor of survival than MTV and SUV in patients with locally advanced esophageal cancer treated with radiotherapy.

A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction

Oncologic PET images provide valuable information that can enable enhanced prognosis of disease. Nonetheless, such information is simplified significantly in routine clinical assessment to meet workflow constraints. Examples of typical FDG PET metrics include: (i) SUVmax, (2) total lesion glycolysis (TLG), and (3) metabolic tumor volume (MTV). We have derived and implemented a novel metric for tumor quantification, inspired in essence by a model of generalized equivalent uniform dose as used in radiation therapy. The proposed metric, denoted generalized effective total uptake (gETU), is attractive as it encompasses the abovementioned commonly invoked metrics, and generalizes them, for both homogeneous and heterogeneous tumors, using a single parameter a. We evaluated this new metric for improved overall survival (OS) prediction on two different baseline FDG PET/CT datasets: (a) 113 patients with squamous cell cancer of the oropharynx, and (b) 72 patients with locally advanced pancreatic adenocarcinoma. Kaplan-Meier survival analysis was performed, where the subjects were subdivided into two groups using the median threshold, from which the hazard ratios (HR) were computed in Cox proportional hazards regression. For the oropharyngeal cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak produced HR values of 1.86, 3.02, 1.34, 1.36 and 1.62, while the proposed gETU metric for a  = 0.25 (greater emphasis on volume information) enabled significantly enhanced OS prediction with HR  =  3.94. For the pancreatic cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak resulted in HR values of 1.05, 1.25, 1.42, 1.45 and 1.52, while gETU at a  = 3.2 (greater emphasis on SUV information) arrived at an improved HR value of 1.61. Overall, the proposed methodology allows placement of differing degrees of emphasis on tumor volume versus uptake for different types of tumors to enable enhanced clinical outcome prediction.

Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies

(18)F-fluoride PET/CT offers the opportunity for accurate skeletal metastasis staging, compared with conventional imaging methods. (18)F-fluoride is a bone-specific tracer whose uptake depends on osteoblastic activity. Because of the resulting increase in bone mineralization and sclerosis, the osteoblastic process can also be detected morphologically in CT images. Although CT is characterized by high resolution, the potential of PET is limited by its lower spatial resolution and the resulting partial-volume effect. In this context, the synergy between PET and CT presents an opportunity to resolve this limitation using a novel multimodal approach called synergistic functional-structural resolution recovery (SFS-RR). Its performance is benchmarked against current resolution recovery technology using the point-spread function (PSF) of the scanner in the reconstruction procedure.

METHODS

The SFS-RR technique takes advantage of the multiresolution property of the wavelet transform applied to both functional and structural images to create a high-resolution PET image that exploits the structural information of CT. Although the method was originally conceived for PET/MR imaging of brain data, an ad hoc version for whole-body PET/CT is proposed here. Three phantom experiments and 2 datasets of metastatic bone (18)F-fluoride PET/CT images from primary prostate and breast cancer were used to test the algorithm performances. The SFS-RR images were compared with the manufacturer's PSF-based reconstruction using the standardized uptake value (SUV) and the metabolic volume as metrics for quantification.

RESULTS

When compared with standard PET images, the phantom experiments showed a bias reduction of 14% in activity and 1.3 cm(3) in volume estimates for PSF images and up to 20% and 2.5 cm(3) for the SFS-RR images. The SFS-RR images were characterized by a higher recovery coefficient (up to 60%) whereas noise levels remained comparable to those of standard PET. The clinical data showed an increase in the SUV estimates for SFS-RR images up to 34% for peak SUV and 50% for maximum SUV and mean SUV. Images were also characterized by sharper lesion contours and better lesion detectability.

CONCLUSION

The proposed methodology generates PET images with improved quantitative and qualitative properties. Compared with standard methods, SFS-RR provides superior lesion segmentation and quantification, which may result in more accurate tumor characterization.

