Matthew J. Oborski

Malignant gliomas: current perspectives in diagnosis, treatment, and early response assessment using advanced quantitative imaging methods

Malignant gliomas consist of glioblastomas, anaplastic astrocytomas, anaplastic oligodendrogliomas and anaplastic oligoastrocytomas, and some less common tumors such as anaplastic ependymomas and anaplastic gangliogliomas. Malignant gliomas have high morbidity and mortality. Even with optimal treatment, median survival is only 12–15 months for glioblastomas and 2–5 years for anaplastic gliomas. However, recent advances in imaging and quantitative analysis of image data have led to earlier diagnosis of tumors and tumor response to therapy, providing oncologists with a greater time window for therapy management. In addition, improved understanding of tumor biology, genetics, and resistance mechanisms has enhanced surgical techniques, chemotherapy methods, and radiotherapy administration. After proper diagnosis and institution of appropriate therapy, there is now a vital need for quantitative methods that can sensitively detect malignant glioma response to therapy at early follow-up times, when changes in management of nonresponders can have its greatest effect. Currently, response is largely evaluated by measuring magnetic resonance contrast and size change, but this approach does not take into account the key biologic steps that precede tumor size reduction. Molecular imaging is ideally suited to measuring early response by quantifying cellular metabolism, proliferation, and apoptosis, activities altered early in treatment. We expect that successful integration of quantitative imaging biomarker assessment into the early phase of clinical trials could provide a novel approach for testing new therapies, and importantly, for facilitating patient management, sparing patients from weeks or months of toxicity and ineffective treatment. This review will present an overview of epidemiology, molecular pathogenesis and current advances in diagnoses, and management of malignant gliomas.

First use of (18)F-labeled ML-10 PET to assess apoptosis change in a newly diagnosed glioblastoma multiforme patient before and early after therapy.

The authors present the first use of the novel positron emission tomography (PET) apoptosis tracer 18F‐labeled 2‐(5‐fluoro‐pentyl)‐2‐methyl‐malonic acid (18F‐ML‐10) for early‐therapy response assessment of a newly diagnosed glioblastoma multiforme (GBM) patient.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T1, were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Improved Benefit of SPECT/CT Compared to SPECT Alone for the Accurate Localization of Endocrine and Neuroendocrine Tumors

Objective: To assess the clinical utility of SPECT/ CT in subjects with endocrine and neuroendocrine tumors compared to SPECT alone. Material and Methods: 48 subjects (31 women;17 men; mean age 54±11) with clinical suspicion or diagnosis of endocrine and neuroendocrine tumor had 50 SPECT/CT scans (32 Tc-99m MIBI, 5 post treatment I-131, 8 In-111 Pentetreotide, and 5 I-123 MIBG). SPECT alone findings were compared to SPECT/CT and to pathology or radiological follow up. Results: From the 32 Tc-99m MIBI scans, SPECT accurately localized the lesion in 22 positive subjects while SPECT/CT did in 31 subjects. Parathyroid lesions not seen on SPECT alone were smaller than 10 mm. In five post treatment I-131 scans, SPECT alone neither characterized, nor localized any lesions accurately. SPECT/CT revealed 3 benign etiologies, a metastatic lymph node, and one equivocal lesion. In 8 In-111 Pentetreotide scans, SPECT alone could not localize primary or metastatic lesions in 6 subjects all of which were localized with SPECT/CT. In five I-123 MIBG scans, SPECT alone could not detect a 1.1 cm adrenal lesion or correctly characterize normal physiologic adrenal uptake in consecutive scans of the same patient with prior history of adrenelectomy, all of which were correctly localized and characterized with SPECT/CT. Conclusion: SPECT/CT is superior to SPECT alone in the assessment of endocrine and neuroendocrine tumors. It is better in lesion localization and lesion characterization leading to a decrease in the number of equivocal findings. SPECT/CT should be included in the clinical work up of all patients with diagnosis or suspicion of endocrine and neuroendocrine tumors. Conflict of interest:None declared.

Combined imaging biomarkers for therapy evaluation in glioblastoma multiforme: correlating sodium MRI and F-18 FLT PET on a voxel-wise basis

We evaluate novel magnetic resonance imaging (MRI) and positron emission tomography (PET) quantitative imaging biomarkers and associated multimodality, serial-time-point analysis methodologies, with the ultimate aim of providing clinically feasible, predictive measures for early assessment of response to cancer therapy. A focus of this work is method development and an investigation of the relationship between the information content of the two modalities. Imaging studies were conducted on subjects who were enrolled in glioblastoma multiforme (GBM) therapeutic clinical trials. Data were acquired, analyzed and displayed using methods that could be adapted for clinical use. Subjects underwent dynamic [(18)F]fluorothymidine (F-18 FLT) PET, sodium ((23)Na) MRI and 3-T structural MRI scans at baseline (before initiation of therapy), at an early time point after beginning therapy and at a late follow-up time point after therapy. Sodium MRI and F-18 FLT PET images were registered to the structural MRI. F-18 FLT PET tracer distribution volumes and sodium MRI concentrations were calculated on a voxel-wise basis to address the heterogeneity of tumor physiology. Changes in, and differences between, these quantities as a function of scan timing were tracked. While both modalities independently show a change in tissue status as a function of scan time point, results illustrate that the two modalities may provide complementary information regarding tumor progression and response. Additionally, tumor status changes were found to vary in different regions of tumor. The degree to which these methods are useful for GBM therapy response assessment and particularly for differentiating true progression from pseudoprogression requires additional patient data and correlation of these imaging biomarker changes with clinical outcome.

