Differentiation Of Brain Tumors Research Articles

Comparative analysis of GPT-4-based ChatGPT's diagnostic performance with radiologists using real-world radiology reports of brain tumors.

Large language models like GPT-4 have demonstrated potential for diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals. This study aimed to assess the diagnostic capabilities of GPT-4-based Chat Generative Pre-trained Transformer (ChatGPT) using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists. We collected brain MRI reports written in Japanese from preoperative brain tumor patients at two institutions from January 2017 to December 2021. The MRI reports were translated into English by radiologists. GPT-4 and five radiologists were presented with the same textual findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. McNemar's test and Fisher's exact test were used for statistical analysis. In a study analyzing 150 radiological reports, GPT-4 achieved a final diagnostic accuracy of 73%, while radiologists' accuracy ranged from 65 to 79%. GPT-4's final diagnostic accuracy using reports from neuroradiologists was higher at 80%, compared to 60% using those from general radiologists. In the realm of differential diagnoses, GPT-4's accuracy was 94%, while radiologists' fell between 73 and 89%. Notably, for these differential diagnoses, GPT-4's accuracy remained consistent whether reports were from neuroradiologists or general radiologists. GPT-4 exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. GPT-4 can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents. This study evaluated GPT-4-based ChatGPT's diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists. We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors. GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists. GPT-4 has the potential to improve the diagnostic process in clinical radiology.

The impact of sampling time point on the lipidome composition

Lipidomic profiling has been reported as an effective approach for characterizing and differentiating brain tumors. However, since lipids can undergo non-specific enzymatic and nonenzymatic reactions due to tissue disruption, it is critical to consider the preanalytical phase of the diagnostic process (e.g., optimizing the sampling time and sampling conditions). Thus, this study assesses the ways in which the time point of sampling impacts the lipidome composition of brain tumors. Two histologically distinct brain tumors—namely, meningiomas and gliomas—were sampled using solid-phase microextraction (SPME) fibers at two time points: on-site directly after removal, and after 12 months of storage at −30 °C. The samples were analyzed via HILIC chromatography coupled with HRMS, which enabled the detection of a wide range of features, including phospholipids and sphingolipids, as well as changes in the profiles of these compounds. The samples obtained from the stored tissues tended to have elevated levels of analytes with lower m/z values. In addition, the samples obtained from the fresh and stored tissues were easily distinguished based on their lipidome compositions, regardless of the histological tumor type. Notably, while storage did not affect the possibility of differentiating meningiomas and gliomas, the biological interpretation of the obtained results were prone to bias.

Application of 5T glutamate chemical exchange saturation transfer imaging in brain tumors: preliminary results.

Glutamate chemical exchange saturation transfer (GluCEST) is a non-invasive CEST imaging technique for detecting glutamate levels in tissues. We aimed to investigate the reproducibility of the 5T GluCEST technique in healthy volunteers and preliminarily explore its potential clinical application in patients with brain tumors. Ten volunteers (4 males, mean age 29years) underwent three 5T GluCEST imaging scans. The reproducibility of the three imaging GluCEST measurements was assessed using one-way repeated measures analysis of variance (ANOVA), generalized estimating equations, and linear mixed models. Twenty-eight patients with brain tumors (10 males, mean age 54years) underwent a single GluCEST scan preoperatively, and t-tests were used to compare the differences in GluCEST values between different brain tumors. In addition, the diagnostic accuracy of GluCEST values in differentiating brain tumors was assessed using the receiver work characteristics (ROC) curve. The coefficients of variation of GluCEST values in healthy volunteers were less than 5% for intra-day, inter-day, and within-subjects and less than 10% for between-subjects. High-grade gliomas (HGG) had higher GluCEST values compared to low-grade gliomas (LGG) (P < 0.001). In addition, cerebellopontine angle (CPA) meningiomas had higher GluCEST values than acoustic neuromas (P < 0.001). The area under the curve (AUC) of the GluCEST value for differentiating CPA meningioma from acoustic neuroma was 0.93. 5T GluCEST images are highly reproducible in healthy brains. In addition, the 5T GluCEST technique has potential clinical applications in differentiating LGG from HGG and CPA meningiomas from acoustic neuromas.

