Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

Incidental brain tumor findings in children: prevalence, natural history, management, controversies, challenges, and dilemmas

Incidental brain tumor findings in children involve the unexpected discovery of brain lesions during imaging for unrelated reasons. These findings differ significantly from those in adults, requiring a focus on pediatric-specific approaches in neurosurgery, neuroimaging, and neuro-oncology. Understanding the prevalence, progression, and management of these incidentalomas is crucial for informed decision-making, balancing patient welfare with the risks and benefits of intervention. Incidental brain tumors are observed in about 0.04-5.7% of cases, with most suspected low-grade lesions in children showing a benign course, though up to 3% may undergo malignant transformation. Treatment decisions are influenced by factors such as patient age, tumor characteristics, and family anxiety, with conservative management through surveillance often preferred. However, upfront surgery may be considered in cases with low surgical risk. Initial follow-up typically involves a comprehensive MRI after three months, with subsequent scans spaced out if the lesion remains stable. Changes in imaging or symptoms during follow-up could indicate malignant transformation, prompting consideration of surgery or biopsy. Several challenges and controversies persist, including the role of upfront biopsy for molecular profiling, the use of advanced imaging techniques like PET-CT and magnetic resonance spectroscopy, and the implications of the child's age at diagnosis. These issues highlight the need for further research to guide management and improve outcomes in pediatric patients with incidental brain tumor findings.

Proton MR spectroscopy shows improved performance to segregate high-grade astrocytoma subgroups when defined with the new 2021 World Health Organization classification of central nervous system tumors

OBJECTIVES

The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors prioritizes isocitrate dehydrogenase (IDH) mutation to define tumor types in diffuse gliomas, in contrast to the 2016 classification, which prioritized histological features. Our objective was to investigate the influence of this change in the performance of proton MR spectroscopy (1H-MRS) in segregating high-grade diffuse astrocytoma subgroups.

METHODS

Patients with CNS WHO grade 3 and 4 diffuse astrocytoma, known IDH mutation status, and available 1H-MRS were retrospectively retrieved and divided into 4 groups based on IDH mutation status and histological grade. Differences in 1H-MRS between groups were analyzed with the Kruskal-Wallis test. The points on the spectrum that showed the greatest differences were chosen to evaluate the performance of 1H-MRS in discriminating between grades 3 and 4 tumors (WHO 2016 defined), and between IDH-mutant and IDH-wildtype tumors (WHO 2021). ROC curves were constructed with these points, and AUC values were calculated and compared.

RESULTS

The study included 223 patients with high-grade diffuse astrocytoma. Discrimination between IDH-mutant and IDH-wildtype tumors showed higher AUC values (highest AUC short TE, 0.943; long TE, 0.864) and more noticeable visual differences than the discrimination between grade 3 and 4 tumors (short TE, 0.885; long TE, 0.838).

CONCLUSION

Our findings suggest that 1H-MRS is more applicable to classify high-grade astrocytomas defined with the 2021 criteria. Improved metabolomic robustness and more homogeneous groups yielded better tumor type discrimination by 1H-MRS with the new criteria.

CLINICAL RELEVANCE STATEMENT

The 2021 World Health Organization classification of brain tumors empowers molecular criteria to improve tumor characterization. This derives in greater segregation of high-grade diffuse astrocytoma subgroups by MR spectroscopy and warrants further development of brain tumor classification tools with spectroscopy.

KEY POINTS

• The new 2021 updated World Health Organization classification of central nervous system tumors maximizes the role of molecular diagnosis in the classification of brain tumors. • Proton MR spectroscopy performs better to segregate high-grade astrocytoma subgroups when defined with the new criteria. • The study provides additional evidence of improved metabolic characterization of brain tumor subgroups with the new criteria.

Preclinical and Clinical Use of Indigenously Developed 99mTc-Diethylenetriaminepentaacetic Acid-Bis-Methionine: l-Type Amino Acid Transporter 1-Targeted Single Photon Emission Computed Tomography Radiotracer for Glioma Management

