Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images

Background and objectives

Glioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging. Therefore, this study explores an improved deep learning algorithm to assist in the differentiation of GBM and MET.

Materials and methods

For this study, axial contrast-enhanced T1 weight (ceT1W) MRI images from 321 cases of high-grade gliomas and solitary brain metastasis were collected. Among these, 251 out of 270 cases were selected for the experimental dataset (127 glioblastomas and 124 metastases), 207 cases were chosen as the training dataset, and 44 cases as the testing dataset. We designed a new deep learning algorithm called SCAT-inception (Spatial Convolutional Attention inception) and used five-fold cross-validation to verify the results.

Results

By employing the newly designed SCAT-inception model to predict glioblastomas and brain metastasis, the prediction accuracy reached 92.3%, and the sensitivity and specificity reached 93.5 and 91.1%, respectively. On the external testing dataset, our model achieved an accuracy of 91.5%, which surpasses other model performances such as VGG, UNet, and GoogLeNet.

Conclusion

This study demonstrated that the SCAT-inception architecture could extract more subtle features from ceT1W images, provide state-of-the-art performance in the differentiation of GBM and MET, and surpass most existing approaches.

Methionine-PET to differentiate between brain lesions appearing similar on conventional CT/MRI scans

BACKGROUND AND PURPOSE

11 C-Methionine (MET)-PET is a useful tool in neuro-oncology. This study aimed to examine whether a combination of diagnostic variables associated with MET uptake could help distinguish between brain lesions that are often difficult to discriminate in conventional CT and MRI.

METHODS

MET-PET was assessed in 129 patients with glioblastoma multiforme, primary central nervous lymphoma, metastatic brain tumor, tumefactive multiple sclerosis, or radiation necrosis. The accuracy of the differential diagnosis was analyzed using five diagnostic characteristics in combination: higher maximum standardized uptake value (SUV) of MET in the lesion/the mean normal cortical SUV of MET ratio, overextension beyond gadolinium, peripheral pattern indicating abundant MET accumulation in the peripheral region, central pattern denoting abundant MET accumulation in the central region, and dynamic-up suggesting increased MET accumulation during dynamic study. The analysis was conducted on sets of two of the five brain lesions.

RESULTS

Significant differences in the five diagnostic traits were observed among the five brain lesions, and differential diagnosis could be achieved by combining these diagnostic features. The area under the curve between each set of two of the five brain lesions using MET-PET features ranged from .85 to 1.0.

CONCLUSIONS

According to the findings, combining the five diagnostic criteria could help with the differential diagnosis of the five brain lesions. MET-PET is an auxiliary diagnostic technique that could help in distinguishing these five brain lesions.

The Role of Molecular Imaging in Patients with Brain Metastases: A Literature Review

Over the last several years, molecular imaging has gained a primary role in the evaluation of patients with brain metastases (BM). Therefore, the "Response Assessment in Neuro-Oncology" (RANO) group recommends amino acid radiotracers for the assessment of BM. Our review summarizes the current use of positron emission tomography (PET) radiotracers in patients with BM, ranging from present to future perspectives with new PET radiotracers, including the role of radiomics and potential theranostics approaches. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2022 were reviewed. Current evidence confirms the important role of amino acid PET radiotracers for the delineation of BM extension, for the assessment of response to therapy, and particularly for the differentiation between tumor progression and radionecrosis. The newer radiotracers explore non-invasively different biological tumor processes, although more consistent findings in larger clinical trials are necessary to confirm preliminary results. Our review illustrates the role of molecular imaging in patients with BM. Along with magnetic resonance imaging (MRI), the gold standard for diagnosis of BM, PET is a useful complementary technique for processes that otherwise cannot be obtained from anatomical MRI alone.

Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

Investigational PET tracers in neuro-oncology-What's on the horizon? A report of the PET/RANO group

Many studies in patients with brain tumors evaluating innovative PET tracers have been published in recent years, and the initial results are promising. Here, the Response Assessment in Neuro-Oncology (RANO) PET working group provides an overview of the literature on novel investigational PET tracers for brain tumor patients. Furthermore, newer indications of more established PET tracers for the evaluation of glucose metabolism, amino acid transport, hypoxia, cell proliferation, and others are also discussed. Based on the preliminary findings, these novel investigational PET tracers should be further evaluated considering their promising potential. In particular, novel PET probes for imaging of translocator protein and somatostatin receptor overexpression as well as for immune system reactions appear to be of additional clinical value for tumor delineation and therapy monitoring. Progress in developing these radiotracers may contribute to improving brain tumor diagnostics and advancing clinical translational research.

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits.

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

A common goal to CARE: Cancer Advocates, Researchers, and Clinicians Explore current treatments and clinical trials for breast cancer brain metastases

Breast cancer is the most commonly diagnosed cancer in women worldwide. Approximately one-tenth of all patients with advanced breast cancer develop brain metastases resulting in an overall survival rate of fewer than 2 years. The challenges lie in developing new approaches to treat, monitor, and prevent breast cancer brain metastasis (BCBM). This review will provide an overview of BCBM from the integrated perspective of clinicians, researchers, and patient advocates. We will summarize the current management of BCBM, including diagnosis, treatment, and monitoring. We will highlight ongoing translational research for BCBM, including clinical trials and improved detection methods that can become the mainstay for BCBM treatment if they demonstrate efficacy. We will discuss preclinical BCBM research that focuses on the intrinsic properties of breast cancer cells and the influence of the brain microenvironment. Finally, we will spotlight emerging studies and future research needs to improve survival outcomes and preserve the quality of life for patients with BCBM.

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases

BACKGROUND

The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.

METHODS

One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC).

RESULTS

Through the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57-0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness.

CONCLUSIONS

We developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

Depression and tryptophan metabolism in patients with primary brain tumors: Clinical and molecular imaging correlates

Patients with brain tumors have an increased risk for depression, whose underlying pathomechanism may involve dysregulated tryptophan/kynurenine metabolism. In this study, we analyzed the relation of depressive symptoms to clinical and tumor characteristics as well as cerebral and systemic tryptophan metabolism in patients with primary brain tumors. Sixty patients with newly-diagnosed or recurrent primary brain tumor underwent testing with the Beck Depression Inventory-II (BDI-II), and 34 patients also had positron emission tomography (PET) imaging with alpha-[¹¹C]methyl-L-tryptophan (AMT). BDI-II scores were correlated with clinical and tumor-related variables, cerebral regional AMT metabolism measured in the non-tumoral hemisphere, and plasma tryptophan metabolite levels. Sixteen patients (27%) had BDI-II scores indicating depression, including 6 with moderate/severe depression. High BDI-II scores were independent of clinical and tumor-related variables except lower Karnofsky Performance Status scores. In patients with recurrent malignant gliomas, depression was associated with shorter survival (hazard ratio: 3.7; p = 0.048). High BDI-II total and somatic subscale scores were associated with higher frontal cortical and thalamic AMT metabolic values measured on PET. In contrast, plasma tryptophan and kynurenine metabolite levels did not correlate with the BDI-II scores. In conclusion, our results confirm previous data that depression affects more than ¼ of patients with primary brain tumors, it is largely independent of tumor characteristics and is associated with shorter survival in patients with recurrent malignant gliomas. On PET imaging, higher tryptophan metabolism in the frontal cortex and thalamus was found in those with brain tumor-associated depression and supports the role of dysregulated tryptophan/kynurenine metabolism in this condition.

Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis

Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.