An Illustrated Scoping Review of the Magnetic Resonance Imaging Characteristics of Canine and Feline Brain Tumors.

Magnetic resonance imaging (MRI) is used pervasively in veterinary practice for the antemortem diagnosis of intracranial tumors. Here, we provide an illustrated summary of the published MRI features of primary and secondary intracranial tumors of dogs and cats, following PRISMA scoping review guidelines. The PubMed and Web of Science databases were searched for relevant records, and input from stakeholders was solicited to select data for extraction. Sixty-seven studies of moderate to low-level evidence quality describing the MRI features of pathologically confirmed canine and feline brain tumors met inclusion criteria. Considerable variability in data inclusion and reporting, as well as low case numbers, prohibited comparative data analyses. Available data support a holistic MRI approach incorporating lesion number, location within the brain, shape, intrinsic signal appearances on multiparametric sequences, patterns of contrast enhancement, and associated secondary changes in the brain to prioritize differential imaging diagnoses, and often allows for accurate presumptive diagnosis of common intracranial tumors. Quantitative MRI techniques show promise for improving discrimination of neoplastic from non-neoplastic brain lesions, as well as differentiating brain tumor types and grades, but sample size limitations will likely remain a significant practical obstacle to the design of robustly powered radiomic studies. For many brain tumor variants, particularly in cats, there remains a need for standardized studies that correlate clinicopathologic and neuroimaging data.

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features.

Brain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient's life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm. This paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB). The presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models. By analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.

Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models

BackgroundIt remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting. MethodsMulti-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow). ResultsFor single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions. ConclusionA general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.

Selective Cell Size MRI Differentiates Brain Tumors from Radiation Necrosis.

This work introduces and provides preclinical validation of a new diffusion MRI method that exploits intrinsic differences in cell sizes to distinguish brain tumors and radiotherapy necrosis.

The Value of APTw CEST MRI in Routine Clinical Assessment of Human Brain Tumor Patients at 3T.

Background. With fast-growing evidence in literature for clinical applications of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), this prospective study aimed at applying amide proton transfer-weighted (APTw) CEST imaging in a clinical setting to assess its diagnostic potential in differentiation of intracranial tumors at 3 tesla (T). Methods. Using the asymmetry magnetization transfer ratio (MTRasym) analysis, CEST signals were quantitatively investigated in the tumor areas and in a similar sized region of the normal-appearing white matter (NAWM) on the contralateral hemisphere of 27 patients with intracranial tumors. Area under curve (AUC) analyses were used and results were compared to perfusion-weighted imaging (PWI). Results. Using APTw CEST, contrast-enhancing tumor areas showed significantly higher APTw CEST metrics than contralateral NAWM (AUC = 0.82; p < 0.01). In subgroup analyses of each tumor entity vs. NAWM, statistically significant effects were yielded for glioblastomas (AUC = 0.96; p < 0.01) and for meningiomas (AUC = 1.0; p < 0.01) but not for lymphomas as well as metastases (p > 0.05). PWI showed results comparable to APTw CEST in glioblastoma (p < 0.01). Conclusions. This prospective study confirmed the high diagnostic potential of APTw CEST imaging in a routine clinical setting to differentiate brain tumors.

Albumin-Based Nanotheranostic Probe with Hypoxia Alleviating Potentiates Synchronous Multimodal Imaging and Phototherapy for Glioma.