A new era in tumor classification, diagnosis, and prognostic evaluation has begun as a consequence of recent developments in the molecular and genetic characterization of central nervous system tumors. In this newly emerging era, molecular imaging modalities are essential for preoperative diagnosis, surgical planning, targeted treatment, and post-therapy evaluation of gliomas. The radiotracers are able to identify brain tumors, distinguish between low- and high-grade lesions, confirm a patient's eligibility for theranostics, and assess post-radiation alterations. We previously synthesized and reported the novel l-type amino acid transporter 1 (LAT-1)-targeted amino acid derivative in light of the use of amino acid derivatives in imaging technologies. Further, we have developed a single vial ready to label Tc-lyophilized kit preparations of diethylenetriaminepentaacetic acid-bis-methionine [DTPA-bis(Met)], also referred to as methionine-diethylenetriaminepentaacetic acid-methionine (MDM) and evaluated its imaging potential in numerous clinical studies. This review summarizes our previous publications on 99mTc-DTPA-bis(Met) in different clinical studies such as detection of breast cancer, as a prognostic marker, in detection of recurrent/residual gliomas, for differentiation of recurrent/residual gliomas from radiation necrosis, and for comparison of 99mTc-DTPA-bis(Met) with 11C-L-methionine (11C-MET), with relevant literature on imaging modalities in glioma management.

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

BACKGROUND

Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation.

METHODS

This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation.

RESULTS

The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method.

CONCLUSIONS

The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study

In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired.

PURPOSE

To test whether MV grids can be classified with models trained with SV.

METHODS

Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score.

RESULTS

The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us.

DISCUSSION

The reasons for failure in the classification of the MV test set were related to the presence of artifacts.

In vivo brain MR spectroscopy in gliomas: clinical and pre-clinical chances

Purpose

Gliomas, the most common primary brain tumours, have recently been re-classified incorporating molecular aspects with important clinical, prognostic, and predictive implications. Concurrently, the reprogramming of metabolism, altering intracellular and extracellular metabolites affecting gene expression, differentiation, and the tumour microenvironment, is increasingly being studied, and alterations in metabolic pathways are becoming hallmarks of cancer. Magnetic resonance spectroscopy (MRS) is a complementary, non-invasive technique capable of quantifying multiple metabolites. The aim of this review focuses on the methodology and analysis techniques in proton MRS (1H MRS), including a brief look at X-nuclei MRS, and on its perspectives for diagnostic and prognostic biomarkers in gliomas in both clinical practice and preclinical research.

Methods

PubMed literature research was performed cross-linking the following key words: glioma, MRS, brain, in-vivo, human, animal model, clinical, pre-clinical, techniques, sequences, 1H, X-nuclei, Artificial Intelligence (AI), hyperpolarization.

Results

We selected clinical works (n = 51), preclinical studies (n = 35) and AI MRS application papers (n = 15) published within the last two decades. The methodological papers (n = 62) were taken into account since the technique first description.

Conclusions

Given the development of treatments targeting specific cancer metabolic pathways, MRS could play a key role in allowing non-invasive assessment for patient diagnosis and stratification, predicting and monitoring treatment responses and prognosis. The characterization of gliomas through MRS will benefit of a wide synergy among scientists and clinicians of different specialties within the context of new translational competences. Head coils, MRI hardware and post-processing analysis progress, advances in research, experts’ consensus recommendations and specific professionalizing programs will make the technique increasingly trustworthy, responsive, accessible.

Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert-based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition-based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non-Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time ¹ H-MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach.

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.

Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data

Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the proton signal of different molecules present in the tissue. However, its performance is severely hindered by low SNR, field inhomogeneities and overlapping spectra of metabolites, which affect the quantification of metabolites. Consequently, MRS is rarely included in routine clinical protocols and has not been proven in multi-institutional trials. This work proposes an alternative approach, where instead of quantifying metabolites' concentration, deep learning (DL) is used to model the complex nonlinear relationship between diseases and their spectroscopic metabolic fingerprint (pattern). DL requires large training datasets, acquired (ideally) with the same protocol/scanner, which are very rarely available. To overcome this limitation, a novel method is proposed that can quantum mechanically synthesise MRS data for any scanner/acquisition protocol. The proposed methodology is applied to the challenging clinical problem of differentiating metastasis from glioblastoma brain tumours on data acquired across multiple institutions. DL algorithms were trained on the augmented synthetic spectra and tested on two independent datasets acquired by different scanners, achieving a receiver operating characteristic area under the curve of up to 0.96 and 0.97, respectively.

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

OBJECTIVE

The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.

MATERIALS AND METHODS

We used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.

RESULTS

Cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.

CONCLUSIONS

Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.

Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning

INTRODUCTION

Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process.

OBJECTIVE

This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods.

METHODS

MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NAA) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm.

RESULTS

RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity.

CONCLUSIONS

A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.

