Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions

Central nervous system metastases in advanced non-small cell lung cancer: A review of the therapeutic landscape

Up to 40% of patients with non-small cell lung cancer (NSCLC) develop central nervous system (CNS) metastases. Current treatments for this subgroup of patients with advanced NSCLC include local therapies (surgery, stereotactic radiosurgery, and, less frequently, whole-brain radiotherapy), targeted therapies for oncogene-addicted NSCLC (small molecules, such as tyrosine kinase inhibitors, and antibody-drug conjugates), and immune checkpoint inhibitors (as monotherapy or combination therapy), with multiple new drugs in development. However, confirming the intracranial activity of these treatments has proven to be challenging, given that most lung cancer clinical trials exclude patients with untreated and/or progressing CNS metastases, or do not include prespecified CNS-related endpoints. Here we review progress in the treatment of patients with CNS metastases originating from NSCLC, examining local treatment options, systemic therapies, and multimodal therapeutic strategies. We also consider challenges regarding assessment of treatment response and provide thoughts around future directions for managing CNS disease in patients with advanced NSCLC.

Advancements in nanotheranostics for glioma therapy

Gliomas are brain tumors mainly derived from glial cells that are difficult to treat and cause high mortality. Radiation, chemotherapy, and surgical excision are the conventional treatments for gliomas. Patients who have surgery or have undergone chemotherapy for glioma treatment have poor prognosis with tumor recurrence. In particular, for glioblastoma, the 5-year average survival rate is 4-7%, and the median survival is 12-18 months. A number of issues hinder effective treatment such as, poor surgical resection, tumor heterogeneity, insufficient drug penetration across the blood-brain barrier, multidrug resistance, and difficulties with drug specificity. Nanotheranostic-mediated drug delivery is becoming a well-researched consideration, and an efficient non-invasive method for delivering chemotherapeutic drugs to the target area. Theranostic nanomedicines, which incorporate therapeutic drugs and imaging agents for personalized therapies, can be used for preventing overdose of non-responders. Through the identification of massive and complicated information from next-generation sequencing, machine learning enables for precise prediction of therapeutic outcomes and post-treatment management for patients with cancer. This article gives a thorough overview of nanocarrier-mediated drug delivery with a brief introduction to drug delivery challenges. In addition, this assessment offers a current summary of preclinical and clinical research on nanomedicines for gliomas. In the future, nanotheranostics will provide personalized treatment for gliomas and other treatable cancers.

Gliomas in adults: Guidance on investigations, diagnosis, treatment and surveillance

Primary brain tumours are rare but carry a significant morbidity and mortality burden. Malignant gliomas are the most common subtype and their incidence is increasing within our ageing population. The diagnosis and treatment of gliomas involves substantial interplay between multiple specialties, including general medical physicians, radiologists, pathologists, surgeons, oncologists and allied health professionals. At any point along this pathway, patients can present to acute medicine with complications of their cancer or anti-cancer therapy. Increasing the awareness of malignant gliomas among general physicians is paramount to delivering prompt radiological and histopathological diagnoses, facilitating access to earlier and individualised treatment options and allows for effective recognition and management of anticipated complications. This article discusses evidence-based real-world practice for malignant gliomas, encompassing patient presentation, diagnostic pathways, treatments and their complications, and prognosis to guide management outside of specialist centres.

Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging

Purpose

Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated.

Approach

We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence.

Results

Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48).

Conclusions

The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

The Utility of Multiparametric Magnetic Resonance Imaging in Reducing Diagnostic Uncertainty for Primary Central Nervous System Lymphoma

BACKGROUND

A key limitation in treatment initiation in primary central nervous system lymphoma (PCNSL) is the diagnostic delay caused by lack of recognition of a lesion as a possible lymphoma, steroid initiation, and lesion involution, often resulting in an inconclusive biopsy result. We highlight the importance of multiparametric magnetic resonance imaging (MRI), which incorporates diffusion-weighted imaging, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, and proton magnetic resonance spectroscopy in addition to standard MRI sequences in resolving diagnostic uncertainty for PCNSL.

METHODS

At our center, a consecutive series of 10 patients with histology-proven PCNSL (specifically, diffuse large B-cell lymphoma of the central nervous system) underwent multiparametric MRI. We retrospectively analyzed qualitative and semiquantitative parameters and assessed their radiological concordance for this diagnosis.

RESULTS

We noted overall low apparent diffusion coefficient on diffusion-weighted imaging (mean minimum apparent diffusion coefficient of 0.74), high percentage signal recovery on perfusion-weighted imaging (mean 170%), a high choline-to-creatine ratio, and a high-grade lipid peak on proton magnetic resonance spectroscopy giving an appearance of twin towers. Of 10 patients, 9 had MRI findings concordant for PCNSL, defined as at least 3 of 4 parameters being consistent for PCNSL.

CONCLUSIONS

Concordance between these imaging multiparametric modalities could be used as a radiological predictor of PCNSL, reducing diagnostic delays, providing a more accurate biopsy target, and resulting in quicker treatment initiation.

Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks

This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.

Epstein-Barr Virus-Associated Smooth-Muscle Tumor of the Brain

Epstein-Barr virus, a herpesvirus, has been associated with a variety of cancers, including Burkitt, Hodgkin, and non-Hodgkin lymphomas; posttransplant lymphoproliferative disorders; gastric carcinoma; and nasopharyngeal carcinoma, in both immunocompetent and immunocompromised individuals. Previous studies have established a connection between Epstein-Barr virus and the development of smooth-muscle tumors. Smooth-muscle tumors of the brain are very rare and are often misdiagnosed as meningiomas on imaging. To our knowledge, advanced imaging findings such as MR perfusion of smooth-muscle tumors of the brain have never been reported. We describe the radiologic and pathologic features of the Epstein-Barr virus-associated smooth-muscle tumors of the brain in a person with newly diagnosed advanced HIV.

