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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 37060

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Special Issue Editors

Dr. Fulvio Zaccagna

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Guest Editor

1. Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
2. Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
Interests: biomedical image analysis; radiomics; machine learning; neuroimaging; neuro-oncology; metabolic imaging

Dr. Leonardo Rundo

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Guest Editor

Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
Interests: biomedical image analysis; radiogenomics; machine learning; computational Intelligence; high-performance computing
Special Issues, Collections and Topics in MDPI journals

Dr. James T. Grist

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Guest Editor

Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
Interests: metabolic imaging, hyperpolarization, neuroimaging, neuroradiology, pediatric neuro-oncology, multiple sclerosis, machine learning

Special Issue Information

Dear Colleagues,

Brain tumors are extremely heterogeneous, both morphologically and biologically, which contributes to a very poor prognosis despite significant advances in their oncological management. Conventional imaging techniques demonstrate many aspects of tumor heterogeneity, but current imaging is insufficient for thorough characterization, therapy assessment and prognosis prediction.

Novel noninvasive methods of in vivo tissue characterization may contribute to this area of unmet need, revealing and quantifying metabolism and biology in new ways, which can help to characterize these tumors, predict their prognosis, and evaluate their response to therapy more accurately. Moreover, refined and more sophisticated imaging techniques could help in furthering our knowledge of brain tumors, potentially informing new treatment strategies.

We encourage the submission of original research and review articles on novel acquisition and post-processing techniques for both adult and pediatric brain tumor imaging, encompassing (but not limited to) magnetic resonance imaging, molecular imaging, and advanced computational methods for image analysis.

Dr. Fulvio Zaccagna
Dr. Leonardo Rundo
Dr. James T. Grist
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

brain tumors
gliomas
brain metastases
neuroimaging
neuroradiology
magnetic resonance imaging
magnetic resonance spectroscopy
computed tomography
molecular imaging
positron emission tomography
tumor metabolism
heterogeneity
biomedical image analysis and processing
computer-assisted tumor detection and segmentation
tumor classification
therapy response prediction
radiomics
radiogenomics
artificial intelligence
applied machine learning
precision medicine

Published Papers (13 papers)

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Research

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21 pages, 2261 KiB

Open AccessArticle

A Novel Lightweight CNN Architecture for the Diagnosis of Brain Tumors Using MR Images

by Kamireddy Rasool Reddy and Ravindra Dhuli

Diagnostics 2023, 13(2), 312; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13020312 - 14 Jan 2023

Cited by 6 | Viewed by 1707

Abstract

Over the last few years, brain tumor-related clinical cases have increased substantially, particularly in adults, due to environmental and genetic factors. If they are unidentified in the early stages, there is a risk of severe medical complications, including death. So, early diagnosis of brain tumors plays a vital role in treatment planning and improving a patient’s condition. There are different forms, properties, and treatments of brain tumors. Among them, manual identification and classification of brain tumors are complex, time-demanding, and sensitive to error. Based on these observations, we developed an automated methodology for detecting and classifying brain tumors using the magnetic resonance (MR) imaging modality. The proposed work includes three phases: pre-processing, classification, and segmentation. In the pre-processing, we started with the skull-stripping process through morphological and thresholding operations to eliminate non-brain matters such as skin, muscle, fat, and eyeballs. Then we employed image data augmentation to improve the model accuracy by minimizing the overfitting. Later in the classification phase, we developed a novel lightweight convolutional neural network (lightweight CNN) model to extract features from skull-free augmented brain MR images and then classify them as normal and abnormal. Finally, we obtained infected tumor regions from the brain MR images in the segmentation phase using a fast-linking modified spiking cortical model (FL-MSCM). Based on this sequence of operations, our framework achieved 99.58% classification accuracy and 95.7% of dice similarity coefficient (DSC). The experimental results illustrate the efficiency of the proposed framework and its appreciable performance compared to the existing techniques. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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11 pages, 2483 KiB

Open AccessArticle

Multiparametric Characterization of Intracranial Gliomas Using Dynamic [18F]FET-PET and Magnetic Resonance Spectroscopy

by Thomas Pyka, Iwona Krzyzanowska, Axel Rominger, Claire Delbridge, Bernhard Meyer, Tobias Boeckh-Behrens, Claus Zimmer and Jens Gempt

Diagnostics 2022, 12(10), 2331; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12102331 - 27 Sep 2022