Optimising delineation accuracy of tumours in PET for radiotherapy planning using blind deconvolution

Positron emission tomography (PET) imaging has been proven to be useful in radiotherapy planning for the determination of the metabolically active regions of tumours. Delineation of tumours, however, is a difficult task in part due to high noise levels and the partial volume effects originating mainly from the low camera resolution. The goal of this work is to study the effect of blind deconvolution on tumour volume estimation accuracy for different computer-aided contouring methods. The blind deconvolution estimates the point spread function (PSF) of the imaging system in an iterative manner in a way that the likelihood of the given image being the convolution output is maximised. In this way, the PSF of the imaging system does not need to be known. Data were obtained from a NEMA NU-2 IQ-based phantom with a GE DSTE-16 PET/CT scanner. The artificial tumour diameters were 13, 17, 22, 28 and 37 mm with a target/background ratio of 4:1. The tumours were delineated before and after blind deconvolution. Student's two-tailed paired t-test showed a significant decrease in volume estimation error (p < 0.001) when blind deconvolution was used in conjunction with computer-aided delineation methods. A manual delineation confirmation demonstrated an improvement from 26 to 16 % for the artificial tumour of size 37 mm while an improvement from 57 to 15 % was noted for the small tumour of 13 mm. Therefore, it can be concluded that blind deconvolution of reconstructed PET images may be used to increase tumour delineation accuracy.

High metabolic tumor volume and total lesion glycolysis are associated with lateral lymph node metastasis in patients with incidentally detected thyroid carcinoma

OBJECTIVE

The objective of this study was to investigate whether total lesion glycolysis (TLG) and metabolic tumor volume (MTV) measured by ¹⁸F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) could predict the aggressiveness and lymph node metastasis (LNM) in patients with incidentally detected differentiated thyroid carcinoma.

METHODS

A total 358 patients with focal FDG-avid thyroid incidentaloma during cancer evaluation were enrolled. Among 235 patients in whom fine-needle aspiration biopsy was performed, 51 patients underwent total thyroidectomy with LN dissection. We analyzed the relationship between volume-based parameters and clinicopathologic characteristics.

RESULTS

The mean age and tumor size were 57.1 ± 11.3 years and 1.15 ± 0.81 cm, respectively. The prevalence of malignancy was 21.7 % (51/235). When SUV(max) > 5.91, MTV2.5 > 2.05 cm³, and TLG2.5 > 9.09 were used as cutoff points, sensitivity, specificity, and area under curve (AUC) for prediction of lateral LNM were 77.9, 69.1 %, 0.716 (P = 0.047), 77.8, 88.1 %, 0.839 (P < 0.001), 77.8, 85.1 %, and 0.815 (P = 0.002), respectively. However, MTV and TLG had no value in prediction of central LNM, extrathyroidal extension, and multifocality. On comparison ROC curve analysis, the MTV and TLG showed the statistical differences for the prediction of lateral LNM compared with SUV(max) (all P's < 0.05).

CONCLUSIONS

This study has shown for the first time that volume-based PET functional parameters had a significant value for the prediction of lateral LNM in incidentally detected PTC. These results suggest that higher MTV and TLG can be potential new risk factors for preoperative risk stratification. The usefulness of TLG and MTV in preoperative risk stratification in patients with PTC needs to be confirmed in further large studies.

FDG-PET Response Prediction in Pediatric Hodgkin's Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value

Background: In pediatric Hodgkin’s lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); Methods: One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; Results: All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Conclusions: Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

Safety of dose escalation by simultaneous integrated boosting radiation dose within the primary tumor guided by (18)FDG-PET/CT for esophageal cancer

PURPOSE

To observe the safety of selective dose boost to the pre-treatment high (18)F-deoxyglucose (FDG) uptake areas of the esophageal GTV.

METHODS

Patients with esophageal squamous cell carcinoma were treated with escalating radiation dose of 4 levels, with a simultaneous integrated boost (SIB) to the pre-treatment 50% SUVmax area of the primary tumor. Patients received 4 monthly cycles of cisplatin and fluorouracil. Dose-limiting toxicity (DLT) was defined as any Grade 3 or higher acute toxicities causing continuous interruption of radiation for over 1 week.

RESULTS

From April 2012 to February 2014, dose has been escalated up to LEVEL 4 (70Gy). All of the 25 patients finished the prescribed dose without DLT, and 10 of them developed Grade 3 acute esophagitis. One patient of LEVEL 2 died of esophageal hemorrhage within 1 month after completion of radiotherapy, which was not definitely correlated with treatment yet. Late toxicities remained under observation. With median follow up of 8.9months, one-year overall survival and local control was 69.2% and 77.4%, respectively.

CONCLUSIONS

Dose escalation in esophageal cancer based on (18)FDG-PET/CT has been safely achieved up to 70Gy using the SIB technique. Acute toxicities were well tolerated, whereas late toxicities and long-term outcomes deserved further observation.

