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Cancers
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The Current Status of Brain Tumors Imaging
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The Current Status of Brain Tumors Imaging

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 6174

Special Issue Editor

Dr. Houman Sotoudeh

E-Mail Website
Guest Editor
Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
Interests: neuroradiology; artificial intelligence; radiomics; neuro-oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Brain tumors (primary and metastatic) are among the most common cancers with high morbidity and mortality rates worldwide. Imaging plays a crucial role in the diagnosis and management of these tumors. The main focus of this Special Issue is the role of CT and MRI in diagnosing and following primary brain neoplasms and metastasis. Topics of interest include (but are not limited to) pre-treatment (predicting the histopathology, genetic profile, and treatment response) and post-treatment (differentiation between recurrence versus radiation necrosis) applications of CT and MRI. This Special Issue will highlight the current state-of-the-art advanced imaging, including MR spectroscopy, MR perfusion, fMRI, DTI, radiomics, and artificial intelligence in brain tumors.

We welcome all studies with findings that will improve the role of neuro-imaging in neuro-oncology, offering readers future perspectives on diagnostic approaches.

Dr. Houman Sotoudeh
Guest Editor

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. Cancers 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 2900 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

  • neuro-oncology

  • glioma
  • brain metastasis
  • CT Scan
  • MRI
  • radiogenomics
  • radiomics
  • artificial intelligence
  • MR spectroscopy
  • MR perfusion
  • fMRI

Published Papers (4 papers)

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Research

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12 pages, 2356 KiB  
Article
Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma
by Keon Mahmoudi, Daniel H. Kim, Elham Tavakkol, Shingo Kihira, Adam Bauer, Nadejda Tsankova, Fahad Khan, Adilia Hormigo, Vivek Yedavalli and Kambiz Nael
Cancers 2024, 16(3), 589; https://doi.org/10.3390/cancers16030589 - 30 Jan 2024
Viewed by 893
Abstract
Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In [...] Read more.
Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. Results: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. Conclusions: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM. Full article
(This article belongs to the Special Issue The Current Status of Brain Tumors Imaging)
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21 pages, 4187 KiB  
Article
RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation
by Abdulkerim Duman, Oktay Karakuş, Xianfang Sun, Solly Thomas, James Powell and Emiliano Spezi
Cancers 2023, 15(23), 5620; https://doi.org/10.3390/cancers15235620 - 28 Nov 2023
Cited by 1 | Viewed by 1029
Abstract
Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate [...] Read more.
Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and echo time) remains a subject of debate. This research aims to address this gap by introducing a novel ‘Region-Focused Selection Plus (RFS+)’ strategy designed to efficiently improve the generalization and quantification capabilities of deep learning (DL) models for automatic brain tumor segmentation. RFS+ advocates a targeted approach, focusing on one region at a time. It presents a holistic strategy that maximizes the benefits of various segmentation methods by customizing input masks, activation functions, loss functions, and normalization techniques. Upon identifying the top three models for each specific region in the training dataset, RFS+ employs a weighted ensemble learning technique to mitigate the limitations inherent in each segmentation approach. In this study, we explore three distinct approaches, namely, multi-class, multi-label, and binary class for brain tumor segmentation, coupled with various normalization techniques applied to individual sub-regions. The combination of different approaches with diverse normalization techniques is also investigated. A comparative analysis is conducted among three U-net model variants, including the state-of-the-art models that emerged victorious in the BraTS 2020 and 2021 challenges. These models are evaluated using the dice similarity coefficient (DSC) score on the 2021 BraTS validation dataset. The 2D U-net model yielded DSC scores of 77.45%, 82.14%, and 90.82% for enhancing tumor (ET), tumor core (TC), and the whole tumor (WT), respectively. Furthermore, on our local dataset, the 2D U-net model augmented with the RFS+ strategy demonstrates superior performance compared to the state-of-the-art model, achieving the highest DSC score of 79.22% for gross tumor volume (GTV). The model utilizing RFS+ requires 10% less training dataset, 67% less memory and completes training in 92% less time compared to the state-of-the-art model. These results confirm the effectiveness of the RFS+ strategy for enhancing the generalizability of DL models in brain tumor segmentation. Full article
(This article belongs to the Special Issue The Current Status of Brain Tumors Imaging)
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15 pages, 27307 KiB  
Article
Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas
by Fabio Raman, Alexander Mullen, Matthew Byrd, Sejong Bae, Jinsuh Kim, Houman Sotoudeh, Fanny E. Morón and Hassan M. Fathallah-Shaykh
Cancers 2023, 15(13), 3274; https://doi.org/10.3390/cancers15133274 - 21 Jun 2023
Cited by 2 | Viewed by 1782
Abstract
Purpose: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator’s discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition [...] Read more.
Purpose: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator’s discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG. Materials and Methods: A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment. Results: RANO product measurements had poor-to-moderate inter-operator reproducibility (r2 = 0.28–0.82; coefficient of variance (CV) = 44–110%; mean percent difference (diff) = 0.4–46.8%) and moderate-to-excellent intra-operator reproducibility (r2 = 0.71–0.88; CV = 31–58%; diff = 0.3–23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02). Conclusion: RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths. Full article
(This article belongs to the Special Issue The Current Status of Brain Tumors Imaging)
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Review

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19 pages, 356 KiB  
Review
Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme
by Mohammadreza Alizadeh, Nima Broomand Lomer, Mobin Azami, Mohammad Khalafi, Parnian Shobeiri, Melika Arab Bafrani and Houman Sotoudeh
Cancers 2023, 15(18), 4429; https://doi.org/10.3390/cancers15184429 - 05 Sep 2023
Cited by 1 | Viewed by 1666
Abstract
Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool [...] Read more.
Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics’s capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis. Full article
(This article belongs to the Special Issue The Current Status of Brain Tumors Imaging)
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