Cited by 2 | Viewed by 1411

Abstract

Both static and dynamic O-(2-[18F]fluoroethyl)-l-tyrosine-(FET)-PET and 1H magnetic resonance spectroscopy (MRS) are useful tools for grading and prognostication in gliomas. However, little is known about the potential of multimodal imaging comprising both procedures. We therefore acquired NAA/Cr and Cho/Cr ratios in multi-voxel MRS as well as FET-PET parameters in 67 glioma patients and determined multiparametric parameter combinations. Using receiver operating characteristics, differentiation between low-grade and high-grade glioma was possible by static FET-PET (area under the curve (AUC) 0.86, p = 0.001), time-to-peak (TTP; AUC 0.79, p = 0.049), and using the Cho/Cr ratio (AUC 0.72, p = 0.039), while the multimodal analysis led to improved discrimination with an AUC of 0.97 (p = 0.001). In order to distinguish glioblastoma from non-glioblastoma, MRS (NAA/Cr ratio, AUC 0.66, p = 0.031), and dynamic FET-PET (AUC 0.88, p = 0.001) were superior to static FET imaging. The multimodal analysis increased the accuracy with an AUC of 0.97 (p < 0.001). In the survival analysis, PET parameters, but not spectroscopy, were significantly correlated with overall survival (OS, static PET p = 0.014, TTP p = 0.012), still, the multiparametric analysis, including MRS, was also useful for the prediction of OS (p = 0.002). In conclusion, FET-PET and MRS provide complementary information to better characterize gliomas before therapy, which is particularly interesting with respect to the increasing use of hybrid PET/MRI for brain tumors. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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12 pages, 4000 KiB

Open AccessArticle

Arterial Spin Labeling Perfusion in Determining the IDH1 Status and Ki-67 Index in Brain Gliomas

by Artem I. Batalov, Natalia E. Zakharova, Ivan V. Chekhonin, Eduard L. Pogosbekyan, Anna V. Sudarikova, Sergey A. Goryainov, Anna A. Shulgina, Artem Yu. Belyaev, Dmirti Yu. Usachev and Igor N. Pronin

Diagnostics 2022, 12(6), 1444; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12061444 - 12 Jun 2022

Cited by 3 | Viewed by 1561

Abstract

The aim of the study was to evaluate the relationship between tumor blood flow (TBF) measured by the pseudo-continuous arterial spin labeling (PCASL) method and IDH1 mutation status of gliomas as well as Ki-67 proliferative index. Methods. The study included 116 patients with newly diagnosed gliomas of various grades. They received no chemotherapy or radiotherapy before MRI. IDH1 status assessment was performed after tumor removal in 106 cases—48 patients were diagnosed with wildtype gliomas (Grade 1–2—6 patients, Grade 3–4—42 patients) and 58 patients were diagnosed with mutant forms of gliomas (Grade 1–2—28 patients, Grade 3–4—30 patients). In 64 cases out of 116 Ki-67 index was measured. Absolute and normalized tumor blood flow values were measured on 3D PCASL maps. Results. TBF and normalized TBF (nTBF) in wildtype gliomas were significantly higher than in IDH1-mutant gliomas (p < 0.001). ASL perfusion showed high values of sensitivity and specificity in the differential diagnosis of gliomas with distinct IDH1 status (for TBF: specificity 75%, sensitivity 77.6%, AUC 0.783, cutoff 80.57 mL/100 g/min, for nTBF: specificity 77.1%, sensitivity 79.3%, AUC 0.791, cutoff 4.7). TBF and nTBF in wildtype high-grade gliomas (HGG) were significantly higher than in mutant forms (p < 0.001). ASL perfusion showed the following values of sensitivity and specificity in the diagnosis of mutant HGG and wildtype HGG (for TBF: specificity 83.3%, sensitivity 60%, AUC 0.719, cutoff 84.18 mL/100 g/min, for nTBF: specificity 88.1%, sensitivity 60%, AUC 0.729, cutoff 4.7). There was a significant positive correlation between tumor blood flow and Ki-67 (for TBF Rs = 0.63, for nTBF Rs = 0.61). Conclusion. ASL perfusion may be an informative factor in determining the IDH1 status in brain gliomas preoperative and tumor proliferative activity. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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14 pages, 856 KiB

