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The Application of Medical Imaging in Brain Tumors
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The Application of Medical Imaging in Brain Tumors

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 16096

Special Issue Editors

Dr. Anna Barnes

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Guest Editor
Institute of Nuclear Medicine, University College London Hospitals NHS-Foundation Trust, 235 Euston Rd, London NW1 2BU, UK
Interests: imaging; PET; MRI; biomarkers
Special Issues, Collections and Topics in MDPI journals
Dr. Stefanie Catherine Thust

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Guest Editor
The Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London WC1N 3BG, UK
Interests: quantitative MRI; glioma diagnostics; advanced imaging techniques
Dr. Balaji Ganeshan

E-Mail Website
Guest Editor
Institute of Nuclear Medicine, University College London / Hospitals NHS Trust, 235 Euston Road, London NW1 2BU, UK
Interests: quantitative image analysis; biomarkers; texture-analysis; radiomics; magnetic resonance; computed tomography; diagnosis; characterisation; prognosis; treatment response; cancer

Special Issue Information

Dear Colleagues,

The application of medical imaging in brain tumours forms the basis of untreated tumour characterisation, surgical planning and long-term follow up. Advances in quantitative and physiological imaging methods have considerably enhanced our ability to depict neoplasms and to distinguish non-malignant tissue. This Special Issue of the Journal of Personalized Medicine highlights new developments in multimodal brain tumour imaging and computational analysis. We are soliciting papers focusing on, but not limited to, clinical studies, new acquisition technology in brain imaging, and novel methods of post processing, such as machine learning applied to imaging in brain tumours, biomarkers, and clinical trials. Such new opportunities in imaging technology will support rapid diagnosis and personalised therapy approaches in the future.

Dr. Anna Barnes
Dr. Stefanie Catherine Thust
Dr. Balaji Ganeshan
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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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 tumour(s)
  • Glioma
  • Magnetic resonance imaging
  • Multimodal imaging
  • Radiomics
  • Texture analysis
  • Image computation

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Published Papers (6 papers)

