The age of reason for FDG PET image-derived indices

Clinico-pathological factors and [18F]FDG PET/CT metabolic parameters for prediction of progression-free survival in radioiodine refractory differentiated thyroid carcinoma

OBJECTIVE

Identifying prognostic markers for clinical outcomes is crucial in selecting appropriate treatment options for patients with radioiodine-refractory (RAI-R) differentiated thyroid carcinoma (DTC). The aim of this study was to investigate the prognostic value of clinico-pathological features and semiquantitative [18F]FDG PET/CT metabolic parameters in predicting progression-free survival (PFS) in DTC patients with RAI-R.

PATIENTS AND METHODS

This prospective cohort study included 110 consecutive RAI-R DTC patients who were referred for [18F]FDG PET/CT imaging. The lesion standard uptake values (SUV)s, including SUVmax, SUVmean, SULpeak as well astotal metabolic tumor volume (tMTV)and total lesion glycolysis (tTLG) were measured. Disease progression was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and/or Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) 1.0. PFS curves were plotted using Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors for PFS.

RESULTS

[18F]FDG PET/CT metabolic parameters demonstrate predictive value for PFS in RAI-R DTC patients, with sensitivity ranging from 70.7% to 81% and specificity from 75% to 92.3% (p < 0.001). PFS was significantly worse in patients with SUVmax > 6.39 g/ml, SUVmean > 3.68 g/ml, SULpeak > 3.14 g/ml, tTLG > 4.23 g/ml × cm3, and tMTV > 1.24 cm3. Clinico-pathological factors including age > 55, aggressive variant and follicular histological subtype, extra-thyroidal extension of the primary tumor, stage III - IV disease at initial DTC diagnosis, distant metastases detected on [18F]FDG PET/CT, and metabolic parameters of [18F]FDG PET/CT associated with PFS in univariate analysis (p < 0.01). In multivariate analysis, extra-thyroidal extension (HR: 2.25; 95% CI: 1.22 - 4.16; p = 0.01), distant metastases on [18F]FDG PET/CT (HR: 2.98; 95%CI: 1.62 - 5.5; p < 0.001), and tMTV > 1.24 cm3 (HR: 4.17; 95% CI: 2.02 - 8.6; p < 0.001), were independent prognostic factors for PFS.

CONCLUSIONS

In addition to classic clinico-pathological factors, the semiquantitative [18F]FDG PET/CT metabolic parameters can be utilized for dynamic risk stratification for progression in RAI-R DTC patients. Furthermore, extra-thyroidal extension of the primary tumor, distant metastases, and tMTV > 1.24 cm3 are independent prognostic factors for PFS.

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

The exploration of quantitative intra-tumoral metabolic heterogeneity in dual-time 18F-FDG PET/CT of pancreatic cancer

PURPOSE

We aimed to analyze the change of quantitative intra-tumoral metabolic heterogeneity consisting of texture features and conventional metabolic parameters of pancreatic cancer (PC) in dual-time 2-deoxy-2(¹⁸F) fluoro-D-glucose (¹⁸F-FDG) positron emission tomography-computed tomography (PET/CT).

METHODS

A retrospective analysis was conducted considering the texture features and conventional metabolic parameters in dual-time ¹⁸F-FDG PET/CT scans of PC patients. Features were extracted based on spatial distribution of ¹⁸F-FDG uptake in image. Firstly, the texture features and the conventional metabolic parameters of the delayed scan were both compared with that of the early scan. Statistically different data was defined among them. Secondly, the study evaluated the correlations between retention index (RI) of the texture features and the conventional metabolic parameters. Finally, the variation of texture features in dual-time PET/CT of resectable PC patients and unresectable PC patients was calculated separately.

RESULTS

In total, 183 PC patients were analyzed retrospectively in this research. The conventional metabolic parameters were all statistically different between the early and delayed scans except for metabolic tumor volume (MTV). In the radiomics, there were 59 textural features. Nineteen of 59 texture features were statistically different between the early and delayed scans. Features that were more than 10% different during two scans were observed in a substantial percentage of patients. Weak correlations were only found between MTV, TLG (Total lesion glycolysis), SUVpeak and the RI of some texture features in early or delayed scans. There were obviously fewer features with significant difference in resectable PC group than in unresectable PC group. Most features showing the difference in unresectable group while no significant difference in resectable group.

CONCLUSIONS

This study investigated the change and inner correlations of quantitative tumoral metabolic heterogeneity in the dual-time ¹⁸F-FDG-PET/CT scan of PC patients. Some features displayed the difference between dual-time scans. Conventional metabolic parameters were weakly related to the change of texture feature. The change of texture feature in resectable PC group was different from that in unresectable PC group. This result is potential to provide more information for the image evaluation of PC.