Open AccessArticle

Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI

by Kaoutar Ben Ahmed, Lawrence O. Hall, Dmitry B. Goldgof and Robert Gatenby

Diagnostics 2022, 12(2), 345; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12020345 - 29 Jan 2022

Cited by 6 | Viewed by 2496

Abstract

Glioma is the most common type of primary malignant brain tumor. Accurate survival time prediction for glioma patients may positively impact treatment planning. In this paper, we develop an automatic survival time prediction tool for glioblastoma patients along with an effective solution to the limited availability of annotated medical imaging datasets. Ensembles of snapshots of three dimensional (3D) deep convolutional neural networks (CNN) are applied to Magnetic Resonance Image (MRI) data to predict survival time of high-grade glioma patients. Additionally, multi-sequence MRI images were used to enhance survival prediction performance. A novel way to leverage the potential of ensembles to overcome the limitation of labeled medical image availability is shown. This new classification method separates glioblastoma patients into long- and short-term survivors. The BraTS (Brain Tumor Image Segmentation) 2019 training dataset was used in this work. Each patient case consisted of three MRI sequences (T1CE, T2, and FLAIR). Our training set contained 163 cases while the test set included 46 cases. The best known prediction accuracy of 74% for this type of problem was achieved on the unseen test set. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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13 pages, 2088 KiB

Open AccessArticle

Effects of Multi-Shell Free Water Correction on Glioma Characterization

by Lea Starck, Fulvio Zaccagna, Ofer Pasternak, Ferdia A. Gallagher, Renate Grüner and Frank Riemer

Diagnostics 2021, 11(12), 2385; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11122385 - 17 Dec 2021

Cited by 4 | Viewed by 2556

Abstract

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. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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7 pages, 1891 KiB

Open AccessCommunication

Detection of 2-Hydroxyglutarate by 3.0-Tesla Magnetic Resonance Spectroscopy in Gliomas with Rare IDH Mutations: Making Sense of “False-Positive” Cases

by Manabu Natsumeda, Hironaka Igarashi, Ramil Gabdulkhaev, Haruhiko Takahashi, Kunio Motohashi, Ryosuke Ogura, Jun Watanabe, Yoshihiro Tsukamoto, Kouichirou Okamoto, Akiyoshi Kakita, Tsutomu Nakada and Yukihiko Fujii

Diagnostics 2021, 11(11), 2129; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11112129 - 16 Nov 2021

Cited by 4 | Viewed by 1572

Abstract

We have previously published a study on the reliable detection of 2-hydroxyglutarate (2HG) in lower-grade gliomas by magnetic resonance spectroscopy (MRS). In this short article, we re-evaluated five glioma cases originally assessed as isocitrate dehydrogenase (IDH) wildtype, which showed a high accumulation of 2HG, and were thought to be false-positives. A new primer was used for the detection of IDH2 mutation by Sanger sequencing. Adequate tissue for DNA analysis was available in 4 out of 5 cases. We found rare IDH2 mutations in two cases, with IDH2 R172W mutation in one case and IDH2 R172K mutation in another case. Both cases had very small mutant peaks, suggesting that the tumor volume was low in the tumor samples. Thus, the specificity of MRS for detecting IDH1/2 mutations was higher (81.3%) than that originally reported (72.2%). The detection of 2HG by MRS can aid in the diagnosis of rare, non-IDH1-R132H IDH1 and IDH2 mutations in gliomas. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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11 pages, 3332 KiB

Open AccessArticle

Predict Treatment Response by Magnetic Resonance Diffusion Weighted Imaging: A Preliminary Study on 46 Meningiomas Treated with Proton-Therapy

by Paola Feraco, Daniele Scartoni, Giulia Porretti, Riccardo Pertile, Davide Donner, Lorena Picori and Dante Amelio

Diagnostics 2021, 11(9), 1684; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11091684 - 15 Sep 2021