Management of incidental brain tumors in children: a systematic review

BACKGROUND

Due to technical advancements and availability of neuroimaging, detection of incidental pediatric brain tumors (IPBT) is growing rapidly. The management of these asymptomatic lesions remains unclear; radiological, pathological, and clinical risk factors for further growth and malignant transformation (MT) are not well defined.

METHODS

We systematically reviewed the literature on the dilemmas and management of IPBT suggestive of a low-grade brain tumor (LGBT). Keyword searches of the PubMed and Medline (NCBI) databases identified studies on IPBT describing the prevalence, neuroimaging, management, or risk of MT through July 2019. References of the identified articles were also reviewed.

RESULTS

A total of 2021 records were screened. Fifty-nine full-text articles were reviewed, and 34 published studies were included. IPBT are diagnosed in 0.2-5.7% of children undergoing brain imaging for various reasons. The accepted approach for management of lesions showing radiological characteristics suggestive of LGBT is radiological follow-up. The rate at which additional intervention is required during follow-up for these apparently low-grade lesions is 9.5%. Nevertheless, the dilemma of early surgical resection or biopsy vs. clinical and radiological follow-up of IPBT is still unresolved. The risk in these cases is missing a transformation to a higher grade tumor. However, MT of pediatric LGBT is very rare, occurring in less than 3% of the cases of proven low-grade gliomas in children. The risk of future MT in pediatric low-grade gliomas seems to be greater in the presence of specific molecular markers such as BRAF V-600E, CDKN2A, and H3F3A K27M.

CONCLUSIONS

The natural history, management, and prognosis of IPBT remain ambiguous. It seems that lesions suggestive of LGBT can initially be followed, since many of these lesions remain stable over time and MT is rare. However, controversy among centers concerning the ideal approach still exists. Further observational and prospective cohort studies, focusing on potential clinical and radiological characteristics or risk factors suggestive of high-grade tumors, tumor progress, or MT of IPBT, are needed.

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.

Pediatric posterior fossa incidentalomas

PURPOSE

Pediatric brain incidentalomas are increasingly being diagnosed. As the posterior fossa (PF) is the location of most brain tumors in children, lesions of this region are of special interest. Currently, the natural history of incidental lesions in the PF is unknown. We present our experience treating such lesions.

METHODS

A retrospective study was carried out in two large tertiary pediatric centers. Patients were included if they had an incidental PF lesion suspected of being a tumor, and diagnosed before the age of 20 years. We analyzed treatment strategy, pathology, and outcome of operated and non-operated cases.

RESULTS

Seventy children (31 females) with a mean age of 8.4 ± 6.1 years were included. The three most common indications for imaging were headaches (16, assumed to be unrelated to the lesions), workup of unrelated conditions (14), and unspecified reasons (14). Twenty-seven patients (39%) were operated immediately, and 43 followed, of which 12 were eventually operated due to radiological changes, 28.9 ± 16.2 months after diagnosis. The most commonly found pathology was pilocytic astrocytomas (21 of 39 operated cases). Almost 10% were found to be malignant tumors including medulloblastomas (5) and ATRT (1).

CONCLUSION

Incidental PF lesions in children include both benign and malignant tumors. While certain lesions may be followed, others may require surgical treatment. Specific treatment decisions are based on initial radiological appearance, change in radiological characteristics over time, location, and evolving symptoms. The surgical risks must be balanced vis-à-vis the risk of missing a high-grade tumor and the very rare risk of malignant transformation.

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂ -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

PURPOSE

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

METHODS

Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

RESULTS

Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

CONCLUSIONS

The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

An update on PET-based molecular imaging in neuro-oncology: challenges and implementation for a precision medicine approach in cancer care

PET imaging using novel radiotracers show promises for tumor grading and molecular characterization through visualizing molecular and functional properties of the tumors. Application of PET tracers in brain neoplasm depends on both type of the neoplasm and the research or clinical significance required to be addressed. In clinical neuro-oncology, 18F-FDG is used mainly to differentiate tumor recurrence from radiation-induced necrosis, and novel PET agents show attractive imaging properties. Novel PET tracers can offer biologic information not visible via contrast-enhanced MRI or 18F-FDG PET. This review aims to provide an update on the complementary role of PET imaging in neuro-oncology both in research and clinical settings along with presenting interesting cases in this context.

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Purpose: To investigative the diagnostic performance of radiomics-based machine learning in differentiating glioblastomas (GBM) from metastatic brain tumors (MBTs).