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning

PURPOSE

To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on 18F-FDG PET/CT and radiomic features using machine-learning methods.

METHODS

A total of 199 colorectal cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and CRC radical surgery. The Chang-Gung Image Texture Analysis toolbox (CGITA) was used to extract 70 PET radiomic features reflecting 18F-FDG uptake heterogeneity of tumors. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomic signature score (Rad-score). The training set was used to establish five machine-learning prediction models and the test set was used to test the efficacy of the models. The effectiveness of the models was compared by ROC analysis.

RESULTS

The CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808-0.925) and XGBoost (AUC 0.903, 95% CI 0.855-0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by 18F-FDG PET/CT for differentiating positive and negative regional LN metastases in CRC (all p < 0.05). Good efficacy of the above two models was also achieved in the test set. We created a nomogram based on the logistic regression model that visualized the results and provided an easy-to-use method for predicting regional LN metastasis in patients with CRC.

CONCLUSION

In this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on 18F-FDG PET/CT and PET-based radiomic features were successfully developed and validated. Among them, the logistic regression and XGBoost models performed the best, with higher efficacy than 18F-FDG PET/CT in both the training and test sets.

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

Simple Summary

Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [¹⁸F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models.

Abstract

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [¹⁸F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [¹⁸F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUV_max model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [¹⁸F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.

Prognostic Impact of Pretherapeutic FDG-PET in Localized Anal Cancer

Due to the heterogeneity of tumour mass segmentation methods and lack of consensus, our study evaluated the prognostic value of pretherapeutic positron emission tomography with fluorodeoxyglucose (FDG-PET) metabolic parameters using different segmentation methods in patients with localized anal squamous cell carcinoma (SCC). Eighty-one patients with FDG-PET before radiochemotherapy were retrospectively analyzed. Semiquantitative data were measured with three fixed thresholds (35%, 41% and 50% of Maximum Standardized Uptake Value (SUVmax)) and four segmentation methods based on iterative approaches (Black, Adaptive, Nestle and Fitting). Metabolic volumes of primary anal tumour (P-MTV) and total tumour load (T-MTV: P-MTV+ lymph node MTV) were calculated. The primary endpoint was event-free survival (EFS). Seven multivariate models were created to compare FDG-PET tumour volumes prognostic impact. For all segmentation thresholds, PET metabolic volume parameters were independent prognostic factor and T-MTV variable was consistently better associated with EFS than P-MTV. Patient's sex was an independent variable and significantly correlated with EFS. With fixed threshold segmentation methods, 35% of SUVmax threshold seemed better correlated with EFS and the best cut-off for discrimination between a low and high risk of event occurrence was 40 cm3. Determination of T-MTV by FDG-PET using fixed threshold segmentation is useful for predicting EFS for primary anal SCC. If these data are confirmed in larger studies, FDG-PET could contribute to individualized patient therapies.

Initial FDG-PET/CT predicts survival in adults Ewing sarcoma family of tumors

Purpose

The aim of this retrospective study was to determine, at baseline, the prognostic value of different FDG-PET/CT quantitative parameters in a homogenous Ewing Sarcoma Family of Tumors (ESFT) adult population, compared with clinically relevant prognostic factors.

Methods

Adult patients from 3 oncological centers, all with proved ESFT, were retrospectively included. Quantitative FDG-PET/CT parameters (SUV (maximum, peak and mean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary lesion of each patient were recorded before treatment, as well as usual clinical prognostic factors (stage of disease, location, tumor size, gender and age). Then, their relation with progression free survival (PFS) and overall survival (OS) was evaluated.

Results

32 patients were included. Median age was 21 years (range, 15 to 61). Nineteen patients (59%) were initially metastatic. On multivariate analysis, high SUV_max remained independent predictor of worst OS (p=0.02) and PFS (p=0.019), metastatic disease of worst PFS (p=0.01) and high SUVpeak of worst OS (p=0.01). Optimal prognostic cut-off of SUV_peak was found at 12.5 in multivariate analyses for PFS and OS (p=0.0001).

Conclusions

FDG-PET/CT, recommended at ESFT diagnosis for initial staging, can be a useful tool for predicting long-term adult patients outcome through semi-quantitative parameters.