The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions

BACKGROUND

Amino acid PET is recommended for the initial diagnosis of brain lesions, but its value for identifying aggressive lesions remains to be established. The current study therefore evaluates the added-value of dynamic [18 F]FDOPA PET as an adjunct to conventional MRI for determining the aggressiveness of presumed glial lesions at diagnosis.

METHODS

Consecutive patients, with a minimal 1 year-follow-up, underwent contrast-enhanced MRI (CE MRI) and dynamic [18 F]FDOPA PET to characterize their suspected glial lesion. Lesions were classified semi-automatically by their CE MRI (MRI-/+), and PET parameters (static tumor-to-background ratio, TBR; dynamic time-to-peak ratio, TTPratio). Diagnostic accuracies of MRI and PET parameters for the differentiation of tumor aggressiveness were evaluated by chi-square test or receiver operating characteristic analyses. Aggressive lesions were either defined as lesions with dismal molecular characteristics based on the WHO 2021 classification of brain tumors or with compatible clinico-radiological profiles. Time-to-treatment failure (TTF) and overall survival (OS) were evaluated.

RESULTS

Of the 109 patients included, 46 had aggressive lesions (45 confirmed by histo-molecular analyses). CE MRI identified aggressive lesions with an accuracy of 73%. TBRmax (threshold of 3.2), and TTPratio (threshold of 5.4 min) respectively identified aggressive lesions with an accuracy of 83% and 76% and were independent of CE MRI and clinical factors in the multivariate analysis. Among the MRI-lesions, 11/56 (20%) were aggressive and respectively 55% and 50% of these aggressive lesions showed high TBRmax and short TTPratio in PET. High TBRmax and short TTPratio in PET were significantly associated to poorer survivals (p ≤ 0.009).

CONCLUSION

Dynamic [18 F]FDOPA PET provides a similar diagnostic accuracy as contrast enhancement in MRI to identify the aggressiveness of suspected glial lesions at diagnosis. Both methods, however, are complementary and [18 F]FDOPA PET may be a useful additional tool in equivocal cases.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Multimodal Image Classification by Multiview Latent Pattern Extraction, Selection, and Correlation

The large amount of data available in the modern big data era opens new opportunities to expand our knowledge by integrating information from heterogeneous sources. Multiview learning has recently achieved tremendous success in deriving complementary information from multiple data modalities. This article proposes a framework called multiview latent space projection (MVLSP) to integrate features extracted from multiple sources in a discriminative way to facilitate binary and multiclass classifications. Our approach is associated with three innovations. First, most existing multiview learning algorithms promote pairwise consistency between two views and do not have a natural extension to applications with more than two views. MVLSP finds optimum mappings from a common latent space to match the feature space in each of the views. As the matching is performed on a view-by-view basis, the framework can be readily extended to multiview applications. Second, feature selection in the common latent space can be readily achieved by adding a class view, which matches the latent space representations of training samples with their corresponding labels. Then, high-order view correlations are extracted by considering feature-label correlations. Third, a technique is proposed to optimize the integration of different latent patterns based on their correlations. The experimental results on the prostate image dataset demonstrate the effectiveness of the proposed method.

Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain

INTRODUCTION

This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM).

METHODS

Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences.

RESULTS

Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values.

CONCLUSION

The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects

INTRODUCTION

Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC.

METHODS

This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies.

RESULTS

Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival.

CONCLUSION

Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.

Dual path parallel hierarchical diagnosis model for intracranial tumors based on multi-feature entropy weight

The grading diagnosis of intracranial tumors is a key step in formulating clinical treatment plans and surgical guidelines. To effectively grade the diagnosis of intracranial tumors, this paper proposes a dual path parallel hierarchical model that can automatically grade the diagnosis of intracranial tumors with high accuracy. In this model, prior features of solid tumor mass and intratumoral necrosis are extracted. Then the optimal division of the data set is achieved through multi-feature entropy weight. The multi-modal input is realized by the dual path structure. Multiple features are superimposed and fused to achieve the image grading. The model has been tested on the actual clinical medical images provided by the Second Affiliated Hospital of Dalian Medical University. The experiment shows that the proposed model has good generalization ability, with an accuracy of 0.990. The proposed model can be applied to clinical diagnosis and has practical application prospects.

Technical note: hydrogel-based mimics of prostate cancer with matched relaxation, diffusion and kurtosis for validating multi-parametric MRI

BACKGROUND

Protocol standardization and optimization for clinical translation of emerging quantitative multiparametric (mp)MRI biomarkers of high-risk prostate cancer requires imaging references that mimic realistic tissue value combinations for bias assessment in derived relaxation and diffusion parameters.

PURPOSE

This work aimed to develop a novel class of hydrogel-based synthetic materials with simultaneously controlled quantitative relaxation, diffusion, and kurtosis parameters that mimic in vivo prostate value combinations in the same spatial compartment and allow stable assemblies of adjacent structures.

METHODS

A set of materials with tunable T2, diffusion, and kurtosis were assembled to create quantitative biomimetic (mp)MRI references. T2 was controlled with variable agarose concentration, monoexponential diffusion by polyvinylpyrrolidone (PVP), and kurtosis by addition of lamellar vesicles. The materials were mechanically stabilized by UV cross-linked polyacrylamide gels (PAG) to allow biomimetic morphologies. The reference T2 were measured on a 3T scanner using multi-echo CPMG, and diffusion kurtosis-with multi-b DWI.

RESULTS

Agarose concentration controls T2 values which are nominally independent of PVP or vesicle concentration. For agarose PVP hydrogels, monoexponential diffusion values are a function of PVP concentration and independent of agarose concentration. Compared to free vesicles, for agarose-PAG combined with vesicles, diffusion was predominantly controlled by vesicles and PAG, while kurtosis was affected by agarose and vesicle concentration. Both hydrogel classes achieved image voxel parameter values (T2, Da, Ka) for relaxation (T2: 65-255 ms), apparent diffusion (Da: 0.8-1.7 μm2/ms), and kurtosis (Ka: 0.5-1.25) within the target literature ranges for normal prostate zones and cancer lesions. Relaxation and diffusion parameters remained stable for over 6 months for layered material assemblies.

