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Purpose: The purpose of this work is to validate a simple and versatile integrated variable flip angle (VFA) method for mapping B 1 in hyperpolarized MRI, which can be used to correct signal variations due to coil inhomogeneity., Theory and Methods: Simulations were run to assess performance of the VFA B 1 mapping method compared to the currently used constant flip angle (CFA) approach. Simulation results were used to inform the design of VFA sequences, validated in four volunteers for hyperpolarized xenon-129 imaging of the lungs and another four volunteers for hyperpolarized carbon-13 imaging of the human brain. B 1 maps obtained were used to correct transmit and receive inhomogeneity in the images., Results: Simulations showed improved performance of the VFA approach over the CFA approach with reduced sensitivity to T 1 . For xenon-129, the B 1 maps accurately reflected the variation of signal depolarization, but in some cases could not be used to correct for coil receive inhomogeneity due to a lack of transmit-receive reciprocity resulting from suboptimal coil positioning. For carbon-13, the B 1 maps showed good agreement with a separately acquired B 1 map of a phantom and were effectively used to correct coil-induced signal inhomogeneity., Conclusion: A simple, versatile, and effective VFA B 1 mapping method was implemented and evaluated. Inclusion of the B 1 mapping method in hyperpolarized imaging studies can enable more robust signal quantification., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To assess radiomics and deep learning (DL) methods in identifying symptomatic Carotid Artery Disease (CAD) from carotid CT angiography (CTA) images. We further compare the performance of these novel methods to the conventional calcium score., Methods: Carotid CT angiography (CTA) images from symptomatic patients (ischaemic stroke/transient ischaemic attack within the last 3 months) and asymptomatic patients were analysed. Carotid arteries were classified into culprit, non-culprit and asymptomatic. The calcium score was assessed using the Agatston method. 93 radiomic features were extracted from regions-of-interest drawn on 14 consecutive CTA slices. For DL, convolutional neural networks (CNNs) with and without transfer learning were trained directly on CTA slices. Predictive performance was assessed over 5-fold cross validated AUC scores. SHAP and GRAD-CAM algorithms were used for explainability., Results: 132 carotid arteries were analysed (41 culprit, 41 non-culprit, and 50 asymptomatic). For asymptomatic vs symptomatic arteries, radiomics attained a mean AUC of 0.96(± 0.02), followed by DL 0.86(± 0.06) and then calcium 0.79(± 0.08). For culprit vs non-culprit arteries, radiomics achieved a mean AUC of 0.75(± 0.09), followed by DL 0.67(± 0.10) and then calcium 0.60(± 0.02). For multi-class classification, the mean AUCs were 0.95(± 0.07), 0.79(± 0.05), and 0.71(± 0.07) for radiomics, DL and calcium, respectively. Explainability revealed consistent patterns in the most important radiomic features., Conclusions: Our study highlights the potential of novel image analysis techniques in extracting quantitative information beyond calcification in the identification of CAD. Though further work is required, the transition of these novel techniques into clinical practice may eventually facilitate better stroke risk stratification., Competing Interests: The authors declare no competing interests., (© 2024 Published by Elsevier Ltd.)

Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T 2 w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T 1 w, T 2 w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment., (© 2024. The Author(s).)

Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious "black boxes". The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.

Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications., (© 2023. Springer Nature Limited.)