Method: The current study involved 134 patients diagnosed and treated in our institution between April 2014 and December 2018. Radiomics features were extracted from contrast-enhanced T1 weighted imaging (T1C). Thirty diagnostic models were built based on five selection methods and six classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen.

Result : Two models represented promising diagnostic performance with AUC of 0.80. The first model was a combination of Distance Correlation as the selection method and Linear Discriminant Analysis (LDA) as the classification algorithm. In the training group, the sensitivity, specificity, accuracy, and AUC were 0.75, 0.85, 0.80, and 0.80, respectively; and in the testing group, the sensitivity, specificity, accuracy, and AUC of the model were 0.69, 0.86, 0.78, and 0.80, respectively. The second model was the Distance Correlation as the selection method and logistic regression (LR) as the classification algorithm, with sensitivity, specificity, accuracy, and AUC of 0.75, 0.85, 0.80, 0.80 in the training group and 0.69, 0.86, 0.78, 0.80 in the testing group.

Conclusion: Radiomic-based machine learning has potential to be utilized in differentiating GBM from MBTs.

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case.

Evaluation of the INTERPRET decision-support system: can it improve the diagnostic value of magnetic resonance spectroscopy of the brain?

Purpose

We evaluated in a clinical setting the INTERPRET decision-support system (DSS), a software generated to aid in MRS analysis to achieve a specific diagnosis for brain lesions.

Methods

The material consisted of 100 examinations of focal intracranial lesions with confirmed diagnoses. MRS was obtained at 1.5 T using TE 20–30 ms. Data were processed with the LCModel for conventional analysis. The INTERPRET DSS 3.1. was used to obtain specific diagnoses. MRI and MRS were reviewed by one interpreter. DSS analysis was made by another interpreter, in 80 cases by two interpreters. The diagnoses were compared with the definitive diagnoses. For comparisons between DSS, conventional MRS analysis, and MRI, the diagnoses were categorised: high-grade tumour, low-grade tumour, non-neoplastic lesion.

Results

Interobserver agreement in choosing the diagnosis from the INTERPRET database was 75%. The diagnosis was correct in 38/100 cases, incorrect in 57 cases. No good match was found in 5/100 cases. The diagnostic category was correct with DSS/conventional MRS/MRI in 67/58/52 cases, indeterminate in 5/8/20 cases, incorrect in 28/34/28 cases. Results with DSS were not significantly better than with conventional MRS analysis. All definitive diagnoses did not exist in the INTERPRET database. In the 61 adult patients with the diagnosis included in the database, DSS/conventional MRS/MRI yielded a correct diagnosis category in 48/32/29 cases (DSS vs conventional MRS: p = 0.002, DSS vs MRI: p = 0.0004).

Conclusion

Use of the INTERPRET DSS did not improve MRS categorisation of the lesions in the unselected clinical cases. In adult patients with lesions existing in the INTERPRET database, DSS improved the results, which indicates the potential of this software with an extended database.

Electronic supplementary material

The online version of this article (10.1007/s00234-018-2129-7) contains supplementary material, which is available to authorized users.

The value of magnetic resonance spectroscopy as a supplement to MRI of the brain in a clinical setting

BACKGROUND

There are different opinions of the clinical value of MRS of the brain. In selected materials MRS has demonstrated good results for characterisation of both neoplastic and non-neoplastic lesions. The aim of this study was to evaluate the supplemental value of MR spectroscopy (MRS) in a clinical setting.

MATERIAL AND METHODS

MRI and MRS were re-evaluated in 208 cases with a clinically indicated MRS (cases with uncertain or insufficient information on MRI) and a confirmed diagnosis. Both single voxel spectroscopy (SVS) and chemical shift imaging (CSI) were performed in 105 cases, only SVS or CSI in 54 and 49 cases, respectively. Diagnoses were grouped into categories: non-neoplastic disease, low-grade tumour, and high-grade tumour. The clinical value of MRS was considered very beneficial if it provided the correct category or location when MRI did not, beneficial if it ruled out suspected diseases or was more specific than MRI, inconsequential if it provided the same level of information, or misleading if it provided less or incorrect information.

RESULTS

There were 70 non-neoplastic lesions, 43 low-grade tumours, and 95 high-grade tumours. For MRI, the category was correct in 130 cases (62%), indeterminate in 39 cases (19%), and incorrect in 39 cases (19%). Supplemented with MRS, 134 cases (64%) were correct, 23 cases (11%) indeterminate, and 51 (25%) incorrect. Additional information from MRS was beneficial or very beneficial in 31 cases (15%) and misleading in 36 cases (17%).