Role of PET quantitation in the monitoring of cancer response to treatment: Review of approaches and human clinical trials

Positron emission tomography (PET) measures of cancer metabolism and cellular proliferation are increasingly being studied as markers of cancer response to treatment, with the goal of using them as predictors of patient therapeutic outcomes - i.e., as surrogate outcome measures. The primary PET radiotracers thus far used for monitoring response of cancer to treatment are ¹⁸F-fluorodeoxyglucose (FDG) for studying abnormal energy metabolism and ¹⁸F-fluorothymidine (FLT) for examining cell proliferation. Both FDG and FLT PET quantitation of cancer response to treatment have been found to correlate with patient outcomes, mostly in single-center studies. The aim of this review is to summarize the impact of commonly selected PET quantitation methods on the ability of PET measures to quantitate cancer response to treatment. An understanding of the biochemistry and kinetics of FDG and FLT uptake and knowledge of the expected tracer uptake by cancerous processes relative to background uptake are required to select appropriate PET quantitation methods for trials testing for correlations between PET measures and patient outcome. PET measures may eventually serve as predictive biomarkers capable of guiding individualized treatment and improving patient outcomes and quality of life by early identification of ineffective therapies. PET can also potentially identify patients who would be good candidates for molecularly targeted drugs and monitor response to these personalized therapies.

Predictive value of pre-therapy (18)F-FDG PET/CT for the outcome of (18)F-FDG PET-guided radiotherapy in patients with head and neck cancer

PURPOSE

The aim of this study was to evaluate the predictive role of pre-therapy fluorodeoxyglucose (FDG) uptake parameters of primary tumour in head and neck cancer (HNC) patients undergoing intensity-modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB) on FDG-positive volume-positron emission tomography (PET) gross tumour volume (PET-GTV).

METHODS

This retrospective study included 19 patients (15 men and 4 women, mean age 59.2 years, range 23-81 years) diagnosed with HNC between 2005 and 2011. Of 19 patients, 15 (79 %) had stage III-IV. All patients underwent FDG PET/CT before treatment. Metabolic indexes of primary tumour, including metabolic tumour volume (MTV), maximum and mean standardized uptake value (SUVmax, SUVmean) and total lesion glycolysis (TLG) were considered. Partial volume effect correction (PVC) was performed for SUVmean and TLG estimation. Correlations between PET/CT parameters and 2-year disease-free survival (DFS), local recurrence-free survival (LRFS) and distant metastasis-free survival (DMFS) were assessed. Median patient follow-up was 19.2 months (range 4-24 months).

RESULTS

MTV, TLG and PVC-TLG predicting patients' outcome with respect to all the considered local and distant disease control endpoints (LRFS, DMFS and DFS) were 32.4 cc, 469.8 g and 547.3 g, respectively. SUVmean and PVC-SUVmean cut-off values predictive of LRFS and DFS were 10.8 and 13.3, respectively. PVC was able to compensate errors up to 25 % in the primary HNC tumour uptake. Moreover, PVC enhanced the statistical significance of the results.

CONCLUSION

FDG PET/CT uptake parameters are predictors of patients' outcome and can potentially identify patients with higher risk of treatment failure that could benefit from more aggressive approaches. Application of PVC is recommended for accurate measurement of PET parameters.

Early prediction of pathological response in locally advanced rectal cancer based on sequential 18F-FDG PET

BACKGROUND

The objectives of this study were to investigate the predictive value of sequential (18)F-FDG PET scans for pathological tumor response grade (TRG) after preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) and the impact of partial volume effects correction (PVC).

METHODS

Twenty-eight LARC patients were included. Responders and non-responders status were determined in histopathology. PET indices [SUV max and mean, volume and total lesion glycolysis (TLG)] at baseline and their evolution after one and two weeks of PCRT were extracted by delineation of the PET images, with or without PVC. Their predictive value was investigated using Mann-Whitney-U tests and ROC analysis.

RESULTS

Within baseline parameters, only SUVmean was correlated with response. No evolution after one week was predictive of the response, whereas after two weeks all the parameters except volume were, the best prediction being obtained with TLG (AUC 0.79, sensitivity 63%, specificity 92%). PVC had no significant impact on these results.

CONCLUSION

Several PET indices at baseline and their evolution after two weeks of PCRT are good predictors of response in LARC, with or without PVC, whereas results after one week are suboptimal. Best predictor was TLG reduction after two weeks, although baseline SUVmean had smaller but similar predictive power.