Highly infiltrative and invasive glioma cells obscure the boundary between tumor and normal brain tissue, making it extremely difficult to precisely diagnose and completely remove. The combination of multimodal imaging with effective treatments to diagnose precisely and guide surgery and therapy accurately is desperately needed for glioma in the brain. Here, we report a biomimetic catalase-integrated-albumin phototheranostic nanoprobe (ICG/AuNR@BCNP) to realize multimodal imaging, amplify phototherapy, and guide surgery for glioma after penetrating the blood-brain barrier, accumulating into deep-seated glioma via albumin-binding protein mediated transportation. The phototheranostic nanoprobe enabled fluorescence, photoacoustic, and infrared thermal imaging with desirable detecting depth and high signal-to-background ratio for clearly differentiating brain tumors from surrounding tissues. Meanwhile, the nanoprobe could effectively induce local hyperthermia and promote the level of singlet oxygen based on alleviated hypoxic glioma microenvironment by decomposing endogenous hydrogen peroxide to oxygen to amplify phototherapy. Thus, significant inhibition of glioma growth, extended survival time, alleviated tumor hypoxia, improved apoptosis, and antiangiogenesis effects were exhibited in several animal models including the periphery and the brain through intravenous or intratumoral injection, meanwhile with low toxicity to normal tissue. The phototherapy was also guided by the assistance of external bioluminescence, magnetic resonance, and positron emission tomography imaging. Moreover, the nanoprobe could accurately guide the glioma resection. These results suggest that the phototheranostic nanoprobe is a promising nanoplatform specifically for glioma to achieve multimodal diagnosis, effective phototherapy, and accurate imaging-guided surgery.

Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?

Background:Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions.Methods:In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI. Results:The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912). Conclusion:The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.

Automatic Detection Brain Segmentation to Detect Brain Tumor Using MRI

Brain tumors are a type of disease in the form of lumps of meat that grow in the brain. In differentiating brain tumor tissue from normal tissue become a difficulty caused by the same colors are an obstacle in seeing brain tumors using MRI images. Accuracy is needed in analyzing brain tumors. However, currently, radiographers (radiologists) still analyze the results of manual MRI images of brain tumors. Therefore we need a method that is able to segment MRI images precisely and automatically, with the aim of obtaining faster and more accurate image segmentation of brain tumors so that we can know the percentage of brain tumors found in the brain. To overcome difficulties when segmenting brain tumors in separating brain tumor tissue from other tissues such as normal brain tissue, cerebrospinal fluid, fat, and edema, a learning-based system method that will carry out the training process uses Haar training to narrow the MRI image so that it is more focused on the part of the head object. Then median filtering is performed to maintain the edge of the image on the MRI image. Then the segmentation process using the thresholding method is run, then repeated to take the largest area. Segmentation of brain is carried out by marking the brain area and the area outside the brain using the DAS method and then cleaning the skull using the cropping method. In this research, 12 images of MRI brain tumors were used. The results of segmentation compared to area of the brain tumor and area of the brain tissue. The system obtains a calculation of the tumor area having an average error of 10,5%.

Diffusion tensor imaging in brain tumors: The role of fractional anisotropy values

Aim: To evaluate the role of Fractional anisotropy (FA) values obtained from diffusion tensor magnetic resonance imaging (DTI) in the differentiation and grading of brain tumors.Materials and Methods: This study examined the conventional and diffusion tensor MR imaging findings of twenty-seven patients diagnosed with brain tumors between 2008 and 2010. Patients were divided into four groups based on tumor types; meningiomas, low-grade gliomas, high-grade gliomas, and metastases. Fractional anisotropy (FA) values were then obtained from the solid components and (if present) peritumoral vasogenic edema of the tumors for each patient by using the region of interest (ROI) method. Finally, the patient groups were analyzed in terms of any statistically significant differences.Results: The FA values obtained from the solid portions and peritumoral edema of meningiomas were found to be higher than those of all other groups (p&amp;lt;0.015). Moreover, the FA values of high-grade gliomas were found to be higher than those of low-grade gliomas (p=0.042). Finally, no statistically significant difference was observed between high-grade gliomas and metastases in terms of the FA values of solid components and peritumoral edema. Conclusion: The determination of FA values among DTI results can be a useful method for differentiating brain tumors such as meningioma, low-grade glioma, high-grade glioma, and metastasis, as the treatment protocols and prognoses of each may differ. Moreover, FA values may contribute preoperatively to the differentiation of brain tumors in multimodal brain tumor imaging. It would be useful to use diffusion tensor imaging in conjunction with conventional MRI in the imaging of brain tumors.