Background: Multiple Sclerosis lesions in the brain and spinal cord can lead to different symptoms, including cognitive and mood changes. In this study we explore the temporal relationship between early microstructural changes in subcortical volumes and cognitive and emotional function in a longitudinal cohort study of patients with relapsing-remitting Multiple Sclerosis., Methods: In vivo imaging in forty-six patients with relapsing-remitting Multiple Sclerosis was performed annually over 3 years magnetic resonance imaging. Microstructural changes were estimated in subcortical structures using the free water fraction, a diffusion-based MRI metric. In parallel, patients were assessed with the Hospital Anxiety and Depression Scale amongst other tests. Predictive structural equation modeling was set up to further explore the relationship between imaging and the assessment scores. In a general linear model analysis, the cohort was split into patients with higher and lower depression scores., Results: Nearly all subcortical diffusion microstructure estimates at the baseline visit correlate with the depression score at the 2 years follow-up. The predictive nature of baseline free water estimates and depression subscores after 2 years are confirmed in the predictive structural equation modeling analysis with the thalamus showing the greatest effect size. The general linear model analysis shows patterns of MRI free water differences in the thalamus and amygdala/hippocampus area between participants with high and low depression score., Conclusions: Our data suggests a relationship between higher levels of free-water in the subcortical structures in an early stage of Multiple Sclerosis and depression symptoms at a later stage of the disease., (© 2023. The Author(s).)

Background and Purpose: Chronic hydrocephalus may develop as a sequela of aneurysmal subarachnoid hemorrhage, requiring long-term cerebrospinal fluid shunting. Several clinical predictors of chronic hydrocephalus and shunt dependence have been proposed. However, no anatomical predictors have been identified., Materials and Methods: A retrospective cohort study was performed including 61 patients with aneurysmal subarachnoid hemorrhage. Clinical characteristics were noted for each patient including presentation World Federation of Neurosurgical Societies grade, modified Fischer grade, aneurysm characteristics, requirement for acute and chronic cerebrospinal fluid diversion, and 3-month modified Rankin scale. CT images were evaluated to determine the Evans' index and to enumerate the number of arachnoid granulations. Association between the clinical characteristics with ventriculoperitoneal shunt insertion and the 3-month modified Rankin scale were assessed., Results: The initial Evans' index was positively associated with mFisher grade and age, but not the number of arachnoid granulations. 16.4% patients required insertion of a ventriculoperitoneal shunt. The number of arachnoid granulations were a significant negative predictor of ventriculoperitoneal shunt insertion [OR: 0.251 (95% CI:0.073-0.862; p = 0.028)]. There was significant difference in the number of arachnoid granulations between those with and without ventriculoperitoneal shunt ( p = 0.002). No patient with greater than 4 arachnoid granulations required a ventriculoperitoneal shunt, irrespective of severity of initial grade., Conclusion: Arachnoid granulations may be protective against the development of shunt dependent chronic hydrocephalus after aneurysmal subarachnoid hemorrhage. This is irrespective of presenting hemorrhage severity. This is a potentially novel radiologic biomarker and anatomic predictor of shunt dependence.

Background and Purpose: Tau deposition in the entorhinal cortex is the earliest pathological feature of Alzheimer's disease (AD). However, this feature has also been observed in cognitively normal (CN) individuals and those with mild cognitive impairment (MCI). The precise pathophysiology for the development of tau deposition remains unclear. We hypothesized that reduced cerebral perfusion is associated with the development of tau deposition., Methods: A subset of the Alzheimer's Disease Neuroimaging Initiative data set was utilized. Included patients had undergone arterial spin labeling perfusion MRI along with [ 18 F]flortaucipir tau PET at baseline, within 1 year of the MRI, and a follow-up at 6 years. The association between baseline cerebral blood flow (CBF) and the baseline and 6-year tau PET was assessed. Univariate and multivariate linear modeling was performed, with p <0.05 indicating significance., Results: Significant differences were found in the CBF between patients with AD and MCI, and CN individuals in the left entorhinal cortex ( p =0.013), but not in the right entorhinal cortex ( p =0.076). The difference in maximum standardized uptake value ratio between 6 years and baseline was significantly and inversely associated with the baseline mean CBF ( p =0.042, R²=0.54) in the left entorhinal cortex but not the right entorhinal cortex. Linear modeling demonstrated that CBF predicted 6-year tau deposition ( p =0.015, R²=0.11)., Conclusions: The results of this study suggest that a reduction in CBF at the entorhinal cortex precedes tau deposition. Further work is needed to understand the mechanism underlying tau deposition in aging and disease., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2023 Korean Neurological Association.)