CONCLUSION

A stable biomimetic mpMR reference based on hydrogels has been developed with a range of multi-compartment diffusion and relaxation parameter combinations observed in cancerous and healthy prostate tissue.

Multiparametric MRI and T2/FLAIR mismatch complements the World Health Organization 2021 classification for the diagnosis of IDH-mutant 1p/19q non-co-deleted/ATRX-mutant astrocytoma

AIM

To investigate whether T2-weighted imaging-fluid-attenuated inversion recovery (T2/FLAIR) mismatch, T2∗ dynamic susceptibility contrast (DSC) perfusion, and magnetic resonance spectroscopy (MRS) correlated with the histological diagnosis and grading of IDH (isocitrate dehydrogenase)-mutant, 1p/19q non-co-deleted/ATRX (alpha-thalassemia mental retardation X-linked)-mutant astrocytoma.

MATERIALS

Imaging of 101 IDH-mutant diffuse glioma cases of histological grades 2-3 (2019-2021) were analysed retrospectively by two neuroradiologists blinded to the molecular diagnosis. T2/FLAIR mismatch sign is used for radio-phenotyping, and pre-biopsy multiparametric MRI images were assessed for grading purposes. Cut-off values pre-determined for radiologically high-grade lesions were relative cerebral blood volume (rCBV) ≥2, choline/creatine ratio (Cho/Cr) ≥1.5 (30 ms echo time [TE]), Cho/Cr ≥1.8 (135 ms TE).

RESULTS

Sixteen of the 101 cases showed T2/FLAIR mismatch, all of which were histogenetically confirmed IDH-mutant 1p/19q non-co-deleted/ATRX mutant astrocytomas; 50% were grade 3 (8/16) and 50% grade 2 (8/16). None showed contrast enhancement. Nine of the 16 had adequate multiparametric MRI for analysis. Any positive value by combining rCBV ≥2 with Cho/Cr ≥1.5 (30 ms TE) or Cho/Cr ≥1.8 (135 ms TE) predicted grade 3 histology with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%.

CONCLUSION

The T2/FLAIR mismatch sign detected diffuse astrocytomas with 100% specificity. When combined with high Cho/Cr and raised rCBV, this predicted histological grading with high accuracy. The future direction for imaging should explore a similar integrated layered approach of 2021 classification of central nervous system (CNS) tumours combining radio-phenotyping and grading from structural and multiparametric imaging.

Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning

BACKGROUND

One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of cases. The clinical usefulness of discriminating this phenomenon through magnetic resonance imaging and nuclear medicine has not yet been standardized; in this study, we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon.

METHODS

For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in 2011-2020 were selected; 15 patients corresponded to early tumor progression and 15 patients to pseudoprogression. Using unsupervised learning, the number of clusters and tumor segmentation was recorded using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction was carried out with a multinomial logistic regression supervised learning method; the outcome variables were the percentage of assignment, class overrepresentation, and degree of voxel adjacency.

RESULTS

Unsupervised learning of the tumor in its diagnosis shows up to 14 well-differentiated tumor areas. In the supervised learning phase, there is a higher percentage of assigned classes (P < 0.01), less overrepresentation of classes (P < 0.01), and greater adjacency (55% vs. 33%) in cases of true tumor progression compared with pseudoprogression.

CONCLUSIONS

True tumor progression preserves the multidimensional characteristics of the basal tumor at the voxel and region of interest level, resulting in a characteristic differential pattern when supervised learning is used.

Comparing the diagnostic accuracy of MR dacryocystography (MRD) and dacryoscintigraphy (DSG) in NLDO-related acquired epiphora

PROPOSE

This study aimed to compare the diagnostic accuracy of MR dacryocystography (MRD) and dacryoscintigraphy (DSG) in the diagnosis of acquired epiphora related to NLDO. A total of 15 patients with acquired epiphora and suspected NLDO were included in this study.

METHODS

All patients underwent MRD and DSG examinations. MRD was performed using a 3-Tesla magnetic resonance imaging (MRI) scanner, while DSG involved injection of a radiotracer into the lacrimal drainage system followed by DSG. The results of both imaging methods were compared with the reference standard that was a combination of clinical examination findings and surgical exploration.

RESULTS

The results of this study showed that no abnormal findings were observed in MR-DCG in patients before the Valsalva maneuver. However, after the Valsalva maneuver, stenosis/obstruction at the canal surface was observed in all 15 patients diagnosed by DSG, giving a sensitivity of 100% for canal stenosis. Moreover, the results revealed that among these 15 patients, 9 showed stenosis or simultaneous obstruction at the level of the canal and lacrimal sac, but MR-DCG showed these lesions in only 9 patients, giving a sensitivity of 60%. The specificity of MRD and DSG were 85% and 76.7%, respectively. There was a statistically significant difference in the sensitivity of MRD and DSG (p < 0.05).

CONCLUSION

This study demonstrated that MRD has a higher diagnostic accuracy in the diagnosis of acquired epiphora associated with NLDO compared to DSG. MRD showed significantly higher sensitivity and specificity than DSG. Therefore, MRD can be considered as the preferred imaging modality in the diagnosis of acquired epiphora due to NLDO. By accurately identifying the underlying cause of NLDO, MRD can help determine the most appropriate treatment approach for patients and lead to better outcomes.

Beyond T2-FLAIR mismatch sign in isocitrate dehydrogenase mutant 1p19q non-codeleted astrocytoma: Analysis of tumor core and evolution with multiparametric magnetic resonance imaging

Background

The T2-FLAIR mismatch sign is an imaging correlate for isocitrate dehydrogenase (IDH)-mutant 1p19q non-codeleted astrocytomas. However, it is only seen in a part of the cases at certain stages. Many of the tumors likely lose T2 homogeneity as they grow in size, and become heterogenous. The aim of this study was to investigate the timecourse of T2-FLAIR mismatch sign, and assess intratumoral heterogeneity using multiparametric magnetic resonance imaging techniques.