11C- and 18F-labelled tryptophans as PET-tracers for imaging of altered tryptophan metabolism in age-associated disorders

The ageing of the world’s population is the result of increased life expectancy observed in almost all countries throughout the world. Consequently, a rising tide of ageing-associated disorders, like cancer and neurodegenerative diseases, represents one of the main global challenges of the 21st century. The ability of mankind to overcome these challenges is directly dependent on the capability to develop novel methods for therapy and diagnosis of age-associated diseases. One hallmark of age-related pathologies is an altered tryptophan metabolism. Numerous pathological processes including neurodegenerative and neurological diseases like epilepsy, Parkinson’s and Alzheimer’s diseases, cancer and diabetes exhibit marked changes in tryptophan metabolism. Visualization of key processes of tryptophan metabolic pathways, especially using positron emission tomography (PET) and related hybrid methods like PET/CT and PET/MRI, can be exploited to early detect the aforementioned disorders with considerable accuracy, allowing appropriate and timely treatment of patients. Here we review the published 11C- and 18F-labelled tryptophans with respect to the production and also preclinical and clinical evaluation as PET-tracers for visualization of different branches of tryptophan metabolism.

The bibliography includes 159 references.

PET imaging in patients with brain metastasis-report of the RANO/PET group

Brain metastases (BM) from extracranial cancer are associated with significant morbidity and mortality. Effective local treatment options are stereotactic radiotherapy, including radiosurgery or fractionated external beam radiotherapy, and surgical resection. The use of systemic treatment for intracranial disease control also is improving. BM diagnosis, treatment planning, and follow-up is most often based on contrast-enhanced magnetic resonance imaging (MRI). However, anatomic imaging modalities including standard MRI have limitations in accurately characterizing posttherapeutic reactive changes and treatment response. Molecular imaging techniques such as positron emission tomography (PET) characterize specific metabolic and cellular features of metastases, potentially providing clinically relevant information supplementing anatomic MRI. Here, the Response Assessment in Neuro-Oncology working group provides recommendations for the use of PET imaging in the clinical management of patients with BM based on evidence from studies validated by histology and/or clinical outcome.

Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach

PURPOSE

To evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach.

METHODS

This retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results.

RESULTS

Highest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations.

CONCLUSION

The proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Imaging tryptophan uptake with positron emission tomography in glioblastoma patients treated with indoximod

INTRODUCTION

Glioblastoma (GBM) is the most frequent and aggressive primary tumor of the central nervous system, accounting for over 50% of all primary malignant gliomas arising in the adult brain. Even after surgical resection, adjuvant radiotherapy (RT) and temozolomide (TMZ) chemotherapy, as well as tumor-treating fields, the median survival is only 15-20 months. We have identified a pathogenic mechanism that contributes to the tumor-induced immunosuppression in the form of increased indoleamine 2,3 dioxygenase 1 (IDO1) expression; an enzyme that metabolizes the essential amino acid, tryptophan (Trp), into kynurenine (Kyn). However, real-time measurements of IDO1 activity has yet to become mainstream in clinical protocols for assessing IDO1 activity in GBM patients.

METHODS

Pre-treatment and on-treatment α-[11C]-methyl-L-Trp (AMT) positron emission tomography (PET) with co-registered MRI was performed on patients with recurrent GBM treated with the IDO1 pathway inhibitor indoximod (D1-MT) and TMZ.

RESULTS

Regional intratumoral variability of AMT within enhancing and non-enhancing tumor was noted at baseline. On treatment imaging revealed decreased regional uptake suggesting IDO1 pathway modulation with treatment.

CONCLUSIONS

Here, we have validated the ability to use PET of the Trp probe, AMT, for use in visualizing and quantifying intratumoral Trp uptake in GBM patients treated with an IDO1 pathway inhibitor. These data serve as rationale to utilize AMT-PET imaging in the future evaluation of GBM patients treated with IDO1 enzyme inhibitors.