CONCLUSION

In most cases MRS did not add to the diagnostic value of MRI. In selected cases, MRS may be a valuable supplement to MRI.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Clinical applications of in vivo magnetic resonance spectroscopy in oncology

Magnetic resonance spectroscopy (MRS) can be performed in vivo using commercial MRI systems to obtain biochemical information about tissues and cancers. Applications in brain, prostate and breast aid lesion detection and characterisation (differential diagnosis), treatment planning and response assessment. Multi-centre clinical trials have been performed in all these tissues. Single centre studies have been performed in many other tissues including cervix, uterus, musculoskeletal and liver. While generally MRS is used to study endogenous metabolites it has also been used in drug studies, for example those that include 19F as part of their structure. Recently the hyperpolarisation of compounds enriched with 13C such as [1-13C] pyruvate has been demonstrated in animal models and now in preliminary clinical studies, permitting the monitoring of biochemical processes with unprecedented sensitivity. This review briefly introduces the underlying methods and then discusses the current status of these applications.

Prospective multicentre evaluation and refinement of an analysis tool for magnetic resonance spectroscopy of childhood cerebellar tumours

BACKGROUND

A tool for diagnosing childhood cerebellar tumours using magnetic resonance (MR) spectroscopy peak height measurement has been developed based on retrospective analysis of single-centre data.

OBJECTIVE

To determine the diagnostic accuracy of the peak height measurement tool in a multicentre prospective study, and optimise it by adding new prospective data to the original dataset.

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) and single-voxel MR spectroscopy were performed on children with cerebellar tumours at three centres. Spectra were processed using standard scanner software and peak heights for N-acetyl aspartate, creatine, total choline and myo-inositol were measured. The original diagnostic tool was used to classify 26 new tumours as pilocytic astrocytoma, medulloblastoma or ependymoma. These spectra were subsequently combined with the original dataset to develop an optimised scheme from 53 tumours in total.

RESULTS

Of the pilocytic astrocytomas, medulloblastomas and ependymomas, 65.4% were correctly assigned using the original tool. An optimized scheme was produced from the combined dataset correctly assigning 90.6%. Rare tumour types showed distinctive MR spectroscopy features.

CONCLUSION

The original diagnostic tool gave modest accuracy when tested prospectively on multicentre data. Increasing the dataset provided a diagnostic tool based on MR spectroscopy peak height measurement with high levels of accuracy for multicentre data.

ACRIN 6684: Multicenter, phase II assessment of tumor hypoxia in newly diagnosed glioblastoma using magnetic resonance spectroscopy

A multi-center imaging trial by the American College of Radiology Imaging Network (ACRIN) "A Multicenter, phase II assessment of tumor hypoxia in glioblastoma using 18F Fluoromisonidazole (FMISO) with PET and MRI (ACRIN 6684)", was conducted to assess hypoxia in patients with glioblastoma (GBM). The aims of this study were to support the role of proton magnetic resonance spectroscopic imaging (1H MRSI) as a prognostic marker for brain tumor patients in multi-center clinical trials. Seventeen participants from four sites had analyzable 3D MRSI datasets acquired on Philips, GE or Siemens scanners at either 1.5T or 3T. MRSI data were analyzed using LCModel to quantify metabolites N-acetylaspartate (NAA), creatine (Cr), choline (Cho), and lactate (Lac). Receiver operating characteristic curves for NAA/Cho, Cho/Cr, lactate/Cr, and lactate/NAA were constructed for overall survival at 1-year (OS-1) and 6-month progression free survival (PFS-6). The OS-1 for the 17 evaluable patients was 59% (10/17). Receiver operating characteristic analyses found the NAA/Cho in tumor (AUC = 0.83, 95% CI: 0.61 to 1.00) and in peritumoral regions (AUC = 0.95, 95% CI 0.85 to 1.00) were predictive for survival at 1 year. PFS-6 was 65% (11/17). Neither NAA/Cho nor Cho/Cr was effective in predicting 6-month progression free survival. Lac/Cr in tumor was a significant negative predictor of PFS-6, indicating that higher lactate/Cr levels are associated with poorer outcome. (AUC = 0.79, 95% CI: 0.54 to 1.00). In conclusion, despite the small sample size in the setting of a multi-center trial comprising different vendors, field strengths, and varying levels of expertise at data acquisition, MRS markers NAA/Cho, Lac/Cr and Lac/NAA predicted overall survival at 1 year and 6-month progression free survival. This study validates that MRSI may be useful in evaluating the prognosis in glioblastoma and should be considered for incorporating into multi-center clinical trials.