Cited by 4 | Viewed by 1644

Abstract

Objective: a considerable subgroup of meningiomas (MN) exhibit indolent and insidious growth. Strategies to detect earlier treatment responses based on tumour biology rather than on size can be useful. We aimed to characterize therapy-induced changes in the apparent diffusion coefficient (ADC) of MN treated with proton-therapy (PT), determining whether the pre- and early post-treatment ADC values may predict tumour response. Methods: Forty-four subjects with MN treated with PT were retrospectively enrolled. All patients underwent conventional magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) at baseline and each 3 months for a follow-up period up to 36 months after the beginning of PT. Mean relative ADC (rADCm) values of 46 MN were measured at each exam. The volume variation percentage (VV) for each MN was calculated. The Wilcoxon test was used to assess the differences in rADCm values between pre-treatment and post-treatment exams. Patients were grouped in terms of VV (threshold −20%). A p < 0.05 was considered statistically significant for all the tests. Results: A significant progressive increase of rADCm values was detected at each time point when compared to baseline rADCm (p < 0.05). Subjects that showed higher pre-treatment rADCm values had no significant volume changes or showed volume increase, while subjects that showed a VV < −20% had significantly lower pre-treatment rADCm values. Higher and earlier rADCm increases (3 months) are related to greater volume reduction. Conclusion: In MN treated with PT, pre-treatment rADCm values and longitudinal rADCm changes may predict treatment response. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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Review

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16 pages, 403 KiB

Open AccessReview

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

by Christian di Noia, James T. Grist, Frank Riemer, Maria Lyasheva, Miriana Fabozzi, Mauro Castelli, Raffaele Lodi, Caterina Tonon, Leonardo Rundo and Fulvio Zaccagna

Diagnostics 2022, 12(9), 2125; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12092125 - 01 Sep 2022

Cited by 6 | Viewed by 3627

Abstract

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. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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46 pages, 6397 KiB

Open AccessReview

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

by Yuting Xie, Fulvio Zaccagna, Leonardo Rundo, Claudia Testa, Raffaele Agati, Raffaele Lodi, David Neil Manners and Caterina Tonon

Diagnostics 2022, 12(8), 1850; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12081850 - 31 Jul 2022

Cited by 49 | Viewed by 5680

Abstract

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. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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12 pages, 3534 KiB

Open AccessReview

The Use of ¹⁸F-FET-PET-MRI in Neuro-Oncology: The Best of Both Worlds—A Narrative Review

by Tineke van de Weijer, Martijn P. G. Broen, Rik P. M. Moonen, Ann Hoeben, Monique Anten, Koos Hovinga, Inge Compter, Jochem A. J. van der Pol, Cristina Mitea, Toine M. Lodewick, Arnaud Jacquerie, Felix M. Mottaghy, Joachim E. Wildberger and Alida A. Postma

Diagnostics 2022, 12(5), 1202; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12051202 - 11 May 2022

Cited by 4 | Viewed by 5268

Abstract

Gliomas are the most frequent primary tumors of the brain. They can be divided into grade II-IV astrocytomas and grade II-III oligodendrogliomas, based on their histomolecular profile. The prognosis and treatment is highly dependent on grade and well-identified prognostic and/or predictive molecular markers. Multi-parametric MRI, including diffusion weighted imaging, perfusion, and MR spectroscopy, showed increasing value in the non-invasive characterization of specific molecular subsets of gliomas. Radiolabeled amino-acid analogues, such as 18F-FET, have also been proven valuable in glioma imaging. These tracers not only contribute in the diagnostic process by detecting areas of dedifferentiation in diffuse gliomas, but this technique is also valuable in the follow-up of gliomas, as it can differentiate pseudo-progression from real tumor progression. Since multi-parametric MRI and 18F-FET PET are complementary imaging techniques, there may be a synergistic role for PET-MRI imaging in the neuro-oncological imaging of primary brain tumors. This could be of value for both primary staging, as well as during treatment and follow-up. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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15 pages, 2643 KiB

Open AccessSystematic Review

The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis

by Yi Luo, Siqi Zhang, Weiting Tan, Guisen Lin, Yijiang Zhuang and Hongwu Zeng

Diagnostics 2022, 12(11), 2796; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12112796 - 15 Nov 2022