Methods

A total of 128 IDH-mutant gliomas were retrospectively analyzed. Observers blinded to molecular status used strict criteria to select T2-FLAIR mismatch astrocytomas. Pre-biopsy and follow-up standard structural sequences of T2, FLAIR and apparent diffusion coefficient, MR spectroscopy (both single- and multi-voxel techniques), and DSC perfusion were observed.

Results

Nine T2-FLAIR mismatch astrocytomas were identified. 7 had MR spectroscopy and perfusion data. The smallest astrocytomas began as rounded T2 homogeneous lesions without FLAIR suppression, and developed T2-FLAIR mismatch during follow-up with falls in NAA and raised Cho/Cr ratio. Larger tumors at baseline with T2-FLAIR mismatch signs developed intratumoral heterogeneity, and showed elevated Cho/Cr ratio and raised relative cerebral blood volume (rCBV). The highest levels of intratumoral Cho/Cr and rCBV changes were located within the tumor core, and this area signifies the progression of the tumors toward high grade.

Conclusions

T2-FLAIR mismatch sign is seen at a specific stage in the development of astrocytoma. By assessing the subsequent heterogeneity, MR spectroscopy and perfusion imaging are able to predict the progression of the tumor towards high grade, thereby can assist targeting for biopsy and selective debulking.

State of the neoadjuvant therapy for glioblastoma multiforme-Where do we stand?

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor in adults. Despite several investigations in this field, maximal safe resection followed by chemoradiotherapy and adjuvant temozolomide with or without tumor-treating fields remains the standard of care with poor survival outcomes. Many endeavors have failed to make a dramatic change in the outcomes of GBM patients. This study aimed to review the available strategies for newly diagnosed GBM in the neoadjuvant setting, which have been mainly neglected in contrast to other solid tumors.

Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review

Gliomas, a prevalent category of primary malignant brain tumors, pose formidable clinical challenges due to their invasive nature and limited treatment options. The current therapeutic landscape for gliomas is constrained by a "one-size-fits-all" paradigm, significantly restricting treatment efficacy. Despite the implementation of multimodal therapeutic strategies, survival rates remain disheartening. The conventional treatment approach, involving surgical resection, radiation, and chemotherapy, grapples with substantial limitations, particularly in addressing the invasive nature of gliomas. Conventional diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), play pivotal roles in outlining tumor characteristics. However, they face limitations, such as poor biological specificity and challenges in distinguishing active tumor regions. The ongoing development of diagnostic tools and therapeutic approaches represents a multifaceted and promising frontier in the battle against this challenging brain tumor. The aim of this comprehensive review is to address recent advances in diagnostic tools and therapeutic approaches for gliomas. These innovations aim to minimize invasiveness while enabling the precise, multimodal targeting of localized gliomas. Researchers are actively developing new diagnostic tools, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, surface-enhanced Raman spectroscopy, and optical biosensors. These tools aim to regulate tumor progression and develop precise treatment methods for gliomas. Recent technological advancements, coupled with bioelectronic sensors, open avenues for new therapeutic modalities, minimizing invasiveness and enabling multimodal targeting with unprecedented precision. The next generation of multimodal therapeutic strategies holds potential for precision medicine, aiding the early detection and effective management of solid brain tumors. These innovations offer promise in adopting precision medicine methodologies, enabling early disease detection, and improving solid brain tumor management. This review comprehensively recognizes the critical role of pioneering therapeutic interventions, holding significant potential to revolutionize brain tumor therapeutics.

Comparative study between dynamic susceptibility contrast magnetic resonance imaging and arterial spin labelling perfusion in differentiating low-grade from high-grade brain tumours

Purpose

Our aim was to distinguish between low-grade and high-grade brain tumours on the basis of dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion and arterial spin labelling (ASL) perfusion and to compare DSC and ASL techniques.

Material and methods

Forty-one patients with brain tumours were evaluated by 3-Tesla MRI. Conventional and perfusion MRI imaging with a 3D pseudo-continuous ASL (PCASL) and DSC perfusion maps were evaluated. Three ROIs were placed to obtain cerebral blood value (CBV) and cerebral blood flow (CBF) in areas of maximum perfusion in brain tumour and normal grey matter. Histopathological diagnosis was considered as the reference. ROC analysis was performed to compare the diagnostic performance and to obtain a feasible cut-off value of perfusion parameters to differentiate low-grade and high-grade brain tumours.

Results

Normalised perfusion parameters with grey matter (rCBF or rCBV lesion/NGM) of malignant lesions were significantly higher than those of benign lesions in both DSC (normalised rCBF of 2.16 and normalised rCBV of 2.63) and ASL (normalised rCBF of 2.22) perfusion imaging. The normalised cut-off values of DSC (rCBF of 1.1 and rCBV of 1.4) and ASL (rCBF of 1.3) showed similar specificity and near similar sensitivity in distinguishing low-grade and high-grade brain tumours.

Conclusions

Quantitative analysis of perfusion parameters obtained by both DSC and ASL perfusion techniques can be reliably used to distinguish low-grade and high-grade brain tumours. Normalisation of these values by grey matter gives us more reliable parameters, eliminating the different technical parameters involved in both the techniques.

Temporal lobe tumors modify local venous drainage

OBJECTIVE

Superficial Middle Cerebral Vein (SMCV) is an anastomotic vein frequently exposed during surgery. Changes in the pattern of cerebral venous outflow can occur in many pathological settings. We explored the hypothesis that the growth of an intracranial tumor could determine alterations in the venous outflow. We analyzed SMCV anatomical variants in patients undergoing surgery for intracranial tumors; we furthermore focused on association with histology.