Clinical usefulness of 18F-FDG PET/CT for initial staging and assessment of treatment efficacy in patients with lymph node tuberculosis

INTRODUCTION

Few studies have evaluated the promising role of ¹⁸F-fluoro-2-deoxy-D-glucose positron emission tomography (PET) and PET/computed tomography FDG PET/CT in evaluating and monitoring treatment response in patients with lymph node tuberculosis (LNTB). The aim of this clinical investigation was to assess the clinical usefulness of FDG PET/CT for initial tuberculosis staging and to determine the prognostic value of the decrease of ¹⁸F-FDG uptake during antibiotic treatment in LNTB patients.

METHODS

We retrospectively reviewed 18 cases of LNTB admitted at a single center from 2004 to 2014. Medical records of patients who underwent two FDG PET/CT (>6 months interval), at initial staging and at the end of therapy were reviewed to determine the impact of FDG PET/CT on initial management of LNTB and response to therapy. Statistical analysis was performed using linear mixed-effects model.

RESULTS

Thirteen cases of disseminated LNTB and five cases of localized LNTB were included in the study. Initial FDG PET/CT allowed guided biopsy for initial diagnosis in 5 patients and identified unknown extra-LN TB sites in 9 patients. Visual analysis follow-up of FDG PET/CT showed a complete metabolic response in 9/18 patients (all of whom were cured), a partial response in 7/18 (5 of whom were cured) and no response in 2/18 (all of whom were not cured). The semi-quantitative evaluation of 18F-FDG intensity decrease based on the maximum standardized uptake value (SUVmax), compared to targeted estimated decrease allowed to predict correctly a complete response to treatment in 14/18 cases.

CONCLUSION

FDG PET/CT allows an accurate pre-therapeutic mapping of LNTB and helps for early TB confirmation. The SUVmax follow up is a potential tool for monitoring the treatment response.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials

Purpose

This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials.

Methods

The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT ⁶⁸Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results.

Results

The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE.

Conclusion

Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

Local respiratory motion correction for PET/CT imaging: Application to lung cancer

PURPOSE

Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications.

METHODS

Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions.

RESULTS

Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV.

CONCLUSIONS

The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.

The Impact of Optimal Respiratory Gating and Image Noise on Evaluation of Intratumor Heterogeneity on 18F-FDG PET Imaging of Lung Cancer

Accurate measurement of intratumor heterogeneity using parameters of texture on PET images is essential for precise characterization of cancer lesions. In this study, we investigated the influence of respiratory motion and varying noise levels on quantification of textural parameters in patients with lung cancer.

METHODS

We used an optimal-respiratory-gating algorithm on the list-mode data of 60 lung cancer patients who underwent ¹⁸F-FDG PET. The images were reconstructed using a duty cycle of 35% (percentage of the total acquired PET data). In addition, nongated images of varying statistical quality (using 35% and 100% of the PET data) were reconstructed to investigate the effects of image noise. Several global image-derived indices and textural parameters (entropy, high-intensity emphasis, zone percentage, and dissimilarity) that have been associated with patient outcome were calculated. The clinical impact of optimal respiratory gating and image noise on assessment of intratumor heterogeneity was evaluated using Cox regression models, with overall survival as the outcome measure. The threshold for statistical significance was adjusted for multiple comparisons using Bonferroni correction.

RESULTS

In the lower lung lobes, respiratory motion significantly affected quantification of intratumor heterogeneity for all textural parameters (P < 0.007) except entropy (P > 0.007). The mean increase in entropy, dissimilarity, zone percentage, and high-intensity emphasis was 1.3% ± 1.5% (P = 0.02), 11.6% ± 11.8% (P = 0.006), 2.3% ± 2.2% (P = 0.002), and 16.8% ± 17.2% (P = 0.006), respectively. No significant differences were observed for lesions in the upper lung lobes (P > 0.007). Differences in the statistical quality of the PET images affected the textural parameters less than respiratory motion, with no significant difference observed. The median follow-up time was 35 mo (range, 7-39 mo). In multivariate analysis for overall survival, total lesion glycolysis and high-intensity emphasis were the two most relevant image-derived indices and were considered to be independent significant covariates for the model regardless of the image type considered.

CONCLUSION

The tested textural parameters are robust in the presence of respiratory motion artifacts and varying levels of image noise.

Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis

The optical signature of leaves is an important monitoring and predictive parameter for a variety of biotic and abiotic stresses, including drought. Such signatures derived from spectroscopic measurements provide vegetation indices - a quantitative method for assessing plant health. However, the commonly used metrics suffer from low sensitivity. Relatively small changes in water content in moderately stressed plants demand high-contrast imaging to distinguish affected plants. We present a new approach in deriving sensitive indices using hyperspectral imaging in a short-wave infrared range from 800 nm to 1600 nm. Our method, based on high spectral resolution (1.56 nm) instrumentation and image processing algorithms (quantitative histogram analysis), enables us to distinguish a moderate water stress equivalent of 20% relative water content (RWC). The identified image-derived indices 15XX nm/14XX nm (i.e. 1529 nm/1416 nm) were superior to common vegetation indices, such as WBI, MSI, and NDWI, with significantly better sensitivity, enabling early diagnostics of plant health.

