A statistical modeling approach to the analysis of spatial patterns of FDG-PET uptake in human sarcoma

Alveolar Rhabdomyosarcoma of Nasopharynx and Paranasal Sinuses in Children Diagnosis and Treatment-Review of the Literature and Case Report

Alveolar rhabdomyosarcoma (aRMS) is a rare pediatric malignant tumor with a poor prognosis, particularly when located in the rhinopharynx and sphenoidal floor, which complicates diagnosis and increases the risk of misclassification as benign growths. The specific genotype of aRMS is associated with a worse clinical outcome. In young children, especially those aged 4 to 12 years, rhinopharyngeal masses are often attributed to chronic adenoiditis; however, other benign (e.g., angiofibroma in boys) and malignant tumors may also be present. Initial symptoms frequently include nasal obstruction, muco-purulent nasal discharge, serous otitis media, sinusitis, and epistaxis. Rhabdomyosarcoma is the second most common ENT neoplasm in children, following lymphoma, with an incidence of approximately 6 cases per 1,000,000 annually. This report presents the case of an 8-year-old boy diagnosed with aRMS, accompanied by a literature review. Alveolar rhabdomyosarcoma should be suspected in children presenting with a vegetative tumor in the rhinopharynx or paranasal sinuses. Combined treatment approaches (surgery, radiotherapy, and chemotherapy) should be tailored to tumor characteristics. Neuronavigation-guided functional endoscopic sinus surgery (FESS) is an effective option for achieving complete tumor excision, depending on tumor size and extent. The prognosis remains reserved and is contingent upon accurate evaluation and timely intervention. Rigorous follow-up, including endoscopic and imagistic investigation, is crucial for early detection of recurrences, thereby improving treatment outcomes.

Role of Textural Analysis of Pretreatment 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Response Prediction in Esophageal Carcinoma Patients

Introduction

Positron emission tomography/computed tomography (PET/CT) is routinely used for staging, response assessment, and surveillance in esophageal carcinoma patients. The aim of this study was to investigate whether textural features of pretreatment 18F-fluorodeoxyglucose (18F-FDG) PET/CT images can contribute to prognosis prediction in carcinoma oesophagus patients.

Materials and Methods

This is a retrospective study of 30 diagnosed carcinoma esophagus patients. These patients underwent pretreatment 18F-FDG PET/CT for staging. The images were processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between progression group and nonprogression group. The original dataset was subject separately to receiver operating curve analysis. Receiver operating characteristic (ROC) curves were used to identify the cutoff values for textural features with a P < 0.05 for statistical significance. Feature selection was done with principal component analysis. The selected features of each evaluator were subject to 4 machine-learning algorithms. The highest area under the curve (AUC) values was selected for 10 features.

Results

A retrospective study of 30 primary carcinoma esophagus patients was done. Patients were followed up after chemo-radiotherapy and they underwent follow-up PET/CT. On the basis of their response, patients were divided into progression group and nonprogression group. Among them, 15 patients showed disease progression and 15 patients were in the nonprogression group. Ten textural analysis parameters turned out to be significant in the prediction of disease progression. Cutoff values were calculated for these parameters according to the ROC curves, GLZLM_long zone emphasis (Gray Level Zone Length Matrix)_long zone emphasis (44.9), GLZLM_low gray level zone emphasis (0.006), GLZLM_short zone low gray level emphasis (0.0032), GLZLM_long zone low gray level emphasis (0.185), GLRLM_long run emphasis (Gray Level Run Length Matrix) (1.31), GLRLM_low gray level run emphasis (0.0058), GLRLM_short run low gray level emphasis (0.005496), GLRLM_long run low gray level emphasis (0.00727), NGLDM_Busyness (Neighborhood Gray Level Difference Matrix) (0.75), and gray level co-occurrence matrix_homogeneity (0.37). Feature selection by principal components analysis and feature classification by the K-nearest neighbor machine-learning model using independent training and test samples yielded the overall highest AUC.

Conclusions

Textural analysis parameters could provide prognostic information in carcinoma esophagus patients. Larger multicenter studies are needed for better clinical prognostication of these parameters.

Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients

Purpose: Radiomics have become invaluable for non-invasive cancer patient risk prediction, and the community now turns to exogenous assessment, e.g., from genomics, for interpretability of these agnostic analyses. Yet, some opportunities for clinically interpretable modeling of positron emission tomography (PET) imaging data remain unexplored, that could facilitate insightful characterization at voxel level. Approach: Here, we present a novel deformable tubular representation of the distribution of tracer uptake within a volume of interest, and derive interpretable prognostic summaries from it. This data-adaptive strategy yields a 3D-coherent and smooth model fit, and a profile curve describing tracer uptake as a function of voxel location within the volume. Local trends in uptake rates are assessed at each voxel via the calculation of gradients derived from this curve. Intratumoral heterogeneity can also be assessed directly from it. Results: We illustrate the added value of this approach over previous strategies, in terms of volume rendering and coherence of the structural representation of the data. We further demonstrate consistency of the implementation via simulations, and prognostic potential of heterogeneity and statistical summaries of the uptake gradients derived from the model on a clinical cohort of 158 sarcoma patients imaged withF 18 -fluorodeoxyglucose-PET, in multivariate prognostic models of patient survival. Conclusions: The proposed approach captures uptake characteristics consistently at any location, and yields a description of variations in uptake that holds prognostic value complementarily to structural heterogeneity. This creates opportunities for monitoring of local areas of greater interest within a tumor, e.g., to assess therapeutic response in avid locations.

Assessment of different quantification metrics of [18F]-NaF PET/CT images of patients with abdominal aortic aneurysm

BACKGROUND

We aim to assess the spill-in effect and the benefit in quantitative accuracy for [18F]-NaF PET/CT imaging of abdominal aortic aneurysms (AAA) using the background correction (BC) technique.

METHODS

Seventy-two datasets of patients diagnosed with AAA were reconstructed with ordered subset expectation maximization algorithm incorporating point spread function (PSF). Spill-in effect was investigated for the entire aneurysm (AAA), and part of the aneurysm excluding the region close to the bone (AAAexc). Quantifications of PSF and PSF+BC images using different thresholds (% of max. SUV in target regions-of-interest) to derive target-to-background (TBR) values (TBRmax, TBR90, TBR70 and TBR50) were compared at 3 and 10 iterations.

RESULTS

TBR differences were observed between AAA and AAAexc due to spill-in effect from the bone into the aneurysm. TBRmax showed the highest sensitivity to the spill-in effect while TBR50 showed the least. The spill-in effect was reduced at 10 iterations compared to 3 iterations, but at the expense of reduced contrast-to-noise ratio (CNR). TBR50 yielded the best trade-off between increased CNR and reduced spill-in effect. PSF+BC method reduced TBR sensitivity to spill-in effect, especially at 3 iterations, compared to PSF (P-value ≤ 0.05).

CONCLUSION

TBR50 is robust metric for reduced spill-in and increased CNR.

Combining structural and textural assessments of volumetric FDG-PET uptake in NSCLC

Numerous studies have reported the prognostic utility of texture analyses and the effectiveness of radiomics in PET and PET/CT assessment of non-small cell lung cancer (NSCLC). Here we explore the potential, relative to this methodology, of an alternative model-based approach to tumour characterization, which was successfully applied to sarcoma in previous works. The spatial distribution of 3D FDG-PET uptake is evaluated in the spatial referential determined by the best-fitting ellipsoidal pattern, which provides a univariate uptake profile function of the radial position of intratumoral voxels. A group of structural features is extracted from this fit that include two heterogeneity variables and statistical summaries of local metabolic gradients. We demonstrate that these variables capture aspects of tumour metabolism that are separate to those described by conventional texture features. Prognostic model selection is performed in terms of a number of classifiers, including stepwise selection of logistic models, LASSO, random forests and neural networks with respect to two-year survival status. Our results for a cohort of 93 NSCLC patients show that structural variables have significant prognostic potential, and that they may be used in conjunction with texture features in a traditional radiomics sense, towards improved baseline multivariate models of patient overall survival. The statistical significance of these models also demonstrates the relevance of these machine learning classifiers for prognostic variable selection.

Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma

Intratumoral heterogeneity biomarkers derived from positron emission tomography (PET) imaging with fluorodeoxyglucose (FDG) are of interest for a number of cancers, including sarcoma. A range of radiomic texture variables, adapted from general methodologies for image analysis, has shown promise in the setting. In the context of sarcoma, our group introduced an alternative model-based approach to the measurement of heterogeneity. In this approach, the heterogeneity of a tumor is characterized by the extent to which the 3-D FDG uptake pattern deviates from a simple elliptically contoured structure. By using a nonparametric analysis of the uptake profile obtained from this spatial model, a variable assessing the metabolic gradient of the tumor is developed. The work explores the prognostic potential of this new variable in the context of FDG-PET imaging of sarcoma. A mature clinical series involving 197 patients, 88 of whom have complete time-to-death information, is used. Texture variables based on the imaging data are also evaluated in this series and a range of appropriate machine learning methodologies are then used to explore the complementary prognostic roles for structure and texture variables. We conclude that both texture-based and model-based variables can be combined to achieve enhanced prognostic assessments of outcome for patients with sarcoma based on FDG-PET imaging information.

Towards enhanced PET quantification in clinical oncology

Positron emission tomography (PET) has, since its inception, established itself as the imaging modality of choice for the in vivo quantitative assessment of molecular targets in a wide range of biochemical processes underlying tumour physiology. PET image quantification enables to ascertain a direct link between the time-varying activity concentration in organs/tissues and the fundamental parameters portraying the biological processes at the cellular level being assessed. However, the quantitative potential of PET may be affected by a number of factors related to physical effects, hardware and software system specifications, tracer kinetics, motion, scan protocol design and limitations in current image-derived PET metrics. Given the relatively large number of PET metrics reported in the literature, the selection of the best metric for fulfilling a specific task in a particular application is still a matter of debate. Quantitative PET has advanced elegantly during the last two decades and is now reaching the maturity required for clinical exploitation, particularly in oncology where it has the capability to open many avenues for clinical diagnosis, assessment of response to treatment and therapy planning. Therefore, the preservation and further enhancement of the quantitative features of PET imaging is crucial to ensure that the full clinical value of PET imaging modality is utilized in clinical oncology. Recent advancements in PET technology and methodology have paved the way for faster PET acquisitions of enhanced sensitivity to support the clinical translation of highly quantitative four-dimensional (4D) parametric imaging methods in clinical oncology. In this report, we provide an overview of recent advances and future trends in quantitative PET imaging in the context of clinical oncology. The pros/cons of the various image-derived PET metrics will be discussed and the promise of novel methodologies will be highlighted.

Prognostic value of FDG-PET indices for the assessment of histological response to neoadjuvant chemotherapy and outcome in pediatric patients with Ewing sarcoma and osteosarcoma

Purpose

The objective of this retrospective work was to evaluate the prognostic value on histological response and survival of quantitative indices derived from FDG-PET performed before and after chemotherapy (CHT), in a homogeneous pediatric Ewing sarcoma (EWS) and Osteosarcoma (OST) population.

Methods

Thirty-one patients with EWS and 31 with OST were included. All patients were treated with neoadjuvant CHT, and underwent surgery for local control. All patients had FDG-PET at diagnosis and after CHT, prior to surgery. Several parameters were evaluated: SUVmax, SUVpeak, SUVmean, metabolic tumor volume, total lesion glycolysis, 7 textural features and 3 shape features (SF). The segmentation was performed using an adaptive approach. Results were compared to histopathological regression of the resected tumor and to clinical follow-up for survival evaluation.

Results

For EWS, univariate analysis did not highlight any prognostic value on histological response, or survival regardless of all the considered metrics. For OST, only one of the SF, namely elongation, was significantly associated with PFS and OS on both univariate and multivariate analysis (PFS: p = 0.019, HR = 5.583; OS: p = 0.0062, HR = 7.113).