Cited by 1 | Viewed by 1483

Abstract

Medulloblastoma (MB) is considered the most common and highly malignant posterior fossa tumor (PFT) in children. The accurate preoperative diagnosis of MB is beneficial in choosing the appropriate surgical methods and treatment strategies. Diffusion-weighted imaging (DWI) has improved the accuracy of differential diagnosis of posterior fossa tumors. Nonetheless, further studies are needed to confirm its value for clinical application. This study aimed to evaluate the performance of DWI in differentiating MB from other PFT. A literature search was conducted using databases PubMed, Embase, and Web of Science for studies reporting the diagnostic performance of DWI for PFT from January 2000 to January 2022. A bivariate random-effects model was employed to evaluate the pooled sensitivities and specificities. A univariable meta-regression analysis was used to assess relevant factors for heterogeneity, and subgroup analyses were performed. A total of 15 studies with 823 patients were eligible for data extraction. Overall pooled sensitivity and specificity of DWI were 0.94 (95% confident interval [CI]: 0.89–0.97) and 0.94 (95% CI: 0.90–0.96) respectively. The area under the curve (AUC) of DWI was 0.98 (95% CI: 0.96–0.99). Heterogeneity was found in the sensitivity (I² = 62.59%) and the specificity (I² = 35.94%). Magnetic field intensity, region of interest definition and DWI diagnostic parameters are the factors that affect the diagnostic performance of DWI. DWI has excellent diagnostic accuracy for differentiating MB from other PFT. Hence, it is necessary to set DWI as a routine examination sequence for posterior fossa tumors. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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15 pages, 4888 KiB

Open AccessCase Report

Diffuse Leptomeningeal Glioneuronal Tumour with 9-Year Follow-Up: Case Report and Review of the Literature

by Milda Sarkinaite, Indre Devyziene, Jurgita Makstiene, Algimantas Matukevicius and Rymante Gleizniene

Diagnostics 2022, 12(2), 342; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12020342 - 28 Jan 2022

Cited by 6 | Viewed by 3579

Abstract

In 2016, the World Health Organisation Classification (WHO) of Tumours was updated with diffuse leptomeningeal glioneuronal tumour (DLGNT) as a provisional unit of mixed neuronal and glial tumours. Here, we report a DLGNT that has been re-diagnosed with the updated WHO classification, with clinical features, imaging, and histopathological findings and a 9-year follow-up. A 16-year-old girl presented with headache, vomiting, and vertigo. Magnetic resonance imaging (MRI) demonstrated a hyperintense mass with heterogenous enhancement in the right cerebellopontine angle and internal auditory canal. No leptomeningeal involvement was seen. The histological examination revealed neoplastic tissue of moderate cellularity formed mostly by oligodendrocyte-like cells. Follow-up MRI scans demonstrated cystic lesions in the subarachnoid spaces in the brain with vivid leptomeningeal enhancement. Later spread of the tumour was found in the spinal canal. On demand biopsy samples were re-examined, and pathological diagnosis was identified as DLGNT. In contrast to most reported DLGNTs, the tumour described in this manuscript did not present with diffuse leptomeningeal spread, but later presented with leptomeningeal involvement in the brain and spinal cord. Our case expands the spectrum of radiological features, provides a long-term clinical and radiological follow-up, and highlights the major role of molecular genetic testing in unusual cases. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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6 pages, 4856 KiB

Open AccessCase Report

Acoustic Neurinoma with Synchronous Ipsilateral Cerebellopontine Angle Lipoma: A Case Report and Review of the Literature

by Takahiro Kanaya, Yasuo Murai, Kanako Yui, Shun Sato and Akio Morita

Diagnostics 2022, 12(1), 120; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12010120 - 05 Jan 2022

Cited by 3 | Viewed by 2752

Abstract

Lipomas of the cerebellopontine angle (CPA) and internal auditory canal (IAC) are relatively rare tumors. Acoustic neurinoma is the most common tumor in this location, which often causes hearing loss, vertigo, and tinnitus. Occasionally, this tumor compresses the brainstem, prompting surgical resection. Lipomas in this area may cause symptoms similar to neurinoma. However, they are not considered for surgical treatment because their removal may result in several additional deficits. Conservative therapy and repeated magnetic resonance imaging examinations for CPA/IAC lipomas are standard measures for preserving cranial nerve function. Herein, we report a case of acoustic neurinoma and CPA lipoma occurring in close proximity to each other ipsilaterally. The main symptom was hearing loss without facial nerve paralysis. Therefore, facial nerve injury had to be avoided. Considering the anatomical relationships among the tumors, cranial nerves, and CPA/IAC lipoma, we performed total surgical removal of the acoustic neurinoma. We intentionally left the lipoma untreated, which enabled facial nerve preservation. This report may be a useful reference for the differential diagnosis of similar cases in the future. Full article

(This article belongs to the Special Issue Brain Tumor Imaging)

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