Investigational PET tracers for high-grade gliomas

High-grade gliomas (HGGs) are the most common primary malignant tumors of the brain, with glioblastoma (GBM) constituting over 50% of all the gliomas in adults. The disease carries very high mortality, and even with optimal treatment, the median survival is 2-5 years for anaplastic tumors and 1-2 years for GBMs. Neuroimaging is critical to managing patients with HGG for diagnosis, treatment planning, response assessment, and detecting recurrent disease. Magnetic resonance imaging (MRI) is the cornerstone of imaging in neuro-oncology, but molecular imaging with positron emission tomography (PET) can overcome some of the inherent limitations of MRI. Additionally, PET has the potential to target metabolic and molecular alterations in HGGs relevant to prognosis and therapy that cannot be assessed with anatomic imaging. Many classes of PET tracers have been evaluated in HGG including agents that target cell membrane biosynthesis, protein synthesis, amino acid transport, DNA synthesis, the tricarboxylic acid (TCA) cycle, hypoxic environments, cell surface receptors, blood flow, vascular endothelial growth factor (VEGF), epidermal growth factor (EGFR), and the 18-kDa translocator protein (TSPO), among others. This chapter will provide an overview of PET tracers for HGG that have been evaluated in human subjects with a focus on tracers that are not yet in widespread use for neuro-oncology.

The "pool sign" of metastatic adenocarcinoma

The differential of a newly discovered solitary intracranial mass is a primary intracranial neoplasm and metastatic disease. Differentiating between the two entities on imaging is difficult, though there are clues on conventional imaging that suggest one over the other. The purpose of this article is to describe a new imaging finding on T2-weighted imaging, the "pool sign," that may be specific for metastatic adenocarcinomas and can help differentiate a solitary metastasis from a primary CNS neoplasm. We present a series of four patients with initial magnetic resonance imaging of a solitary intracranial mass demonstrating the "pool sign," and therefore predicted to be metastatic adenocarcinoma. All of these cases were confirmed to be metastatic adenocarcinoma on pathology.

Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound

Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons' visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.

Brain metastases: neuroimaging

Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

Prognostic Molecular and Imaging Biomarkers in Primary Glioblastoma

PURPOSE

Several molecular glioma markers (including isocitrate dehydrogenase 1 [IDH1] mutation, amplification of the epidermal growth factor receptor [EGFR], and methylation of the O6-methylguanine-DNA methyltransferase [MGMT] promoter) have been associated with glioblastoma survival. In this study, we examined the association between tumoral amino acid uptake, molecular markers, and overall survival in patients with IDH1 wild-type (primary) glioblastoma.

PATIENTS AND METHODS

Twenty-one patients with newly diagnosed IDH1 wild-type glioblastomas underwent presurgical MRI and PET scanning with alpha[C-11]-L-methyl-tryptophan (AMT). MRI characteristics (T2- and T1-contrast volume), tumoral tryptophan uptake, PET-based metabolic tumor volume, and kinetic variables were correlated with prognostic molecular markers (EGFR and MGMT) and overall survival.

RESULTS

EGFR amplification was associated with lower T1-contrast volume (P = 0.04) as well as lower T1-contrast/T2 volume (P = 0.04) and T1-contrast/PET volume ratios (P = 0.02). Tumors with MGMT promoter methylation showed lower metabolic volume (P = 0.045) and lower tumor/cortex AMT unidirectional uptake ratios than those with unmethylated MGMT promoter (P = 0.009). While neither EGFR amplification nor MGMT promoter methylation was significantly associated with survival, high AMT tumor/cortex uptake ratios on PET were strongly prognostic for longer survival (hazards ratio, 30; P = 0.002). Estimated mean overall survival was 26 months in patients with high versus 8 months in those with low tumoral AMT uptake ratios.

CONCLUSIONS

The results demonstrate specific MRI and amino acid PET imaging characteristics associated with EGFR amplification and MGMT promoter methylation in patients with primary glioblastoma. High tryptophan uptake on PET may identify a subgroup with prolonged survival.