A convolutional neural network to filter artifacts in spectroscopic MRI

PURPOSE

Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information.

METHODS

A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts.

RESULTS

When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time.

CONCLUSION

The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.

Imaging in neuro-oncology

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

Exploration of human brain tumour metabolism using pairwise metabolite-metabolite correlation analysis (MMCA) of HR-MAS 1H NMR spectra

METHODS

We quantified 378 HRMAS 1H NMR spectra of human brain tumours (132 glioblastomas, 101 astrocytomas, 75 meningiomas, 37 oligodendrogliomas and 33 metastases) from the eTumour database and looked for metabolic interactions by metabolite-metabolite correlation analysis (MMCA).

RESULTS

All tumour types showed remarkably similar metabolic correlations. Lactate correlated positively with alanine, glutamate with glutamine; creatine + phosphocreatine (tCr) correlated positively with lactate, alanine and choline + phosphocholine + glycerophosphocholine (tCho), and tCho correlated positively with lactate; fatty acids correlated negatively with lactate, glutamate + glutamine (tGlut), tCr and tCho. Oligodendrogliomas had fewer correlations but they still fitted that pattern.

CONCLUSIONS

Possible explanations include (i) glycolytic tumour cells (the Warburg effect) generating pyruvate which is converted to lactate, alanine, glutamate and then glutamine; (ii) an association between elevated glycolysis and increased choline turnover in membranes; (iii) an increase in the tCr pool to facilitate phosphocreatine-driven glutamate uptake; (iv) lipid signals come from cytosolic lipid droplets in necrotic or pre-necrotic tumour tissue that has lower concentrations of anabolic and catabolic metabolites. Additional metabolite exchanges with host cells may also be involved. If tumours co-opt a standard set of biochemical mechanisms to grow in the brain, then drugs might be developed to disrupt those mechanisms.

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools

PURPOSE

To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors.

METHODS

A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment.

RESULTS

AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system.

CONCLUSION

Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Big GABA: Edited MR spectroscopy at 24 research sites

Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study's protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.

Brain Tumor Imaging

Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.

Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories

OBJECTIVE

To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ).

MATERIALS AND METHODS

Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) monitoring and exploring changes with time. The methods are suited to big data and multitype, multivariate, and multimodal data.

RESULTS

The repository was partitioned into 2 probabilistically separated temporal subgroups following a change in the Spanish National Death Certificate in 2009. Punctual temporal anomalies were noticed due to a punctual increment in the missing data, along with outlying and clustered health departments due to differences in populations or in practices.

DISCUSSION

Changes in protocols, differences in populations, biased practices, or other systematic DQ problems affected data variability. Even if semantic and integration aspects are addressed in data sharing infrastructures, probabilistic variability may still be present. Solutions include fixing or excluding data and analyzing different sites or time periods separately. A systematic approach to assessing temporal and multisite variability is proposed.

CONCLUSION

Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures.

Magnetic resonance spectroscopy - Revisiting the biochemical and molecular milieu of brain tumors

Background

Magnetic resonance spectroscopy (MRS) is an established tool for in-vivo evaluation of the biochemical basis of human diseases. On one hand, such lucid depiction of ‘live biochemistry’ helps one to decipher the true nature of the pathology while on the other hand one can track the response to therapy at sub-cellular level. Brain tumors have been an area of continuous interrogation and instigation for mankind. Evaluation of these lesions by MRS plays a crucial role in the two aspects of disease management described above.

Scope of review

Presented is an overview of the window provided by MRS into the biochemical aspects of brain tumors. We systematically visit each metabolite deciphered by MRS and discuss the role of deconvoluting the biochemical aspects of pathologies (here in context of brain tumors) in the disease management cycle. We further try to unify a radiologist's perspective of disease with that of a biochemist to prove the point that preclinical work is the mother of the treatment we provide at bedside as clinicians. Furthermore, an integrated approach by various scientific experts help resolve a query encountered in everyday practice.

Major conclusions

MR spectroscopy is an integral tool for evaluation and systematic follow-up of brain tumors. A deeper understanding of this technology by a biochemist would help in a swift and more logical development of the technique while a close collaboration with radiologist would enable definitive application of the same.

General significance

The review aims at inciting closer ties between the two specialists enabling a deeper understanding of this valuable technology.