METHODS

We retrospectively collected data of 120 patients undergoing surgery, 60 presenting intracranial tumor and 60 presenting cerebral aneurysms (control group). Tumor series was divided into "Low Growth-Rate tumors" (WHO grade I and II) and "High Growth-Rate tumors" (WHO grade III and IV). Anatomical variants of SMCV were analyzed on intraoperative videos and then classified as Type 1 (normotrophic), 2 A (hypotrophic) and Type 2B (absent/atrophic). We furthermore defined as Type 2 any alteration of the SMCV (2 A+2B) encountered. Relationships among SMCV types and both populations were analyzed using the chi-squared test; values of p < 0.05 were considered statistically significant.

RESULTS

We found a positive correlation between the presence of a primary brain tumor and Type 2B SMCV (PC.004, p < 0.05) and Type 2 SMCV (PC.000, p < 0.05). Specifically, we found a strong correlation between the absence of SMCV (Type 2B) and both tumors subgroups. Thus, the growth of a primary brain tumor seems to affect the cerebral local outflow.

CONCLUSIONS

Primary brain tumors seem to alter local venous network of SMCV. Clinical and oncological implications remain subject of further investigation.

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics

ABSTRACT

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation

PURPOSE

To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability.

METHODS

By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we implemented a novel deep learning model, Neural ODE, in which deep feature extraction was governed by an ODE parameterized by a neural network. The dynamics of (1) MR images after interactions with the deep neural network and (2) segmentation formation can thus be visualized after solving the ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate each MR image's utilization by the deep neural network toward the final segmentation results. The proposed Neural ODE model was demonstrated using 369 glioma patients with a 4-modality multi-parametric MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. Three Neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The key MRI modalities with significant utilization by deep neural networks were identified based on ACC analysis. Segmentation results by deep neural networks using only the key MRI modalities were compared to those using all four MRI modalities in terms of Dice coefficient, accuracy, sensitivity, and specificity.

RESULTS

All Neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U-Net results using all four MRI modalities, the Dice coefficient of ET (0.784→0.775), TC (0.760→0.758), and WT (0.841→0.837) using the key modalities only had minimal differences without significance. Accuracy, sensitivity, and specificity results demonstrated the same patterns.

CONCLUSION

The Neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image-related deep-learning applications.

The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review

Background and purpose

Improving the accuracy of brain tumour radiotherapy (RT) treatment planning is important to optimise patient outcomes. This systematic review investigates primary studies providing clinical evidence for the integration of quantitative magnetic resonance imaging (qMRI) biomarkers and MRI radiomics to optimise brain tumour RT planning.

Materials and methods

PubMed, Scopus, Embase and Web of Science databases were searched for all years until June 21, 2022. The search identified original articles demonstrating clinical evidence for the use of qMRI biomarkers and MRI radiomics for the optimization of brain cancer RT planning. Relevant information was extracted and tabulated, including qMRI metrics and techniques, impact on RT plan optimization and changes in target and normal tissue contouring and dose distribution.

Results

Nineteen articles met the inclusion criteria. Studies were grouped according to the qMRI biomarkers into: 1) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI; five studies); 2) diffusion tensor imaging (DTI; seven studies); and 3) MR spectroscopic imaging (MRSI; seven studies). No relevant MRI-based radiomics studies were identified. Integration of DTI maps offers the potential for improved organs at risk (OAR) sparing. MRSI metabolic maps are a promising technique for improving delineation accuracy in terms of heterogeneity and infiltration, with OAR sparing. No firm conclusions could be drawn regarding the integration of DWI metrics and PWI maps.

Conclusions

Integration of qMRI metrics into RT planning offers the potential to improve delineation and OAR sparing. Clinical trials and consensus guidelines are required to demonstrate the clinical benefits of such approaches.

Imaging Cancer in Neuroradiology

Neuroimaging plays a pivotal role in the diagnosis, management, and prognostication of brain tumors. Recently, the World Health Organization published the fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS5), which places greater emphasis on tumor genetics and molecular markers to complement the existing histological and immunohistochemical approaches. Recent advances in computational power allowed modern neuro-oncological imaging to move from a strictly morphology-based discipline to advanced neuroimaging techniques with quantifiable tissue characteristics such as tumor cellularity, microstructural organization, hemodynamic, functional, and metabolic features, providing more precise tumor diagnosis and management. The aim of this review is to highlight the key imaging features of the recently published CNS5, outlining the current imaging standards and summarizing the latest advances in neuro-oncological imaging techniques and their role in complementing traditional brain tumor imaging and management.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Role of Multiparametric Magnetic Resonance Imaging of the Brain in Differentiating Neurocysticercosis From Tuberculoma

INTRODUCTION

The two most common infectious causes of ring-enhancing lesions are neurocysticercosis (NCC) and tuberculoma. It is a challenge to differentiate NCC and tuberculomas radiologically since they show the same imaging findings on computed tomography (CT). Hence, this study was performed to assess the role of magnetic resonance imaging (MRI) as an additional advanced modality to aptly characterize the lesion. Conventional MRI with additional advanced imaging sequences like diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), magnetic resonance spectroscopy (MRS), and post-contrast T1-weighted imaging (T1WI) aids in characterizing the lesion and helps in differentiating NCC and tuberculomas.

OBJECTIVES

To compare the findings of DWI, ADC cut-off values, spectroscopy, and contrast-enhanced MRI in differentiating NCC from tuberculoma.

MATERIALS AND METHODS

Individuals who matched the inclusion criterion underwent an MRI of the brain (plain and contrast) in a 1.5 Tesla, 18-channel, magnetic resonance scanner (Magnetom Avanto®, Siemens Healthineers, Erlangen, Germany). The following imaging sequences were included: T1WI (axial and sagittal), T2-weighted imaging (axial and coronal), fluid-attenuated inversion recovery, DWI at 0, 500, and 1000 mm²/s b-values with corresponding ADC values, and single-voxel MRS. Based on MRI features such as number, size, location, margins of lesions, scolex, surrounding edema, DWI features with corresponding ADC values, enhancement pattern of lesions, and spectroscopy findings, we evaluated and differentiated the lesions as NCC or tuberculoma. Radiological diagnoses were correlated in terms of clinical symptoms and response to treatment.