Conclusion

Only elongation determined on initial FDG-PET has a potential interest as a prognostic factor of PFS and OS in pediatric OST patients. Unlike recent studies of the literature realized in adult population, all the metrics reveal limited additional prognostic value in pediatric EWS patients. This seems to reinforce the question of whether children experience different subtypes of the same pathologies than older patients, with different outcomes.

Statistical assessment of treatment response in a cancer patient based on pre-therapy and post-therapy FDG-PET scans

This work arises from consideration of sarcoma patients in which fluorodeoxyglucose positron emission tomography (FDG-PET) imaging pre-therapy and post-chemotherapy is used to assess treatment response. Our focus is on methods for evaluation of the statistical uncertainty in the measured response for an individual patient. The gamma distribution is often used to describe data with constant coefficient of variation, but it can be adapted to describe the pseudo-Poisson character of PET measurements. We propose co-registering the pre-therapy and post- therapy images and modeling the approximately paired voxel-level data using the gamma statistics. Expressions for the estimation of the treatment effect and its variability are provided. Simulation studies explore the performance in the context of testing for a treatment effect. The impact of misregistration errors and how test power is affected by estimation of variability using simplified sampling assumptions, as might be produced by direct bootstrapping, is also clarified. The results illustrate a marked benefit in using a properly constructed paired approach. Remarkably, the power of the paired analysis is maintained even if the pre-image and post- image data are poorly registered. A theoretical explanation for this is indicated. The methodology is further illustrated in the context of a series of fluorodeoxyglucose-PET sarcoma patient studies. These data demonstrate the additional prognostic value of the proposed treatment effect test statistic. Copyright © 2016 John Wiley & Sons, Ltd.

Rhabdomyosarcoma in adults: new perspectives on therapy

OPINION STATEMENT

Rhabdomyosarcoma (RMS) is well known as a pediatric disease. Most of the knowledge, like biology, genetics, and treatments of this disease, comes from studies done in that age group. The two subtypes of RMS, embryonic RMS and alveolar RMS, that affect mainly the pediatric population are well described in the literature and that has had an impact on the improvement in overall survival during the past 20 years. RMS in the adult population has a low incidence, therefor the study of RMS in this group is challenging. Pleomorphic RMS is the subtype that mainly affects adults and its biology and genetics are not yet completely understood and described. The risk factors for this tumor and the differences among adults and children is also poorly understood. The treatments for adults that have RMS are not standardized having an impact on the overall survival. Pleomorphic RMS has, compared to other adult sarcomas, poor overall survival. Adult patients with RMS have poor prognosis. The standardization of treatments for the adult population is necessary as maybe new treatments for this specific group. There are new treatment options that are being studied mostly in pediatrics and young adults. Immunotherapy is currently proposed as an important treatment possibility including different techniques like vaccination, antigen-mediated therapy, and immune checkpoints. Even if we have a better understanding of RMS, there are still unanswered questions. The improvements seen in the pediatric population are encouraging, but there is still the need to enhance better therapies for adults with RMS.

An update on novel quantitative techniques in the context of evolving whole-body PET imaging

Since its foundation PET has established itself as one of the standard imaging modalities enabling the quantitative assessment of molecular targets in vivo. In the past two decades, quantitative PET has become a necessity in clinical oncology. Despite introduction of various measures for quantification and correction of PET parameters, there is debate on the selection of the appropriate methodology in specific diseases and conditions. In this review, we have focused on these techniques with special attention to topics such as static and dynamic whole body PET imaging, tracer kinetic modeling, global disease burden, texture analysis and radiomics, dual time point imaging and partial volume correction.

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

BACKGROUND

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

METHODOLOGY

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

PRINCIPAL FINDINGS

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

CONCLUSIONS

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

Prognostic value of pretreatment 18F-FDG PET/CT parameters including visual evaluation in patients with head and neck squamous cell carcinoma

OBJECTIVE

The purpose of this study was to determine whether pretreatment quantitative and visual parameters seen on PET/CT using (18)F-FDG add prognostic information for clinical staging in patients with head and neck cancer.