Assessment of Lymph Nodes and Prostate Status Using Early Dynamic Curves with (18)F-Choline PET/CT in Prostate Cancer

Introduction

Dynamic image acquisition with ¹⁸F-Choline [fluorocholine (FCH)] PET/CT in prostate cancer is mostly used to overcome the bladder repletion, which could obstruct the loco-regional analysis. The aim of our study was to analyze early dynamic FCH acquisitions to define pelvic lymph node or prostate pathological status.

Material and methods

Retrospective analysis was performed on 39 patients for initial staging (n = 18), or after initial treatment (n = 21). Patients underwent 10-min dynamic acquisitions centered on the pelvis, after injection of 3–4 MBq/kg of FCH. Whole-body images were acquired about 1 h after injection using a PET/CT GE Discovery LS (GE-LS) or Siemens Biograph mCT (mCT). Maximum and mean SUV according to time were measured on nodal and prostatic lesions. SUV_mean was corrected for partial volume effect (PVEC) with suitable recovery coefficients. The status of each lesion was based on histological results or patient follow-up (>6 months). A Mann–Whitney test and ANOVA were used to compare mean and receiver operating characteristic (ROC) curve analysis.

Results

The median PSA was 8.46 ng/mL and the median Gleason score was 3 + 4. Ninety-two lesions (43 lymph nodes and 49 prostate lesions) were analyzed, including 63 malignant lesions. In early dynamic acquisitions, the maximum and mean SUV were significantly higher, respectively, on mCT and GE-LS, in malignant versus benign lesions (p < 0.001, p < 0.001). Mean SUV without PVEC, allowed better discrimination of benign from malignant lesions, in comparison with maximum and mean SUV (with PVEC), for both early and late acquisitions. For patients acquired on mCT, area under the ROC curve showed a trend to better sensitivity and specificity for early acquisitions, compared with late acquisitions (SUVmax AUC 0.92 versus 0.85, respectively).

Conclusion

Assessment of lymph nodes and prostate pathological status with early dynamic imaging using PET/CT FCH allowed prostate cancer detection in situations where proof of malignancy is difficult to obtain.

Quantitative Imaging in Radiation Oncology: An Emerging Science and Clinical Service

Radiation oncology has long required quantitative imaging approaches for the safe and effective delivery of radiation therapy. The past 10 years has seen a remarkable expansion in the variety of novel imaging signals and analyses that are starting to contribute to the prescription and design of the radiation treatment plan. These include a rapid increase in the use of magnetic resonance imaging, development of contrast-enhanced imaging techniques, integration of fluorinated deoxyglucose-positron emission tomography, evaluation of hypoxia imaging techniques, and numerous others. These are reviewed with an effort to highlight challenges related to quantification and reproducibility. In addition, several of the emerging applications of these imaging approaches are also highlighted. Finally, the growing community of support for establishing quantitative imaging approaches as we move toward clinical evaluation is summarized and the need for a clinical service in support of the clinical science and delivery of care is proposed.

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.

Correction for FDG PET dose extravasations: Monte Carlo validation and quantitative evaluation of patient studies

PURPOSE

Current procedure guidelines for whole body [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET) state that studies with visible dose extravasations should be rejected for quantification protocols. Our work is focused on the development and validation of methods for estimating extravasated doses in order to correct standard uptake value (SUV) values for this effect in clinical routine.

METHODS

One thousand three hundred sixty-seven consecutive whole body FDG-PET studies were visually inspected looking for extravasation cases. Two methods for estimating the extravasated dose were proposed and validated in different scenarios using Monte Carlo simulations. All visible extravasations were retrospectively evaluated using a manual ROI based method. In addition, the 50 patients with higher extravasated doses were also evaluated using a threshold-based method.

RESULTS

Simulation studies showed that the proposed methods for estimating extravasated doses allow us to compensate the impact of extravasations on SUV values with an error below 5%. The quantitative evaluation of patient studies revealed that paravenous injection is a relatively frequent effect (18%) with a small fraction of patients presenting considerable extravasations ranging from 1% to a maximum of 22% of the injected dose. A criterion based on the extravasated volume and maximum concentration was established in order to identify this fraction of patients that might be corrected for paravenous injection effect.