Assessment of Tryptophan Uptake and Kinetics Using 1-(2-18F-Fluoroethyl)-l-Tryptophan and α-11C-Methyl-l-Tryptophan PET Imaging in Mice Implanted with Patient-Derived Brain Tumor Xenografts

Abnormal tryptophan metabolism via the kynurenine pathway is involved in the pathophysiology of a variety of human diseases including cancers. α-¹¹C-methyl-l-tryptophan (¹¹C-AMT) PET imaging demonstrated increased tryptophan uptake and trapping in epileptic foci and brain tumors, but the short half-life of ¹¹C limits its widespread clinical application. Recent in vitro studies suggested that the novel radiotracer 1-(2-¹⁸F-fluoroethyl)-l-tryptophan (¹⁸F-FETrp) may be useful to assess tryptophan metabolism via the kynurenine pathway. In this study, we tested in vivo organ and tumor uptake and kinetics of ¹⁸F-FETrp in patient-derived xenograft mouse models and compared them with ¹¹C-AMT uptake.

METHODS

Xenograft mouse models of glioblastoma and metastatic brain tumors (from lung and breast cancer) were developed by subcutaneous implantation of patient tumor fragments. Dynamic PET scans with ¹⁸F-FETrp and ¹¹C-AMT were obtained for mice bearing human brain tumors 1-7 d apart. The biodistribution and tumoral SUVs for both tracers were compared.

RESULTS

¹⁸F-FETrp showed prominent uptake in the pancreas and no bone uptake, whereas ¹¹C-AMT showed higher uptake in the kidneys. Both tracers showed uptake in the xenograft tumors, with a plateau of approximately 30 min after injection; however, ¹⁸F-FETrp showed higher tumoral SUV than ¹¹C-AMT in all 3 tumor types tested. The radiation dosimetry for ¹⁸F-FETrp determined from the mouse data compared favorably with the clinical ¹⁸F-FDG PET tracer.

CONCLUSION

¹⁸F-FETrp tumoral uptake, biodistribution, and radiation dosimetry data provide strong preclinical evidence that this new radiotracer warrants further studies that may lead to a broadly applicable molecular imaging tool to examine abnormal tryptophan metabolism in human tumors.

Brain Tumors: An Update on Clinical PET Research in Gliomas

A previous review published in 2012 demonstrated the role of clinical PET for diagnosis and management of brain tumors using mainly FDG, amino acid tracers, and ¹⁸F-fluorothymidine. This review provides an update on clinical PET studies, most of which are motivated by prediction of prognosis and planning and monitoring of therapy in gliomas. For FDG, there has been additional evidence supporting late scanning, and combination with ¹³N ammonia has yielded some promising results. Large neutral amino acid tracers have found widespread applications mostly based on ¹⁸F-labeled compounds fluoroethyltyrosine and fluorodopa for targeting biopsies, therapy planning and monitoring, and as outcome markers in clinical trials. ¹¹C-alpha-methyltryptophan (AMT) has been proposed as an alternative to ¹¹C-methionine, and there may also be a role for cyclic amino acid tracers. ¹⁸F-fluorothymidine has shown strengths for tumor grading and as an outcome marker. Studies using ¹⁸F-fluorocholine (FCH) and ⁶⁸Ga-labeled compounds are promising but have not yet clearly defined their role. Studies on radiotherapy planning have explored the use of large neutral amino acid tracers to improve the delineation of tumor volume for irradiation and the use of hypoxia markers, in particular ¹⁸F-fluoromisonidazole. Many studies employed the combination of PET with advanced multimodal MR imaging methods, mostly demonstrating complementarity and some potential benefits of hybrid PET/MR.