MATERIALS AND METHODS

We enrolled 108 patients with histologically proven oral, oropharyngeal, hypopharyngeal, and laryngeal squamous cell carcinomas who underwent FDG PET/CT before treatment and, later, definitive therapy in our study. PET/CT parameters-maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and uptake pattern (sphere-shaped or ring-shaped)-were recorded. The prognostic value of these parameters was evaluated using univariate and multivariate Cox regression analyses.

RESULTS

In the univariate analysis, all of the FDG PET/CT parameters--SUVmax (> 10 g/mL) of the primary tumor, MTV (> 20 cm(3)), TLG (> 70 g), and uptake pattern (ring-shaped)--were significantly associated with negative effects on disease-specific survival (DSS) and disease-free survival (DFS). In the multivariate analysis, the MTV and uptake pattern remained associated with DSS after corrections for the Union for International Cancer Control (UICC) stage and definitive therapy (p = 0.023 and < 0.001, respectively). Another multivariate model that included MTV as a continuous variable, uptake pattern, and UICC stage showed that the uptake pattern remained significantly associated with DSS, whereas the association between DSS and MTV was not significant (p < 0.001 and = 0.332, respectively).

CONCLUSION

Our data indicate that the pretreatment PET/CT parameters had prognostic value. In particular, a qualitative factor, uptake pattern, provided better prognostic information to the clinical staging of head and neck squamous cell carcinomas than the other PET/CT parameters.

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

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

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

TRANSLATIONAL RELEVANCE

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

PURPOSE

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

EXPERIMENTAL DESIGN

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

RESULTS

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

CONCLUSIONS

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

Tumor quantification in clinical positron emission tomography

Positron emission tomography (PET) is used extensively in clinical oncology for tumor detection, staging and therapy response assessment. Quantitative measurements of tumor uptake, usually in the form of standardized uptake values (SUVs), have enhanced or replaced qualitative interpretation. In this paper we review the current status of tumor quantification methods and their applications to clinical oncology. Factors that impede quantitative assessment and limit its accuracy and reproducibility are summarized, with special emphasis on SUV analysis. We describe current efforts to improve the accuracy of tumor uptake measurements, characterize overall metabolic tumor burden and heterogeneity of tumor uptake, and account for the effects of image noise. We also summarize recent developments in PET instrumentation and image reconstruction and their impact on tumor quantification. Finally, we offer our assessment of the current development needs in PET tumor quantification, including practical techniques for fully quantitative, pharmacokinetic measurements.

Regional compensation for statistical maximum likelihood reconstruction error of PET image pixels

In positron emission tomography (PET), there is an increasing interest in studying not only the regional mean tracer concentration, but its variation arising from local differences in physiology, the tissue heterogeneity. However, in reconstructed images this physiological variation is shadowed by a large reconstruction error, which is caused by noisy data and the inversion of tomographic problem. We present a new procedure which can quantify the error variation in regional reconstructed values for given PET measurement, and reveal the remaining tissue heterogeneity. The error quantification is made by creating and reconstructing the noise realizations of virtual sinograms, which are statistically similar with the measured sinogram. Tests with physical phantom data show that the characterization of error variation and the true heterogeneity are possible, despite the existing model error when real measurement is considered.

Update on rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a malignant childhood tumor of mesenchymal origin that currently has a greater than 70% overall 5-year survival. Multimodality treatment is determined by risk stratification according to pretreatment stage, postoperative group, histology, and site of the primary tumor. Pretreatment staging is dependent on primary tumor site, size, regional lymph node status, and presence of metastases. Unique to RMS is the concept of postoperative clinical grouping that assesses the completeness of disease resection and takes into account lymph node evaluation. At all tumor sites, the clinical grouping, and therefore completeness of resection, is an independent predictor of outcome. Overall, the prognosis for RMS is dependent on primary tumor site, patient age, completeness of resection, extent of disease, including the presence and number of metastatic sites and histology and biology of the tumor cells. Therefore, the surgeon plays a vital role in RMS by contributing to risk stratification for treatment, local control of the primary tumor, and outcome. The current state-of-the-art treatment is determined by treatment protocols developed by the Soft Tissue Sarcoma Committee of the children's Oncology Group.