CONCLUSIONS

The authors propose the use of a manual ROI based method for estimating the effectively administered FDG dose and then correct SUV quantification in those patients fulfilling the proposed criterion.

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.

Is the standard uptake value (SUV) appropriate for quantification in clinical PET imaging? - Variability induced by different SUV measurements and varying reconstruction methods

INTRODUCTION

PET quantification using the standard uptake value (SUV) is very prone to variations by technical factors of the scanner system and patient specific characteristics. Aim of the study was to investigate the reproducibility of SUV values between different measures and different reconstruction algorithms in a PET/CT scanner of the newest generation.

METHODS

The time-of-flight PET datasets of 27 consecutive oncological patients were reconstructed with OSEM3D in two different matrix sizes (200 × 200 and 400 × 400) as well as in a matrix size of 400 × 400 and additional point-spread-reconstruction. The standardized uptake values SUVmax, SUVmean and SUVpeak in 60 lesions were compared concerning their variability in the three reconstructions.

RESULTS

The addition of point-spread-reconstruction causes a significant increase of SUV values in comparison to OSEM 3D. SUVpeak showed the highest reproducibility between the different reconstruction algorithms. The variability of SUVmax and SUVmean increases in small lesions <5 ml, while SUVpeak remains more stable.

CONCLUSION

SUVmax, SUVmean and SUVpeak can be used for PET quantification in principle. However, quantification of small lesions is difficult. SUVpeak is the most robust method when using varying reconstruction methods, especially in small lesions.

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.

Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images

BACKGROUND

The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project.

METHODS

With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies.

RESULTS

In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmean for outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmean and TLG (0.6 and 0.52, resp.).

CONCLUSIONS

CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use at http://code.google.com/p/cgita.

Cloud computing in medical imaging

Over the past century technology has played a decisive role in defining, driving, and reinventing procedures, devices, and pharmaceuticals in healthcare. Cloud computing has been introduced only recently but is already one of the major topics of discussion in research and clinical settings. The provision of extensive, easily accessible, and reconfigurable resources such as virtual systems, platforms, and applications with low service cost has caught the attention of many researchers and clinicians. Healthcare researchers are moving their efforts to the cloud, because they need adequate resources to process, store, exchange, and use large quantities of medical data. This Vision 20/20 paper addresses major questions related to the applicability of advanced cloud computing in medical imaging. The paper also considers security and ethical issues that accompany cloud computing.

Standardized added metabolic activity (SAM) IN ¹⁸F-FDG PET assessment of treatment response in colorectal liver metastases

PURPOSE

Standardized added metabolic activity (SAM) is a PET parameter for assessing the total metabolic load of malignant processes, avoiding partial volume effects and lesion segmentation. The potential role of this parameter in the assessment of response to chemotherapy and bevacizumab was tested in patients with metastatic colorectal cancer with potentially resectable liver metastases (mCRC).

METHODS

(18)F-FDG PET/CT was performed in 18 mCRC patients with liver metastases before treatment and after five cycles of FOLFOX/FOLFIRI and bevacizumab. Of the 18 patients, 16 subsequently underwent resection of liver metastases. Baseline and follow-up SUVmax, and SAM as well as reduction in SUVmax (∆SUVmax) and SAM (∆SAM) of all liver metastases were correlated with morphological response, and progression-free and overall survival (PFS and OS).

RESULTS

A significant reduction in metabolic activity of the liver metastases was seen after chemotherapy with a median ∆SUVmax of 25.3% and ∆SAM of 94.5% (p = 0.033 and 0.003). Median baseline SUVmax and SAM values were significantly different between morphological responders and nonresponders (3.8 vs. 7.2, p = 0.021; and 34 vs. 211, p = 0.002, respectively), but neither baseline PET parameters nor morphological response was correlated with PFS or OS. Follow-up SUVmax and SAM as well as ∆SAM were found to be prognostic factors. The median PFS and OS in the patient group with a high follow-up SUVmax were 10.4 months and 32 months, compared to a median PFS of 14.7 months and a median OS which had not been reached in the group with a low follow-up SUVmax (p = 0.01 and 0.003, respectively). The patient group with a high follow-up SAM and a low ∆SAM had a median PFS and OS of 9.4 months and 32 months, whereas the other group had a median PFS of 14.7 months and a median OS which had not been reached (p = 0.002 for both PFS and OS).

CONCLUSION

(18)F-FDG PET imaging is a useful tool to assess treatment response and predict clinical outcome in patients with mCRC who undergo chemotherapy before liver metastasectomy. Follow-up SUVmax, follow-up SAM and ∆SAM were found to be significant prognostic factors for PFS and OS.