Multi‐Scale 3D U‐Nets: An approach to automatic segmentation of brain tumor

AbstractGliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi‐Scale 3D U‐Nets architecture, which uses several U‐net blocks to capture long‐distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state‐of‐the‐art methods.

Added Value of Spectroscopy to Perfusion MRI in the Differential Diagnostic Performance of Common Malignant Brain Tumors.

Perfusion and spectroscopic MR imaging provide noninvasive physiologic and metabolic characterization of tissues, which can help in differentiating brain tumors. We investigated the diagnostic role of perfusion and spectroscopic MR imaging using individual and combined classifiers of these modalities and assessed the added performance value that spectroscopy can provide to perfusion using optimal combined classifiers that have the highest differential diagnostic performance to discriminate lymphomas, glioblastomas, and metastases. From January 2013 to January 2016, fifty-five consecutive patients with histopathologically proved lymphomas, glioblastomas, and metastases were included after undergoing MR imaging. The perfusion parameters (maximum relative CBV, maximum percentage of signal intensity recovery) and spectroscopic concentration ratios (lactate/Cr, Cho/NAA, Cho/Cr, and lipids/Cr) were analyzed individually and in optimal combinations. Differences among tumor groups, differential diagnostic performance, and differences in discriminatory performance of models with quantification of the added performance value of spectroscopy to perfusion were tested using 1-way ANOVA models, receiver operating characteristic analysis, and comparisons between receiver operating characteristic analysis curves using a bivariate χ2, respectively. The highest differential diagnostic performance was obtained with the following combined classifiers: maximum percentage of signal intensity recovery-Cho/NAA to discriminate lymphomas from glioblastomas and metastases, significantly increasing the sensitivity from 82.1% to 95.7%; relative CBV-Cho/NAA to discriminate glioblastomas from lymphomas and metastases, significantly increasing the specificity from 92.7% to 100%; and maximum percentage of signal intensity recovery-lactate/Cr and maximum percentage of signal intensity recovery-Cho/Cr to discriminate metastases from lymphomas and glioblastomas, significantly increasing the specificity from 83.3% to 97.0% and 100%, respectively. Spectroscopy yielded an added performance value to perfusion using optimal combined classifiers of these modalities, significantly increasing the differential diagnostic performances for these common brain tumors.

T1-weighted dynamic contrast-enhanced brain magnetic resonance imaging: A preliminary study with low infusion rate in pediatric patients

Background The aim of this preliminary study is to evaluate the results of T1-weighted dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in pediatric patients at 1.5T, with a low peripheral intravenous gadoteric acid injection rate of 1 ml/s. Materials and methods Children with neurological symptoms were examined prospectively with conventional MRI and T1-weighted DCE MRI. An magnetic resonance perfusion analysis method was used to obtain time-concentration curves (persistent pattern, type-I; plateau pattern, type-II; washout pattern, type-III) and to calculate pharmacokinetic parameters. A total of two radiologists manually defined regions of interest (ROIs) in the part of the lesion exhibiting the greatest contrast enhancement and in the surrounding normal or contralateral tissue. Lesion/surrounding tissue or contralateral tissue pharmacokinetic parameter ratios were calculated. Tumors were categorized by grade (I-IV) using the World Health Organization (WHO) Grade. Mann-Whitney testing and receiver-operating characteristic (ROC) curves were performed. Results A total of nine boys and nine girls (mean age 10.5 years) were included. Lesions consisted of 10 brain tumors, 3 inflammatory lesions, 3 arteriovenous malformations and 2 strokes. We obtained analyzable concentration-time curves for all patients (6 type-I, 9 type-II, 3 type-III). Ktrans between tumor tissue and surrounding or contralateral tissue was significantly different ( p = 0.034). Ktrans ratios were significantly different between grade I tumors and grade IV tumors ( p = 0.027) and a Ktrans ratio value superior to 0.63 appeared to be discriminant to determine a grade IV of malignancy. Conclusions Our results confirm the feasibility of pediatric T1-weighted DCE MRI at 1.5T with a low injection rate, which could be of great value in differentiating brain tumor grades.

Accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue.

Raman spectroscopy could be applied to distinguish tumor from normal tissues. This meta-analysis was conducted to assess the accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue. PubMed and Embase were searched to identify suitable studies prior to Jan 1st, 2016. We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic odds ratio (DOR), and constructed summary receiver operating characteristics (SROC) curves to identity the accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue. A total of six studies with 1951 spectra were included. For glioma, the pooled sensitivity and specificity of Raman spectroscopy were 0.96 (95% CI 0.94-0.97) and 0.99 (95% CI 0.98-0.99), respectively. The area under the curve (AUC) was 0.9831. For meningioma, the pooled sensitivity and specificity were 0.98 (95% CI 0.94-1.00) and 1.00 (95% CI 0.98-1.00), respectively. The AUC was 0.9955. This meta-analysis suggested that Raman spectroscopy could be an effective and accurate tool for differentiating glioma and meningioma from normal brain tissue, which would help us both avoid removal of normal tissue and minimize the volume of residual tumor.

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Comment on Hatzoglou et al: Dynamic contrast-enhanced MRI perfusion versus 18FDG PET/CT in differentiating brain tumor progression from radiation injury.

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Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors.

Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade. Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated. Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (Ktrans), rate constant from extracellular extravascular space back into blood plasma (Kep), and extracellular extravascular volume fraction (Ve) were all significantly correlated with tumor grade; high-grade tumors showed higher Ktrans, higher Kep, and lower Ve. Although all 3 parameters had high specificity (range, 82%-100%), Kep had the highest specificity for both grades. Optimal sensitivity was achieved for Ve, with a combined sensitivity of 76% (compared with 71% for Ktrans and Kep). Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors.

A prospective trial of dynamic contrast-enhanced MRI perfusion and fluorine-18 FDG PET-CT in differentiating brain tumor progression from radiation injury after cranial irradiation

The aim of this study was to assess the effectiveness of fluorine-18 fluorodeoxyglucose (FDG) PET-CT and dynamic contrast-enhanced (DCE) MRI in differentiating tumor progression and radiation injury in patients with indeterminate enhancing lesions after radiation therapy (RT) for brain malignancies. Patients with indeterminate enhancing brain lesions on conventional MRI after RT underwent brain DCE-MRI and PET-CT in a prospective trial. Informed consent was obtained. Lesion outcomes were determined by histopathology and/or clinical and imaging follow-up. Metrics obtained included plasma volume (Vp) and volume transfer coefficient (K(trans)) from DCE-MRI, and maximum standardized uptake value (SUVmax) from PET-CT; lesion-to-normal brain ratios of all metrics were calculated. The Wilcoxon rank sum test and receiver operating characteristic analysis were performed. The study included 53 patients (29 treated for 29 gliomas and 24 treated for 26 brain metastases). Progression was determined in 38/55 (69%) indeterminate lesions and radiation injury in 17 (31%). Vpratio (VP lesion/VP normal brain, P < .001), K(trans) ratio (P = .002), and SUVratio (P = .002) correlated significantly with diagnosis of progression versus radiation injury. Progressing lesions exhibited higher values of all 3 metrics compared with radiation injury. Vpratio had the highest accuracy in determining progression (area under the curve = 0.87), with 92% sensitivity and 77% specificity using the optimal, retrospectively determined threshold of 2.1. When Vpratio was combined with K(trans) ratio (optimal threshold 3.6), accuracy increased to 94%. Vpratio was the most effective metric for distinguishing progression from radiation injury. Adding K(trans) ratio to Vpratio further improved accuracy. DCE-MRI is an effective imaging technique for evaluating nonspecific enhancing intracranial lesions after RT.