Age-related white matter hyperintensities are associated with cognitive impairment and dementia. Venous insufficiency has recently been proposed as a potential mechanism for the development of periventricular white matter hyperintensities based on the neuroanatomic distribution. The current study assesses age related changes of the internal jugular veins and its association with white matter hyperintensities. A retrospective study was performed assessing patients with computed tomography angiography (CTA) and magnetic resonance imaging (MRI) within a 4-week window. The size of the internal jugular veins, straight sinus, vein of Galen and internal cerebral veins were measured on the CT angiography. A normalized neck venous ratio was developed. Burden of white matter hyperintensities were quantified on MRI using periventricular/deep Fazekas scores. Association was assessed using correlation analysis and multrivariate linear modeling, and differences between groups were assessed using t test, ANOVA or Kruskal-Wallis test, using p < 0.05 for significance. One hundred eighty-two patients were included with a mean age of 65.2 ± 16.8 (51.6% females). Age was correlated with the normalized neck venous ratio (r s  = 0.25, p < 0.001), and, with both, the periventricular Fazekas (r s  = 0.63, p < 0.001) and the deep Fazekas (r s  = 0.57, p < 0.001) grades. The periventricular Fazekas score was positively correlated with the normalized neck venous ratio (r s  = 0.21, p = 0.003), but not significant on multivariate analysis accounting for age. The internal jugular veins demonstrate age related increase in size, paralleling the progression of periventricular white matter hyperintensities. Age remains the strongest predictor of white matter hyperintensities. Further work is needed to evaluate any causal role of venous changes on white matter hyperintensities., (© 2022 American Association for Clinical Anatomists and the British Association for Clinical Anatomists.)

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

Objective: To explore translational biological and imaging biomarkers for sunitinib treatment before and after debulking nephrectomy in the NeoSun (European Union Drug Regulating Authorities Clinical Trials Database [EudraCT] number: 2005-004502-82) single-centre, single-arm, single-agent, Phase II trial., Patients and Methods: Treatment-naïve patients with metastatic renal cell carcinoma (mRCC) received 50 mg once daily sunitinib for 12 days pre-surgically, then post-surgery on 4 week-on, 2 week-off, repeating 6-week cycles until disease progression in a single arm phase II trial. Structural and dynamic contrast-enhanced magnet resonance imaging (DCE-MRI) and research blood sampling were performed at baseline and after 12 days. Computed tomography imaging was performed at baseline and post-surgery then every two cycles. The primary endpoint was objective response rate (Response Evaluation Criteria In Solid Tumors [RECIST]) excluding the resected kidney. Secondary endpoints included changes in DCE-MRI of the tumour following pre-surgery sunitinib, overall survival (OS), progression-free survival (PFS), response duration, surgical morbidity/mortality, and toxicity. Translational and imaging endpoints were exploratory., Results: A total of 14 patients received pre-surgery sunitinib, 71% (10/14) took the planned 12 doses. All underwent nephrectomy, and 13 recommenced sunitinib postoperatively. In all, 58.3% (seven of 12) of patients achieved partial or complete response (PR or CR) (95% confidence interval 27.7-84.8%). The median OS was 33.7 months and median PFS was 15.7 months. Amongst those achieving a PR or CR, the median response duration was 8.7 months. No unexpected surgical complications, sunitinib-related toxicities, or surgical delays occurred. Within the translational endpoints, pre-surgical sunitinib significantly increased necrosis, and reduced cluster of differentiation-31 (CD31), Ki67, circulating vascular endothelial growth factor-C (VEGF-C), and transfer constant (K Trans , measured using DCE-MRI; all P < 0.05). There was a trend for improved OS in patients with high baseline plasma VEGF-C expression (P = 0.02). Reduction in radiological tumour volume after pre-surgical sunitinib correlated with high percentage of solid tumour components at baseline (Spearman's coefficient ρ = 0.69, P = 0.02). Conversely, the percentage tumour volume reduction correlated with lower baseline percentage necrosis (coefficient = -0.51, P = 0.03)., Conclusion: Neoadjuvant studies such as the NeoSun can safely and effectively explore translational biological and imaging endpoints., (© 2021 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63-100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0-99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