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Magnetic resonance spectroscopy is an established technology for non-invasive assessment of pathological tissue.

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Good understanding of the physical principles of the technique can help one exploit it maximally.

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An array of information from the technique is available and needs deep understanding of the results.

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Newer variations of this technology are being invented to evaluate different aspects of pathologies in a more refined manner.

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We also discuss the limitations of this technology and possible solutions there-off.

Clinical magnetic resonance spectroscopy of the central nervous system

Proton magnetic resonance spectroscopy (1H MRS) is a noninvasive imaging technique that can easily be added to the conventional magnetic resonance (MR) imaging sequences. Using MRS one can directly compare spectra from pathologic or abnormal tissue and normal tissue. Metabolic changes arising from pathology that can be visualized by MRS may not be apparent from anatomy that can be visualized by conventional MR imaging. In addition, metabolic changes may precede anatomic changes. Thus, MRS is used for diagnostics, to observe disease progression, monitor therapeutic treatments, and to understand the pathogenesis of diseases. MRS may have an important impact on patient management. The purpose of this chapter is to provide practical guidance in the clinical application of MRS of the brain. This chapter provides an overview of MRS-detectable metabolites and their significance. In addition some specific current clinical applications of MRS will be discussed, including brain tumors, inborn errors of metabolism, leukodystrophies, ischemia, epilepsy, and neurodegenerative diseases. The chapter concludes with technical considerations and challenges of clinical MRS.

Usefulness of the apparent diffusion coefficient for the evaluation of the white matter to differentiate between glioblastoma and brain metastases

OBJECTIVE

To determine whether there are significant differences in the apparent diffusion coefficient (ADC) between the apparently normal peritumor white matter surrounding glioblastomas and that surrounding brain metastases.

MATERIAL AND METHODS

We retrospectively reviewed 42 patients with histologically confirmed glioblastomas and 42 patients with a single cerebral metastasis. We measured the signal intensity in the apparently normal peritumor white matter and in the abnormal peritumor white matter on the ADC maps. We used mean ADC values in the contralateral occipital white matter as a reference from which to design normalized ADC indices. We compared mean values between the two tumor types. We calculated the area under the receiver operator characteristic curve and estimated the sensitivity and specificity of the measurements taken.

RESULTS

Supratentorial lesions and compromise of the corpus callosum were more common in patients with glioblastoma than in patients with brain metastases. The maximum diameter of the enhanced area after injection of a contrast agent was greater in the glioblastomas (p<0.001). The minimum ADC value measured in the apparently normal peritumor white matter was higher for the glioblastomas than for the metastases (p=0.002). Significant differences in the ADC index were found only for the minimum ADC value in apparently normal peritumor white matter. The sensitivity and specificity were less than 70% for all variables analyzed.

CONCLUSIONS

There are differences in the ADC values of apparently normal peritumor white matter between glioblastomas and cerebral metastases, but the magnitude of these differences is slight and the application of these differences in clinical practice is still limited.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

BACKGROUND

We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas.

METHODS

In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients.

RESULTS

Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%.

CONCLUSIONS

Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.

Diagnostic and prognostic value of 18F-DOPA PET and 1H-MR spectroscopy in pediatric supratentorial infiltrative gliomas: a comparative study

BACKGROUND

(1)H-MR spectroscopy (MRS) and (18)F-dihydroxyphenylalanine (DOPA) PET are noninvasive imaging techniques able to assess metabolic features of brain tumors. The aim of this study was to compare diagnostic and prognostic information gathered by (18)F-DOPA PET and (1)H-MRS in children with supratentorial infiltrative gliomas or nonneoplastic brain lesions suspected to be gliomas.

METHODS

We retrospectively analyzed 27 pediatric patients with supratentorial infiltrative brain lesions on conventional MRI (21 gliomas and 6 nonneoplastic lesions) who underwent (18)F-DOPA PET and (1)H-MRS within 2 weeks of each other. (1)H-MRS data (choline/N-acetylaspartate, choline-to-creatine ratios, and presence of lactate) and (18)F-DOPA uptake parameters (lesion-to-normal tissue and lesion-to-striatum ratios) were compared and correlated with histology, WHO tumor grade, and patient outcome.