Tryptophan PET Imaging of the Kynurenine Pathway in Patient-Derived Xenograft Models of Glioblastoma

Increasing evidence demonstrates the immunosuppressive kynurenine pathway's (KP) role in the pathophysiology of human gliomas. To study the KP in vivo, we used the noninvasive molecular imaging tracer α-[(11)C]-methyl-l-tryptophan (AMT). The AMT-positron emission tomography (PET) has shown high uptake in high-grade gliomas and predicted survival in patients with recurrent glioblastoma (GBM). We generated patient-derived xenograft (PDX) models from dissociated cells, or tumor fragments, from 5 patients with GBM. Mice bearing subcutaneous tumors were imaged with AMT-PET, and tumors were analyzed to detect the KP enzymes indoleamine 2,3-dioxygenase (IDO) 1, IDO2, tryptophan 2,3-dioxygenase, kynureninase, and kynurenine 3-monooxygenase. Overall, PET imaging showed robust tumoral AMT uptake in PDX mice with prolonged tracer accumulation over 60 minutes, consistent with AMT trapping seen in humans. Immunostained tumor tissues demonstrated positive detection of multiple KP enzymes. Furthermore, intracranial implantation of GBM cells was performed with imaging at both 9 and 14 days postimplant, with a marked increase in AMT uptake at 14 days and a corresponding high level of tissue immunostaining for KP enzymes. These results indicate that our PDX mouse models recapitulate human GBM, including aberrant tryptophan metabolism, and offer an in vivo system for development of targeted therapeutics for patients with GBM.

Component spectra extraction from terahertz measurements of unknown mixtures

The aim of this work is to extract component spectra from unknown mixtures in the terahertz region. To that end, a method, hard modeling factor analysis (HMFA), was applied to resolve terahertz spectral matrices collected from the unknown mixtures. This method does not require any expertise of the user and allows the consideration of nonlinear effects such as peak variations or peak shifts. It describes the spectra using a peak-based nonlinear mathematic model and builds the component spectra automatically by recombination of the resolved peaks through correlation analysis. Meanwhile, modifications on the method were made to take the features of terahertz spectra into account and to deal with the artificial baseline problem that troubles the extraction process of some terahertz spectra. In order to validate the proposed method, simulated wideband terahertz spectra of binary and ternary systems and experimental terahertz absorption spectra of amino acids mixtures were tested. In each test, not only the number of pure components could be correctly predicted but also the identified pure spectra had a good similarity with the true spectra. Moreover, the proposed method associated the molecular motions with the component extraction, making the identification process more physically meaningful and interpretable compared to other methods. The results indicate that the HMFA method with the modifications can be a practical tool for identifying component terahertz spectra in completely unknown mixtures. This work reports the solution to this kind of problem in the terahertz region for the first time, to the best of the authors' knowledge, and represents a significant advance toward exploring physical or chemical mechanisms of unknown complex systems by terahertz spectroscopy.

Comparison of amino acid positron emission tomographic radiotracers for molecular imaging of primary and metastatic brain tumors

Positron emission tomography (PET) is an imaging technology that can detect and characterize tumors based on their molecular and biochemical properties, such as altered glucose, nucleoside, or amino acid metabolism. PET plays a significant role in the diagnosis, prognostication, and treatment of various cancers, including brain tumors. In this article, we compare uptake mechanisms and the clinical performance of the amino acid PET radiotracers (l-[methyl-11C]methionine [MET], 18F-fluoroethyl-tyrosine [FET], 18F-fluoro-l-dihydroxy-phenylalanine [FDOPA], and 11C-alpha-methyl-l-tryptophan [AMT]) most commonly used for brain tumor imaging. First, we discuss and compare the mechanisms of tumoral transport and accumulation, the basis of differential performance of these radioligands in clinical studies. Then we summarize studies that provided direct comparisons among these amino acid tracers and to clinically used 2-deoxy-2[18F]fluoro-d-glucose [FDG] PET imaging. We also discuss how tracer kinetic analysis can enhance the clinical information obtained from amino acid PET images. We discuss both similarities and differences in potential clinical value for each radioligand. This comparative review can guide which radiotracer to favor in future clinical trials aimed at defining the role of these molecular imaging modalities in the clinical management of brain tumor patients.