Purpose To evaluate glioblastoma (GBM) metabolism by using hyperpolarized carbon 13 ( 13 C) MRI to monitor the exchange of the hyperpolarized 13 C label between injected [1- 13 C]pyruvate and tumor lactate and bicarbonate. Materials and Methods In this prospective study, seven treatment-naive patients (age [mean ± SD], 60 years ± 11; five men) with GBM were imaged at 3 T by using a dual-tuned 13 C-hydrogen 1 head coil. Hyperpolarized [1- 13 C]pyruvate was injected, and signal was acquired by using a dynamic MRI spiral sequence. Metabolism was assessed within the tumor, in the normal-appearing brain parenchyma (NABP), and in healthy volunteers by using paired or unpaired t tests and a Wilcoxon signed rank test. The Spearman ρ correlation coefficient was used to correlate metabolite labeling with lactate dehydrogenase A (LDH-A) expression and some immunohistochemical markers. The Benjamini-Hochberg procedure was used to correct for multiple comparisons. Results The bicarbonate-to-pyruvate (BP) ratio was lower in the tumor than in the contralateral NABP ( P < .01). The tumor lactate-to-pyruvate (LP) ratio was not different from that in the NABP ( P = .38). The LP and BP ratios in the NABP were higher than those observed previously in healthy volunteers ( P < .05). Tumor lactate and bicarbonate signal intensities were strongly correlated with the pyruvate signal intensity (ρ = 0.92, P < .001, and ρ = 0.66, P < .001, respectively), and the LP ratio was weakly correlated with LDH-A expression in biopsy samples (ρ = 0.43, P = .04). Conclusion Hyperpolarized 13 C MRI demonstrated variation in lactate labeling in GBM, both within and between tumors. In contrast, bicarbonate labeling was consistently lower in tumors than in the surrounding NABP. Keywords: Hyperpolarized 13 C MRI, Glioblastoma, Metabolism, Cancer, MRI, Neuro-oncology Supplemental material is available for this article. Published under a CC BY 4.0 license.

Background: Pathological brain processes may induce adaptive cortical reorganization, however, the mechanisms underlying neuroplasticity that occurs in the presence of lesions in eloquent areas are not fully explained. The aim of this study was to evaluate functional compensatory cortical activations in patients with frontal brain gliomas during a phonemic fluency task and to explore correlations with cognitive performance, white matter tracts microstructural alterations, and tumor histopathological and molecular characterization., Methods: Fifteen patients with frontal glioma were preoperatively investigated with an MRI study on a 3T scanner and a subgroup underwent an extensive neuropsychological assessment. The hemispheric laterality index (LI) was calculated through phonemic fluency task functional MRI (fMRI) activations in the frontal, parietal, and temporal lobe parcellations. Diffusion-weighted images were acquired for all patients and for a group of 24 matched healthy volunteers. Arcuate Fasciculus (AF) and Frontal Aslant Tract (FAT) tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. All patients were operated on with a resective aim and underwent adjuvant therapies, depending on the final diagnosis., Results: All patients during the phonemic fluency task fMRI showed left hemispheric dominance in temporal and parietal regions. Regarding frontal regions (i.e., frontal operculum) we found right hemispheric dominance that increases when considering only those patients with tumors located on the left side. These latter activations positively correlate with verbal and visuo-spatial short-term memory, and executive functions. No correlations were found between the left frontal operculum and cognitive performance. Furthermore, patients with IDH-1 mutation and without TERT mutation, showed higher rightward frontal operculum fMRI activations and better cognitive performance in tests measuring general cognitive abilities, semantic fluency, verbal short-term memory, and executive functions. As for white matter tracts, we found left and right AF and FAT microstructural alterations in patients with, respectively, left-sided and right-side glioma compared to controls., Conclusions: Compensatory cortical activation of the corresponding region in the non-dominant hemisphere and its association with better cognitive performance and more favorable histopathological and molecular tumor characteristics shed light on the neuroplasticity mechanisms that occur in the presence of a tumor, helping to predict the rate of post-operative deficit, with the final goal of improving patients'quality of life., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Mitolo, Zoli, Testa, Morandi, Rochat, Zaccagna, Martinoni, Santoro, Asioli, Badaloni, Conti, Sturiale, Lodi, Mazzatenta and Tonon.)