RESULTS

In our study, 42 subjects were included, of which the total number of NCC cases was 25 (59.52%) and tuberculoma was 17 (40.47%). The mean age of patients included was 42.85 ± 14.76 years (21 to 78 years). On post-contrast imaging, all 25 cases of NCC (100%) showed thin ring enhancement whereas the majority of tuberculomas (64.7%) showed thick irregular ring enhancement. On MRS, all 25 cases (100%) of NCC showed an amino acid peak and all 17 cases (100%) of tuberculoma showed a lipid lactate peak. On DWI, out of 25 NCC cases, restriction of diffusion was absent in the majority of cases (88%) and out of 17 cases of tuberculoma, restriction of diffusion was present in 12 cases (70.5%) (T2 hyperintense tuberculoma, indicative of caseating tuberculoma with central liquefaction) and was absent in the rest. In our study, the mean ADC value of NCC lesions (1.30 ± 0.137 x 10^-3 mm²/s) was found to be greater than that of tuberculoma (0.74 ± 0.090 x 10^-3 mm²/s). ADC value of 1.2 x 10^-3 was obtained as a cut-off to differentiate NCC and tuberculoma. The ADC cut-off value of 1.2 x 10^-3 mm²/s showed a sensitivity of 92% and specificity of 94.1% in differentiating NCC from tuberculoma.

CONCLUSIONS

Conventional MRI with additional advanced imaging sequences like DWI, ADC, MRS, and post-contrast T1WI aids in characterizing the lesion and thereby helps in differentiating NCC and tuberculomas. Hence, multiparametric MRI assessment is useful in making a prompt diagnosis and eliminating the need for a biopsy.

Acquisition and Analysis of Excised Neocortex from Pediatric Patients with Focal Cortical Dysplasia Using Mesoscale Diffusion MRI

Non-invasive classification of focal cortical dysplasia (FCD) subtypes remains challenging from a radiology perspective. Quantitative imaging biomarkers (QIBs) have the potential to distinguish subtypes that lack pathognomonic features and might help in defining the extent of abnormal connectivity associated with each FCD subtype. A key motivation of diagnostic imaging is to improve the localization of a "lesion" that can guide the surgical resection of affected tissue, which is thought to cause seizures. Conversely, surgical resections to eliminate or reduce seizures provided unique opportunities to develop magnetic resonance imaging (MRI)-based QIBs by affording long scan times to evaluate multiple contrast mechanisms at the mesoscale (0.5 mm isotropic voxel dimensions). Using ex vivo hybrid diffusion tensor imaging on a 9.4 T MRI scanner, the grey to white matter ratio of scalar indices was lower in the resected middle temporal gyrus (MTG) of two neuropathologically confirmed cases of FCD compared to non-diseased control postmortem fixed temporal lobes. In contrast, fractional anisotropy was increased within FCD and also adjacent white matter tracts. Connectivity (streamlines/mm3) in the MTG was higher in FCD, suggesting that an altered connectivity at the lesion locus can potentially provide a tangible QIB to distinguish and characterize FCD abnormalities. However, as illustrated here, a major challenge for a robust tractographical comparison lies in the considerable differences in the ex vivo processing of bioptic and postmortem samples. Mesoscale diffusion MRI has the potential to better define and characterize epileptic tissues obtained from surgical resection to advance our understanding of disease etiology and treatment.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology

OBJECTIVE

To investigate the diagnostic performance of a multiparametric magnetic resonance imaging (MRI) protocol comprising quantitative MRI (diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) perfusion MRI) and conventional MRI in the characterization of gallbladder wall thickening (GWT).

METHODS

This prospective study comprised consecutive adults with GWT who underwent multiparametric MRI between July 2020 and April 2022. Two radiologists evaluated the MRI independently. The final diagnosis was based on surgical histopathology. The association of MRI parameters with malignant GWT was evaluated. The area under the curve (AUC) for the quantitative MRI parameters and diagnostic performance of conventional, and multiparametric MRI were compared. The interobserver agreement between two radiologists was calculated.

RESULTS

Thirty-five patients (mean age, 56 years, 23 females) with GWT (25 benign and ten malignant) were evaluated. The quantitative MRI parameters significantly associated with malignant GWT were apparent diffusion coefficient on DWI (p = 0.007) and mean diffusivity (MD) on DTI (p = 0.013), perfusion fraction (f) on IVIM (p = 0.033), time to peak enhancement (TTP, p = 0.008), and wash in rate (p = 0.049) on DCE-MRI. TTP had the highest AUC of 0.790, followed by MD (0.782) and f (0.742) (p = 0.213) for predicting malignant GWT. Multiparametric MRI had significantly higher sensitivity (90% vs. 80%, p = 0.045) than conventional MRI for diagnosing malignant GWT. The two radiologists' reading had substantial to near-perfect agreement (kappa = 0.639-1) and moderate to strong correlation (interclass correlation coefficient = 0.5-0.88).

CONCLUSION

Multiparametric protocol incorporating advanced sequences improved the diagnostic performance of MRI for differentiating benign and malignant GWT.

KEY POINTS

• Multiparametric MRI had 90% sensitivity and 88% specificity for diagnosing malignant GWT, compared to 80% sensitivity and 88% specificity for conventional CE-MRI. • Among the quantitative MRI parameters, TTP (perfusion-MRI) had the highest AUC of 0.790, followed by MD (0.782) and IVIM-f (0.742). • For most quantitative MRI parameters, there was moderate to strong agreement (ICC = 0.5-0.88).