RESULTS

(1)H-MRS and (18)F-DOPA PET data were positively correlated. Sensitivity, specificity, and accuracy in distinguishing gliomas from nonneoplastic lesions were 95%, 83%, and 93% for (1)H-MRS and 76%, 83%, and 78% for (18)F-DOPA PET, respectively. No statistically significant differences were found between the 2 techniques (P > .05). Significant differences regarding (18)F-DOPA uptake and (1)H-MRS ratios were found between low-grade and high-grade gliomas (P≤.001 and P≤.04, respectively). On multivariate analysis, (18)F-DOPA uptake independently correlated with progression-free survival (P≤.05) and overall survival (P = .04), whereas (1)H-MRS did not show significant association with outcome.

CONCLUSIONS

(1)H-MRS and (18)F-DOPA PET provide useful complementary information for evaluating the metabolism of pediatric brain lesions. (1)H-MRS represents the method of first choice for differentiating brain gliomas from nonneoplastic lesions.(18)F-DOPA uptake better discriminates low-grade from high-grade gliomas and is an independent predictor of outcome.

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis

PURPOSE

Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome.

METHODS

Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics.

RESULTS

Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods.

CONCLUSION

The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med 75:2505-2516, 2016. © 2015 Wiley Periodicals, Inc.

Audit method suited for DSS in clinical environment

This chapter presents a novel online method to audit predictive models using a Bayesian perspective. The auditing model has been specifically designed for Decision Support Systems (DSSs) suited for clinical or research environments. Taking as starting point the working diagnosis supplied by the clinician, this method compares and evaluates the predictive skills of those models able to answer to that diagnosis. The approach consists in calculating the posterior odds of a model through the composition of a prior odds, a static odds, and a dynamic odds. To do so, this method estimates the posterior odds from the cases that the comparing models had in common during the design stage and from the cases already viewed by the DSS after deployment in the clinical site. In addition, if an ontology of the classes is available, this method can audit models answering related questions, which offers a reinforcement to the decisions the user already took and gives orientation on further diagnostic steps.The main technical novelty of this approach lies in the design of an audit model adapted to suit the decision workflow of a clinical environment. The audit model allows deciding what is the classifier that best suits each particular case under evaluation and allows the detection of possible misbehaviours due to population differences or data shifts in the clinical site. We show the efficacy of our method for the problem of brain tumor diagnosis with Magnetic Resonance Spectroscopy (MRS).

Volumetric spectroscopic imaging of glioblastoma multiforme radiation treatment volumes

PURPOSE

Magnetic resonance (MR) imaging and computed tomography (CT) are used almost exclusively in radiation therapy planning of glioblastoma multiforme (GBM), despite their well-recognized limitations. MR spectroscopic imaging (MRSI) can identify biochemical patterns associated with normal brain and tumor, predominantly by observation of choline (Cho) and N-acetylaspartate (NAA) distributions. In this study, volumetric 3-dimensional MRSI was used to map these compounds over a wide region of the brain and to evaluate metabolite-defined treatment targets (metabolic tumor volumes [MTV]).

METHODS AND MATERIALS

Volumetric MRSI with effective voxel size of ∼1.0 mL and standard clinical MR images were obtained from 19 GBM patients. Gross tumor volumes and edema were manually outlined, and clinical target volumes (CTVs) receiving 46 and 60 Gy were defined (CTV46 and CTV60, respectively). MTVCho and MTVNAA were constructed based on volumes with high Cho and low NAA relative to values estimated from normal-appearing tissue.

RESULTS

The MRSI coverage of the brain was between 70% and 76%. The MTVNAA were almost entirely contained within the edema, and the correlation between the 2 volumes was significant (r=0.68, P=.001). In contrast, a considerable fraction of MTVCho was outside of the edema (median, 33%) and for some patients it was also outside of the CTV46 and CTV60. These untreated volumes were greater than 10% for 7 patients (37%) in the study, and on average more than one-third (34.3%) of the MTVCho for these patients were outside of CTV60.

CONCLUSIONS

This study demonstrates the potential usefulness of whole-brain MRSI for radiation therapy planning of GBM and revealed that areas of metabolically active tumor are not covered by standard RT volumes. The described integration of MTV into the RT system will pave the way to future clinical trials investigating outcomes in patients treated based on metabolic information.

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data

INTRODUCTION

A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification.

METHODS

MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data.

RESULTS

The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible.

CONCLUSIONS

FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.

(1) H 2D MRSI tissue type analysis of gliomas

PURPOSE

To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation.

METHODS

Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent.

RESULTS

Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging.

CONCLUSION

LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade.

Clinical proton MR spectroscopy in central nervous system disorders

A large body of published work shows that proton (hydrogen 1 [(1)H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of (1)H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of (1)H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which (1)H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units.