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Research

17 pages, 3753 KiB  
Article
Imaging-Based Patterns of Failure following Re-Irradiation for Recurrent/Progressive High-Grade Glioma
by Debanjali Datta, Archya Dasgupta, Abhishek Chatterjee, Arpita Sahu, Kajari Bhattacharya, Lilawati Meena, Kishore Joshi, Ameya Puranik, Indraja Dev, Aliasgar Moiyadi, Prakash Shetty, Vikas Singh, Vijay Patil, Nandini Menon, Epari Sridhar, Ayushi Sahay and Tejpal Gupta
J. Pers. Med. 2023, 13(4), 685; https://doi.org/10.3390/jpm13040685 - 19 Apr 2023
Viewed by 1740
Abstract
Background: Re-irradiation (ReRT) is an effective treatment modality in appropriately selected patients with recurrent/progressive high-grade glioma (HGG). The literature is limited regarding recurrence patterns following ReRT, which was investigated in the current study. Methods: Patients with available radiation (RT) contours, dosimetry, and imaging-based [...] Read more.
Background: Re-irradiation (ReRT) is an effective treatment modality in appropriately selected patients with recurrent/progressive high-grade glioma (HGG). The literature is limited regarding recurrence patterns following ReRT, which was investigated in the current study. Methods: Patients with available radiation (RT) contours, dosimetry, and imaging-based evidence of recurrence were included in the retrospective study. All patients were treated with fractionated focal conformal RT. Recurrence was detected on imaging with magnetic resonance imaging (MRI) and/ or amino-acid positron emission tomography (PET), which was co-registered with the RT planning dataset. Failure patterns were classified as central, marginal, and distant if >80%, 20–80%, or <20% of the recurrence volumes were within 95% isodose lines, respectively. Results: Thirty-seven patients were included in the current analysis. A total of 92% of patients had undergone surgery before ReRT, and 84% received chemotherapy. The median time to recurrence was 9 months. Central, marginal, and distant failures were seen in 27 (73%), 4 (11%), and 6 (16%) patients, respectively. None of the patient-, disease-, or treatment-related factors were significantly different across different recurrence patterns. Conclusion: Failures are seen predominantly within the high-dose region following ReRT in recurrent/ progressive HGG. Full article
(This article belongs to the Special Issue The Application of Medical Imaging in Brain Tumors)
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10 pages, 2309 KiB  
Article
Clinical, Imaging and Neurogenetic Features of Patients with Gliomatosis Cerebri Referred to a Tertiary Neuro-Oncology Centre
by David Doig, Lewis Thorne, Jeremy Rees, Naomi Fersht, Michael Kosmin, Sebastian Brandner, Hans Rolf Jäger and Stefanie Thust
J. Pers. Med. 2023, 13(2), 222; https://doi.org/10.3390/jpm13020222 - 27 Jan 2023
Cited by 1 | Viewed by 2139
Abstract
Introduction: Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre. Methods: We analysed demographic data, presenting [...] Read more.
Introduction: Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre. Methods: We analysed demographic data, presenting symptoms, imaging, histology and genetics, and survival in individuals referred to a multidisciplinary team meeting over a 10-year period. Results: In total, 29 patients fulfilled the inclusion criteria with a median age of 64 years. The most common presenting symptoms were neuropsychiatric (31%), seizure (24%) or headache (21%). Of 20 patients with molecular data, 15 had IDH wild-type glioblastoma, with an IDH1 mutation most common in the remainder (5/20). The median length of survival from MDT referral to death was 48 weeks (IQR 23 to 70 weeks). Contrast enhancement patterns varied between and within tumours. In eight patients who had DSC perfusion studies, five (63%) had a measurable region of increased tumour perfusion with rCBV values ranging from 2.8 to 5.7. A minority of patients underwent MR spectroscopy with 2/3 (66.6%) false-negative results. Conclusions: Gliomatosis imaging, histological and genetic findings are heterogeneous. Advanced imaging, including MR perfusion, could identify biopsy targets. Negative MR spectroscopy does not exclude the diagnosis of glioma. Full article
(This article belongs to the Special Issue The Application of Medical Imaging in Brain Tumors)
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16 pages, 8036 KiB  
Article
Multiparametric Magnetic Resonance Imaging Correlates of Isocitrate Dehydrogenase Mutation in WHO high-Grade Astrocytomas
by Arpita Sahu, Nandakumar G. Patnam, Jayant Sastri Goda, Sridhar Epari, Ayushi Sahay, Ronny Mathew, Amit Kumar Choudhari, Subhash M. Desai, Archya Dasgupta, Abhishek Chatterjee, Pallavi Pratishad, Prakash Shetty, Ali Asgar Moiyadi and Tejpal Gupta
J. Pers. Med. 2023, 13(1), 72; https://doi.org/10.3390/jpm13010072 - 29 Dec 2022
Cited by 3 | Viewed by 2825
Abstract
Purpose and background: Isocitrate dehydrogenase (IDH) mutation and O-6 methyl guanine methyl transferase (MGMT) methylation are surrogate biomarkers of improved survival in gliomas. This study aims at studying the ability of semantic magnetic resonance imaging (MRI) features to predict the IDH mutation status [...] Read more.
Purpose and background: Isocitrate dehydrogenase (IDH) mutation and O-6 methyl guanine methyl transferase (MGMT) methylation are surrogate biomarkers of improved survival in gliomas. This study aims at studying the ability of semantic magnetic resonance imaging (MRI) features to predict the IDH mutation status confirmed by the gold standard molecular tests. Methods: The MRI of 148 patients were reviewed for various imaging parameters based on the Visually AcceSAble Rembrandt Images (VASARI) study. Their IDH status was determined using immunohistochemistry (IHC). Fisher’s exact or chi-square tests for univariate and logistic regression for multivariate analysis were used. Results: Parameters such as mild and patchy enhancement, minimal edema, necrosis < 25%, presence of cysts, and less rCBV (relative cerebral blood volume) correlated with IDH mutation. The median age of IDH-mutant and IDH-wild patients were 34 years (IQR: 29–43) and 52 years (IQR: 45–59), respectively. Mild to moderate enhancement was observed in 15/19 IDH-mutant patients (79%), while 99/129 IDH-wildtype (77%) had severe enhancement (p-value <0.001). The volume of edema with respect to tumor volume distinguished IDH-mutants from wild phenotypes (peritumoral edema volume < tumor volume was associated with higher IDH-mutant phenotypes; p-value < 0.025). IDH-mutant patients had a median rCBV value of 1.8 (IQR: 1.4–2.0), while for IDH-wild phenotypes, it was 2.6 (IQR: 1.9–3.5) {p-value = 0.001}. On multivariate analysis, a cut-off of 25% necrosis was able to differentiate IDH-mutant from IDH-wildtype (p-value < 0.001), and a cut-off rCBV of 2.0 could differentiate IDH-mutant from IDH-wild phenotypes (p-value < 0.007). Conclusion: Semantic imaging features could reliably predict the IDH mutation status in high-grade gliomas. Presurgical prediction of IDH mutation status could help the treating oncologist to tailor the adjuvant therapy or use novel IDH inhibitors. Full article
(This article belongs to the Special Issue The Application of Medical Imaging in Brain Tumors)
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15 pages, 5413 KiB  
Article
Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients
by Lina Chato and Shahram Latifi
J. Pers. Med. 2021, 11(12), 1336; https://doi.org/10.3390/jpm11121336 - 9 Dec 2021
Cited by 11 | Viewed by 3285
Abstract
Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is [...] Read more.
Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions. Full article
(This article belongs to the Special Issue The Application of Medical Imaging in Brain Tumors)
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Graphical abstract