Infratemporal and pterygopalatine fossae (ITF and PPF) represent two complex paramedian skull base areas, which can be defined as jewelry boxes, containing a large number of neurovascular and osteomuscular structures of primary importance. They are in close communication with many craniofacial areas, such as nasal/paranasal sinuses, orbit, middle cranial fossa, and oral cavities. Therefore, they can be involved by tumoral, infective or inflammatory lesions spreading from these spaces. Moreover, they can be the primary site of the development of some primitive tumors. For the deep-seated location of ITF and PPF lesions and their close relationship with the surrounding functional neuro-vascular structures, their surgery represents a challenge. In the last decades, the introduction of the endoscope in skull base surgery has favored the development of an innovative anterior endonasal approach for ITF and PPF tumors: the transmaxillary-pterygoid, which gives a direct and straightforward route for these areas. It has demonstrated that it is effective and safe for the treatment of a large number of benign and malignant neoplasms, located in these fossae, avoiding extensive bone drilling, soft tissue demolition, possibly unaesthetic scars, and reducing the risk of neurological deficits. However, some limits, especially for vascular tumors or lesions with lateral extension, are still present. Based on the experience of our multidisciplinary team, we present our operative technique, surgical indications, and pre- and post-operative management protocol for patients with ITF and PPF tumors.

Differentiating aggressive clear cell renal cell carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of renal tumors using carbon-13 MRI following injection of hyperpolarized [1- 13 C]pyruvate (HP- 13 C-MRI) and validated these findings with histopathology. Nine patients with treatment-naïve renal tumors (6 ccRCCs, 1 liposarcoma, 1 pheochromocytoma, 1 oncocytoma) underwent pre-operative HP- 13 C-MRI and conventional proton ( 1 H) MRI. Multi-regional tissue samples were collected using patient-specific 3D-printed tumor molds for spatial registration between imaging and molecular analysis. The apparent exchange rate constant ( k PL ) between 13 C-pyruvate and 13 C-lactate was calculated. Immunohistochemistry for the pyruvate transporter (MCT1) from 44 multi-regional samples, as well as associations between MCT1 expression and outcome in the TCGA-KIRC dataset, were investigated. Increasing k PL in ccRCC was correlated with increasing overall tumor grade (ρ = 0.92, p = 0.009) and MCT1 expression (r = 0.89, p = 0.016), with similar results acquired from the multi-regional analysis. Conventional 1 H-MRI parameters did not discriminate tumor grades. The correlation between MCT1 and ccRCC grade was confirmed within a TCGA dataset ( p < 0.001), where MCT1 expression was a predictor of overall and disease-free survival. In conclusion, metabolic imaging using HP- 13 C-MRI differentiates tumor aggressiveness in ccRCC and correlates with the expression of MCT1, a predictor of survival. HP- 13 C-MRI may non-invasively characterize metabolic phenotypes within renal cancer.

Diffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy ( FA ). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy ( p ≤ 0.003), whereas skewness is decreased ( p ≤ 0.004). Kurtosis is significantly decreased ( p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA , which helps to disentangle the cancer heterogeneity.