Comparison between 18F-FDG PET image-derived indices for early prediction of response to neoadjuvant chemotherapy in breast cancer

The goal of this study was to determine the best predictive factor among image-derived parameters extracted from sequential (18)F-FDG PET scans for early tumor response prediction after 2 cycles of neoadjuvant chemotherapy in breast cancer.

METHODS

51 breast cancer patients were included. Responder and nonresponder status was determined by histopathologic examination according to the tumor and node Sataloff scale. PET indices (maximum and mean standardized uptake value [SUV], metabolically active tumor volume, and total lesion glycolysis [TLG]), at baseline and their variation (Δ) after 2 cycles of neoadjuvant chemotherapy were extracted from the PET images. Their predictive value was investigated using Mann-Whitney U tests and receiver-operating-characteristic analysis. Subgroup analysis was also performed by considering estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative, triple-negative, and HER2-positive tumors separately. The impact of partial-volume correction was also investigated using an iterative deconvolution algorithm.

RESULTS

There were 24 pathologic nonresponders and 27 responders. None of the baseline PET parameters was correlated with response. After 2 neoadjuvant chemotherapy cycles, the reduction of each parameter was significantly associated with response, the best prediction of response being obtained with ΔTLG (96% sensitivity, 92% specificity, and 94% accuracy), which had a significantly higher area under the curve (0.91 vs. 0.82, P = 0.01) than did ΔSUVmax (63% sensitivity, 92% specificity, and 77% accuracy). Subgroup analysis confirmed a significantly higher accuracy for ΔTLG than ΔSUV for ER-positive/HER-negative but not for triple-negative and HER2-positive tumors. Partial-volume correction had no impact on the predictive value of any of the PET image-derived parameters despite significant changes in their absolute values.

CONCLUSION

Our results suggest that the reduction after 2 neoadjuvant chemotherapy cycles of the metabolically active volume of primary tumor measurements such as ΔTLG predicts histopathologic tumor response with higher accuracy than does ΔSUV measurements, especially for ER-positive/HER2-negative breast cancer. These results should be confirmed in a larger group of patients as they may potentially increase the clinical value and efficiency of (18)F-FDG PET for early prediction of response to neoadjuvant chemotherapy.

Prognostic significance of volume-based PET parameters in cancer patients

Accurate prediction of cancer prognosis before the start of treatment is important since these predictions often affect the choice of treatment. Prognosis is usually based on anatomical staging and other clinical factors. However, the conventional system is not sufficient to accurately and reliably determine prognosis. Metabolic parameters measured by ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) have the potential to provide valuable information regarding prognosis and treatment response evaluation in cancer patients. Among these parameters, volume-based PET parameters such as metabolic tumor volume and total lesion glycolysis are especially promising. However, the measurement of these parameters is significantly affected by the imaging methodology and specific image characteristics, and a standard method for these parameters has not been established. This review introduces volume-based PET parameters as potential prognostic indicators, and highlights methodological considerations for measurement, potential implications, and prospects for further studies.

Imaging of cellular proliferation in liver metastasis by [18F]fluorothymidine positron emission tomography: effect of therapy

Although [(18)F]fluorothymidine positron emission tomography (FLT-PET) permits estimation of tumor thymidine kinase-1 expression, and thus, cell proliferation, high physiological uptake of tracer in liver tissue can limit its utility. We evaluated FLT-PET combined with a temporal-intensity information-based voxel-clustering approach termed kinetic spatial filtering (FLT-PET(KSF)) for detecting drug response in liver metastases. FLT-PET and computed tomography data were collected from patients with confirmed breast or colorectal liver metastases before, and two weeks after the first cycle of chemotherapy. Changes in tumor FLT-PET and FLT-PET(KSF) variables were determined. Visual distinction between tumor and normal liver was seen in FLT-PET(KSF) images. Of the 33 metastases from 20 patients studied, 26 were visible after kinetic filtering. The net irreversible retention of the tracer (Ki; from unfiltered data) in the tumor, correlated strongly with tracer uptake when the imaging variable was an unfiltered average or maximal standardized uptake value, 60 min post-injection (SUV(60,av): r = 0.9, SUV(60,max): r = 0.7; p < 0.0001 for both) and occurrence of high intensity voxels derived from FLT-PET(KSF) (r = 0.7, p < 0.0001). Overall, a significant reduction in the imaging variables was seen in responders compared to non-responders; however, the two week time point selected for imaging was too early to allow prediction of long term clinical benefit from chemotherapy. FLT-PET and FLT-PET(KSF) detected changes in proliferation in liver metastases.