Differentiation of Perilesional Edema in Glioblastomas and Brain Metastases: Comparison of Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging and Diffusion Microstructure Imaging

Although the free water content within the perilesional T2 hyperintense region should differ between glioblastomas (GBM) and brain metastases based on histological differences, the application of classical MR diffusion models has led to inconsistent results regarding the differentiation between these two entities. Whereas diffusion tensor imaging (DTI) considers the voxel as a single compartment, multicompartment approaches such as neurite orientation dispersion and density imaging (NODDI) or the recently introduced diffusion microstructure imaging (DMI) allow for the calculation of the relative proportions of intra- and extra-axonal and also free water compartments in brain tissue. We investigate the potential of water-sensitive DTI, NODDI and DMI metrics to detect differences in free water content of the perilesional T2 hyperintense area between histopathologically confirmed GBM and brain metastases. Respective diffusion metrics most susceptible to alterations in the free water content (MD, V-ISO, V-CSF) were extracted from T2 hyperintense perilesional areas, normalized and compared in 24 patients with GBM and 25 with brain metastases. DTI MD was significantly increased in metastases (p = 0.006) compared to GBM, which was corroborated by an increased DMI V-CSF (p = 0.001), while the NODDI-derived ISO-VF showed only trend level increase in metastases not reaching significance (p = 0.060). In conclusion, diffusion MRI metrics are able to detect subtle differences in the free water content of perilesional T2 hyperintense areas in GBM and metastases, whereas DMI seems to be superior to DTI and NODDI.

Revisiting Concepts of Magnetic Resonance Spectroscopy in the Evaluation of Brain Lesions: An Institutional Experience

Objective Magnetic resonance spectroscopy (MRS) has emerged as a technique due to its ability to characterize the metabolite constituent of any lesion. We have evaluated magnetic resonance (MR) spectral patterns in different neoplastic brain lesions, using the ability of MRS in grading of gliomas. MRS also helps in differentiating between high-grade glioma and metastases.

Method A retrospective observational study in histologically confirmed cases of brain neoplasms in which MRS was performed as a part of preoperative MR imaging. The pattern of metabolite peak was observed and means with standard deviation of different metabolite ratios (choline/creatine, choline/N-acetylaspartate [NAA], NAA/creatine) were calculated for different tumors. Analysis was done to see statistically significant differences in metabolite ratios of different grades of gliomas and to differentiate high-grade gliomas from metastases.

Result A total of 61 cases with brain tumor were included in the study. Of which, 20 cases were of gliomas, 11 metastases, 9 meningiomas, 4 dysembryoplastic neuroepithelial tumors, 6 pituitary macroadenomas, 4 trigeminal schwannomas, 3 craniopharyngiomas, 2 acoustic schwannomas, and 2 medulloblastomas. Statistically significant differences in ratios of metabolite peaks were noted between different grades of gliomas and for high-grade glioma versus metastases.

Conclusion MRS compliments the MR imaging and stands out as problem-solving method to distinguish neoplastic lesions in selected cases and also has a role in grading of gliomas and in differentiation of types of malignancies.

Convolutional neural networks to predict brain tumor grades and Alzheimer’s disease with MR spectroscopic imaging data

Purpose

To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer’s disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.

Methods

A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel.

Results

Our CNN method separated glioma grades 3 and 4 and identified Alzheimer’s disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature.

Conclusion

Shallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

The road-map for establishment of a prognostic molecular marker panel in glioma using liquid biopsy: current status and future directions

Gliomas are primary intracranial tumors with defined molecular markers available for precise diagnosis. The prognosis of glioma is bleak as there is an overlook of the dynamic crosstalk between tumor cells and components of the microenvironment. Herein, different phases of gliomagenesis are presented with reference to the role and involvement of secreted proteomic markers at various stages of tumor initiation and development. The secreted markers of inflammatory response, namely interleukin-6, tumor necrosis factor-α, interferon-ϒ, and kynurenine, proliferation markers human telomerase reverse transcriptase and microtubule-associated-protein-Tau, and stemness marker human-mobility-group-AThook-1 are involved in glial tumor initiation and growth. Further, hypoxia and angiogenic factors, heat-shock-protein-70, endothelial-growth-factor-receptor-1 and vascular endothelial growth factor play a major role in promoting vascularization and tumor volume expansion. Eventually, molecules such as matrix-metalloprotease-7 and intercellular adhesion molecule-1 contribute to the degradation and remodeling of the extracellular matrix, ultimately leading to glioma progression. Our study delineates the roadmap to develop and evaluate a non-invasive panel of secreted biomarkers using liquid biopsy for precisely evaluating disease progression, to accomplish a clinical translation.

Brain abscess in immunocompetent patients: recent findings

PURPOSE OF REVIEW

We conducted a systematic review of the literature to update findings on the epidemiology and the management of cerebral abscesses in immunocompetent patients.

RECENT FINDINGS

Observational studies suggest that the overall prognosis has improved over the last decades but mortality rates remain high. Several parameters may contribute to a better prognosis, including the identification of common risk factors for brain abscess, the systematic use of brain MRI at diagnosis, the implementation of appropriate neurosurgical and microbiological techniques for diagnosis, the optimization of the antibacterial treatment based on epidemiology and pharmacokinetic/pharmacodynamic studies, and a long-term follow-up for detection of secondary complications. Outcome research on brain abscess is mainly based on observational studies. Randomized controlled trials have yet to be performed to identify clinically relevant interventions associated with improved patient-centered outcomes.

SUMMARY

Our review highlights the importance of a multidisciplinary approach to optimize brain abscess management both at the acute phase and in the long-term. Randomized controlled studies are urgently needed to identify interventions associated with improved outcomes.

Quantitative Synthetic Magnetic Resonance Imaging for Brain Metastases: A Feasibility Study

Simple Summary

This preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. In addition, synthetic MR could provide contrast images analogous to standard T1- and T2-weighted images. The reproducibility and repeatability of this method have been previously established for brain imaging. This study reports and analyzes the quantitative T1 and T2 values for 11 BM patients (17 BM lesions) with a total of 82 regions of interest (ROIs) delineated by an experienced neuroradiologist. The initial results, which need to be further validated in a larger patient cohort, demonstrated the ability of T1 and T2 metric values to characterize BMs and normal-appearing brain tissues. The T1 and T2 metrics could be potential surrogate biomarkers for BM free water content (cellularity) and tumor morphology, respectively.