Clinical significance of tryptophan metabolism in the nontumoral hemisphere in patients with malignant glioma

α-(11)C-methyl-L-tryptophan (AMT) PET allows evaluation of brain serotonin synthesis and can also track upregulation of the immunosuppressive kynurenine pathway in tumor tissue. Increased AMT uptake is a hallmark of World Health Organization grade III-IV gliomas. Our recent study also suggested decreased frontal cortical AMT uptake in glioma patients contralateral to the tumor. The clinical significance of extratumoral tryptophan metabolism has not been established. In the present study, we investigated clinical correlates of tryptophan metabolic abnormalities in the nontumoral hemisphere of glioma patients.

METHODS

Standardized AMT uptake values (SUVs) and the uptake rate constant of AMT (K [mL/g/min], a measure proportional to serotonin synthesis in nontumoral gray matter) were quantified in the frontal and temporal cortex and thalamus in the nontumoral hemisphere in 77 AMT PET scans of 66 patients (41 men, 25 women; mean age ± SD, 55 ± 15 y) with grade III-IV gliomas. These AMT values were determined before treatment in 35 and after treatment in 42 patients and were correlated with clinical variables and survival.

RESULTS

AMT uptake in the thalamus showed a moderate age-related increase before treatment (SUV, r = 0.39, P = 0.02) but decrease after treatment (K, r = -0.33, P = 0.057). Women had higher thalamic SUVs before treatment (P = 0.037) and higher thalamic (P = 0.013) and frontal cortical K values (P = 0.023) after treatment. In the posttreatment glioma group, high thalamic SUVs and high thalamocortical SUV ratios were associated with short survival in Cox regression analysis. The thalamocortical ratio remained strongly prognostic (P < 0.01) when clinical predictors, including age, glioma grade, and time since radiotherapy, were entered in the regression model. Long interval between radiotherapy and posttreatment AMT PET as well as high radiation dose affecting the thalamus were associated with lower contralateral thalamic or cortical AMT uptake values.

CONCLUSION

These observations provide evidence for altered tryptophan uptake in contralateral cortical and thalamic brain regions in glioma patients after initial therapy, suggesting treatment effects on the serotonergic system. Low thalamic tryptophan uptake appears to be a strong, independent predictor of long survival in patients with previous glioma treatment.

Terahertz spectroscopic investigation of human gastric normal and tumor tissues

Human dehydrated normal and cancerous gastric tissues were measured using transmission time-domain terahertz spectroscopy. Based on the obtained terahertz absorption spectra, the contrasts between the two kinds of tissue were investigated and techniques for automatic identification of cancerous tissue were studied. Distinctive differences were demonstrated in both the shape and amplitude of the absorption spectra between normal and tumor tissue. Additionally, some spectral features in the range of 0.2~0.5 THz and 1~1.5 THz were revealed for all cancerous gastric tissues. To systematically achieve the identification of gastric cancer, principal component analysis combined with t-test was used to extract valuable information indicating the best distinction between the two types. Two clustering approaches, K-means and support vector machine (SVM), were then performed to classify the processed terahertz data into normal and cancerous groups. SVM presented a satisfactory result with less false classification cases. The results of this study implicate the potential of the terahertz technique to detect gastric cancer. The applied data analysis methodology provides a suggestion for automatic discrimination of terahertz spectra in other applications.

SPECT and PET serve as molecular imaging techniques and in vivo biomarkers for brain metastases

Nuclear medicine techniques (single photon emission computerized tomography, SPECT, and positron emission tomography, PET) represent molecular imaging tools, able to provide in vivo biomarkers of different diseases. To investigate brain tumours and metastases many different radiopharmaceuticals imaged by SPECT and PET can be used. In this review the main and most promising radiopharmaceuticals available to detect brain metastases are reported. Furthermore the diagnostic contribution of the combination of SPECT and PET data with radiological findings (magnetic resonance imaging, MRI) is discussed.