12 pages, 1267 KiB  
Article
Filtration-Histogram Based Magnetic Resonance Texture Analysis (MRTA) for the Distinction of Primary Central Nervous System Lymphoma and Glioblastoma
by Claire L. MacIver, Ayisha Al Busaidi, Balaji Ganeshan, John A. Maynard, Stephen Wastling, Harpreet Hyare, Sebastian Brandner, Julia E. Markus, Martin A. Lewis, Ashley M. Groves, Kate Cwynarski and Stefanie C. Thust
J. Pers. Med. 2021, 11(9), 876; https://doi.org/10.3390/jpm11090876 - 31 Aug 2021
Cited by 4 | Viewed by 2409
Abstract
Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. [...] Read more.
Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre-treatment MRI sequences (T1-weighted contrast-enhanced (T1CE), T2-weighted (T2), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2–6 mm) and unfiltered (SSF = 0) histogram parameters were compared using Mann-Whitney U non-parametric testing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permitted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T1CE-derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross-sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction. Full article
(This article belongs to the Special Issue The Application of Medical Imaging in Brain Tumors)
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12 pages, 1060 KiB  
Article
PSMA Expression in 122 Treatment Naive Glioma Patients Related to Tumor Metabolism in 11C-Methionine PET and Survival
by Tatjana Traub-Weidinger, Nina Poetsch, Adelheid Woehrer, Eva-Maria Klebermass, Tatjana Bachnik, Matthias Preusser, Mario Mischkulnig, Barbara Kiesel, Georg Widhalm, Markus Mitterhauser, Marcus Hacker and Oskar Koperek
J. Pers. Med. 2021, 11(7), 624; https://doi.org/10.3390/jpm11070624 - 30 Jun 2021
Cited by 12 | Viewed by 2566
Abstract
Apart from its expression in benign and malignant prostate tissue, prostate specific membrane antigen (PSMA) was shown to be expressed specifically in the neovasculature of solid tumors. For gliomas only little information exists. Therefore, we aimed to correlate PSMA expression in gliomas to [...] Read more.
Apart from its expression in benign and malignant prostate tissue, prostate specific membrane antigen (PSMA) was shown to be expressed specifically in the neovasculature of solid tumors. For gliomas only little information exists. Therefore, we aimed to correlate PSMA expression in gliomas to tumor metabolism by L-[S-methyl-11C]methionine (MET) PET and survival. Therefore, immunohistochemical staining (IHC) for isocitrate dehydrogenase 1-R132H (IDH1-R132H) mutation and PSMA expression was performed on the paraffin embedded tissue samples of 122 treatment-naive glioma patients. The IHC results were then related to the pre-therapeutic semiquantitative MET PET data and patients’ survival. Vascular PSMA expression was observed in 26 of 122 samples and was rather specific for high-grade gliomas ([HGG] 81% of glioblastoma multiforme, 10% of WHO grade III and just 2% of grade II gliomas). Significantly higher amounts of gliomas without verifiable IDH1-R132H mutation showed vascular PSMA expression. Significantly shorter median survival times were seen for patients with vascular PSMA staining in all tumors as well as HGG only. Additionally, significantly higher numbers of PSMA staining vessels were found in tumors with high amino acid metabolic rates. Vascular PSMA expression in gliomas was seen as a high-grade specific feature associated with elevated amino acid metabolism and short survival. Full article
(This article belongs to the Special Issue The Application of Medical Imaging in Brain Tumors)
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