Quantifying metabolic heterogeneity in head and neck tumors in real time: 2-DG uptake is highest in hypoxic tumor regions.

Purpose Intratumoral metabolic heterogeneity may increase the likelihood of treatment failure due to the presence of a subset of resistant tumor cells. Using a head and neck squamous cell carcinoma (HNSCC) xenograft model and a real-time fluorescence imaging approach, we tested the hypothesis that tumors are metabolically heterogeneous, and that tumor hypoxia alters patterns of glucose uptake within the tumor. Experimental Design Cal33 cells were grown as xenograft tumors (n = 16) in nude mice after identification of this cell lines metabolic response to hypoxia. Tumor uptake of fluorescent markers identifying hypoxia, glucose import, or vascularity was imaged simultaneously using fluorescent molecular tomography. The variability of intratumoral 2-deoxyglucose (IR800-2-DG) concentration was used to assess tumor metabolic heterogeneity, which was further investigated using immunohistochemistry for expression of key metabolic enzymes. HNSCC tumors in patients were assessed for intratumoral variability of 18F-fluorodeoxyglucose (18F-FDG) uptake in clinical PET scans. Results IR800-2-DG uptake in hypoxic regions of Cal33 tumors was 2.04 times higher compared to the whole tumor (p = 0.0001). IR800-2-DG uptake in tumors containing hypoxic regions was more heterogeneous as compared to tumors lacking a hypoxic signal. Immunohistochemistry staining for HIF-1α, carbonic anhydrase 9, and ATP synthase subunit 5β confirmed xenograft metabolic heterogeneity. We detected heterogeneous 18F-FDG uptake within patient HNSCC tumors, and the degree of heterogeneity varied amongst tumors. Conclusion Hypoxia is associated with increased intratumoral metabolic heterogeneity. 18F-FDG PET scans may be used to stratify patients according to the metabolic heterogeneity within their tumors, which could be an indicator of prognosis.

Assessment of early therapy response with 18F-FLT PET in glioblastoma multiforme.

Early therapy response assessment in glioblastoma multiforme remains a challenge. Evaluation by MRI relies on changes in tumor contrast enhancement or size, which are usually not visible at early therapy response assessment times. In addition, MRI may not be reliable for early therapy response assessment if only molecular changes have occurred. PET with F-FLT, a tracer associated with cellular proliferation, has been proposed as a potential method of early therapy response assessment and is an area of active research. We present a case where early response assessment with F-FLT PET was associated with a favorable 1-year follow-up outcome.

Distinguishing pseudoprogression from progression in high-grade gliomas: a brief review of current clinical practice and demonstration of the potential value of 18F-FDG PET.

We report a case in which 18F-FDG PET was able to discriminate pseudoprogression from progression observed on contrast-enhanced (CE) MRI (CE-MRI). A 56-year-old male patient with anaplastic oligodendroglioma demonstrated markedly increased tumor enhancement on CE-MRI 1 month after completing radiation therapy (RT), suggesting radiological progression. However, the patient was clinically improved and therefore received an early-therapy response assessment PET to assess for pseudoprogression. PET showed low tumor uptake indicating stable disease. Follow-up CE-MRI at 3 and 4 months post-RT confirmed stable disease. This case emphasizes the value of 18F-FDG PET when pseudoprogression is clinically suspected.

Multi‐site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data

Purpose: Radiomics utilizes a large number of image‐derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image‐based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi‐institutional trials and clinical oncology decision making. Methods: To assess segmentation quality and consistency at the multi‐institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. Results: On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between −36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between −50.5 to 701.5%, and the repeat difference for each institution ranged between −37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively. Conclusion: Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi‐site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Brain imaging: assessing therapy responses using quantitative imaging biomarkers

Abstract: Neuroimaging is essential for both the diagnosis and evaluation of the therapeutic response of intracranial tumors. Measurement of structural changes in tumor morphology after institution of therapy using magnetic resonance imaging (MRI) serves as the current gold standard for monitoring therapeutic response; however, information of prognostic value cannot be obtained until weeks after the initiation of treatment. Positron emission tomography (PET) using radiolabled ligands that trace physiologic processes offers promise as a method for early therapy response assessment, although to date the neuro-oncologic utility of PET imaging has not been good enough to replace the current gold standard. This chapter first reviews some of the current methods in which MR is used to evaluate physiologic changes in tumor environment after therapy (e.g. spectroscopy and diffusion-weighted imaging). The chapter then surveys several PET tracers that are either currently in clinical use (e.g. FDG) or under investigation (e.g. FLT) and discusses their use for therapeutic response assessment.