Abstract

The present preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. We acquired synthetic MRI data from 11 BM patients on a 3T scanner. A multiple-dynamic multiple-echo (MDME) sequence was used for data acquisition and synthetic image reconstruction, including post-processing. MDME is a multi-contrast sequence that enables absolute quantification of physical tissue properties, including T1 and T2, independent of the scanner settings. In total, 82 regions of interest (ROIs) were analyzed, which were obtained from both normal-appearing brain tissue and BM lesions. The mean values obtained from the 48 normal-appearing brain tissue regions and 34 ROIs of BM lesions (T1 and T2) were analyzed using standard statistical methods. The mean T1 and T2 values were 1143 ms and 78 ms, respectively, for normal-appearing gray matter, 701 ms and 64 ms for white matter, and 4206 ms and 390 ms for cerebrospinal fluid. For untreated BMs, the mean T1 and T2 values were 1868 ms and 100 ms, respectively, and 2211 ms and 114 ms for the treated group. The quantitative T1 and T2 values generated from synthetic MRI can characterize BM and normal-appearing brain tissues.

T2-Fluid-Attenuated Inversion Recovery Mismatch Sign in Grade II and III Gliomas: Is There a Coexisting T2-Diffusion-Weighted Imaging Mismatch?

OBJECTIVE

To determine whether the T2 fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign in diffuse gliomas is associated with an equivalent pattern of disparity in signal intensities when comparing T2- and diffusion-weighted imaging (DWI).

METHODS

The level of correspondence between T2-FLAIR and T2-DWI evaluations in 34 World Health Organization grade II/III gliomas and interreader agreement among 3 neuroradiologists were assessed by calculating intraclass correlation coefficient and κ statistics, respectively. Tumoral apparent diffusion coefficient values were compared using t test.

RESULTS

There was an almost perfect correspondence between the 2 mismatch signs (intraclass correlation coefficient = 0.824 [95% confidence interval, 0.68-0.91]) that were associated with higher mean tumoral apparent diffusion coefficient (P < 0.01). Interreader agreement was substantial for T2-FLAIR (Fleiss κ = 0.724) and moderate for T2-DWI comparisons (Fleiss κ = 0.589) (P < 0.001).

CONCLUSIONS

The T2-FLAIR mismatch sign is usually reflected by a distinct microstructural pattern on DWI. The management of this tumor subtype may benefit from specifically tailored imaging assessments.

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma-a narrative review

This review addresses the challenges of primary central nervous system (CNS) lymphoma diagnosis, assessment of treatment response, and detection of recurrence. Primary CNS lymphoma is a rare form of extra-nodal non-Hodgkin lymphoma that can involve brain, spinal cord, leptomeninges, and eyes. Primary CNS lymphoma lesions are most commonly confined to the white matter or deep cerebral structures such as basal ganglia and deep periventricular regions. Contrast-enhanced magnetic resonance imaging (MRI) is the standard diagnostic modality employed by neuro-oncologists. MRI often shows common morphological features such as a single or multiple uniformly well-enhancing lesions without necrosis but with moderate surrounding edema. Other brain tumors or inflammatory processes can show similar radiological patterns, making differential diagnosis difficult. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) has selected utility in cerebral lymphoma, especially in diagnosis. Primary CNS lymphoma can sometimes present with atypical findings on MRI and FDG PET, such as disseminated disease, non-enhancing or ring-like enhancing lesions. The complementary strengths of PET and MRI have led to the development of combined PET-MR systems, which in some cases may improve lesion characterization and detection. By highlighting active developments in this field, including advanced MRI sequences, novel radiotracers, and potential imaging biomarkers, we aim to spur interest in sophisticated imaging approaches.

Observing deep radiomics for the classification of glioma grades

Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, a technical challenge lies in the difficulty of determining which types of internal representation are associated with a specific task, because feature vectors can vary dynamically according to individual inputs. Here, based on the magnetic resonance imaging (MRI) of gliomas, we propose a novel method to extract a shareable set of feature vectors that encode various parts in tumor imaging phenotypes. By applying vector quantization to latent representations, features extracted by an encoder are replaced with a fixed set of feature vectors. Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics. Using deep radiomics, a classifier is established using logistic regression to predict the glioma grade with 90% accuracy. We also devise an algorithm to visualize the image region encoded by each feature vector, and demonstrate that the classification model preferentially relies on feature vectors associated with the presence or absence of contrast enhancement in tumor regions. Our proposal provides a data-driven approach to enhance the understanding of the imaging appearance of gliomas.

Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

PURPOSE OF REVIEW

This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET).

RECENT FINDINGS

Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.

Whole-body magnetic resonance imaging for prostate cancer assessment: Current status and future directions

Over the past decade, updated definitions for the different stages of prostate cancer and risk for distant disease, along with the advent of new therapies, have remarkably changed the management of patients. The two expectations from imaging are accurate staging and appropriate assessment of disease response to therapies. Modern, next-generation imaging (NGI) modalities, including whole-body magnetic resonance imaging (WB-MRI) and nuclear medicine (most often prostate-specific membrane antigen [PSMA] positron emission tomography [PET]/computed tomography [CT]) bring added value to these imaging tasks. WB-MRI has proven its superiority over bone scintigraphy (BS) and CT for the detection of distant metastasis, also providing reliable evaluations of disease response to treatment. Comparison of the effectiveness of WB-MRI and molecular nuclear imaging techniques with regard to indications and the definition of their respective/complementary roles in clinical practice is ongoing. This paper illustrates the evolution of WB-MRI imaging protocols, defines the current state-of-the art, and highlights the latest developments and future challenges. The paper presents and discusses WB-MRI indications in the care pathway of men with prostate cancer in specific key situations: response assessment of metastatic disease, "all in one" cancer staging, and oligometastatic disease.