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Advanced Imaging in Brain Tumor Patient Management

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 10682

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Special Issue Editors

Prof. Dr. Hyunsuk Shim

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Guest Editor

Department of Radiation Oncology, Winship Cancer Institute of Emory University, 1701 Uppergate Drive, C5018, Atlanta, GA 30322, USA
Interests: brain tumor; MRI; MR spectroscopy; imaging biomarker; neuro-oncology; neuroimaging; radiation oncology; clinical trial

Dr. Eric A. Mellon

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Guest Editor

Sylvester Comprehensive Cancer Center, Miller School of Medicine, Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA
Interests: radiation oncology; brain tumor; neuroimaging; clinical trial

Dr. Brent Weinberg

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Guest Editor

Department of Radiology and Imaging Sciences, Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
Interests: neuroimaging; brain tumor; MRI; neuro-oncology; tumor response criteria
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Imaging gliomas is a particular challenge because these tumors are highly infiltrative, and current standard imaging methods are limited in assessing tumor distribution and extent. Accurate tumor localization is particularly important because this information is used to guide biopsy, surgical resection, and radiation therapy. These complexities are compounded by the wide range of glioma appearances. Lower-grade gliomas (LGG), including grade II and III tumors, are heterogeneous and may contain both more and less aggressive regions within the same tumor. They may have little or no contrast enhancement to define targets for biopsy and radiation treatment guidance, and better tumor identification is needed to guide treatment. Grade IV IDH-wild type gliomas (glioblastomas, GBMs) are the most common primary malignant brain tumor in adults and are plagued by poor survival rates despite aggressive resection and radiation therapy targeted at the gadolinium contrast-enhancing tumor. For these patients, undertreating infiltrating disease that is not well identified on imaging likely contributes to poor outcomes. This Special Issue will address the urgent unmet need for better imaging technology to map gliomas in the brain and serve as the basis for improved guidance of therapies including biopsy, resection, radiation therapy, and longitudinal follow-up.

Prof. Dr. Hyunsuk Shim
Dr. Eric A. Mellon
Dr. Brent Weinberg
Guest Editors

Manuscript Submission Information

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Keywords

neuroimaging
brain tumor
radiation therapy
neurosurgery
gliomas

Published Papers (7 papers)

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Research

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14 pages, 2842 KiB

Open AccessArticle

Enhancing Whole-Brain Magnetic Field Homogeneity for 3D-Magnetic Resonance Spectroscopic Imaging with a Novel Unified Coil: A Preliminary Study

by Archana Vadiraj Malagi, Xinqi Li, Na Zhang, Yucen Liu, Yuheng Huang, Fardad Michael Serry, Ziyang Long, Chia-Chi Yang, Yujie Shan, Yubin Cai, Jeremy Zepeda, Nader Binesh, Debiao Li, Hsin-Jung Yang and Hui Han

Cancers 2024, 16(6), 1233; https://doi.org/10.3390/cancers16061233 - 21 Mar 2024

Viewed by 694

Abstract

The spectral quality of magnetic resonance spectroscopic imaging (MRSI) can be affected by strong magnetic field inhomogeneities, posing a challenge for 3D-MRSI’s widespread clinical use with standard scanner-equipped 2nd-order shim coils. To overcome this, we designed an empirical unified shim–RF head coil (32-ch RF receive and 51-ch shim) for 3D-MRSI improvement. We compared its shimming performance and 3D-MRSI brain coverages against the standard scanner shim (2nd-order spherical harmonic (SH) shim coils) and integrated parallel reception, excitation, and shimming (iPRES) 32-ch AC/DC head coil. We also simulated a theoretical 3rd-, 4th-, and 5th-order SH shim as a benchmark to assess the UNIfied shim–RF coil (UNIC) improvements. In this preliminary study, the whole-brain coverage was simulated by using B₀ field maps of twenty-four healthy human subjects (n = 24). Our results demonstrated that UNIC substantially improves brain field homogeneity, reducing whole-brain frequency standard deviations by 27% compared to the standard 2nd-order scanner shim and 17% compared to the iPRES shim. Moreover, UNIC enhances whole-brain coverage of 3D-MRSI by up to 34% compared to the standard 2nd-order scanner shim and up to 13% compared to the iPRES shim. UNIC markedly increases coverage in the prefrontal cortex by 147% and 47% and in the medial temporal lobe and temporal pole by 29% and 13%, respectively, at voxel resolutions of 1.4 cc and 0.09 cc for 3D-MRSI. Furthermore, UNIC effectively reduces variations in shim quality and brain coverage among different subjects compared to scanner shim and iPRES shim. Anticipated advancements in higher-order shimming (beyond 6th order) are expected via optimized designs using dimensionality reduction methods. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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11 pages, 1365 KiB

Open AccessArticle

Downfield Proton MRSI at 3 Tesla: A Pilot Study in Human Brain Tumors

by İpek Özdemir, David O. Kamson, Semra Etyemez, Lindsay Blair, Doris D. M. Lin and Peter B. Barker

Cancers 2023, 15(17), 4311; https://doi.org/10.3390/cancers15174311 - 29 Aug 2023

Cited by 1 | Viewed by 905

Abstract

Purpose: To investigate the use of 3D downfield proton magnetic resonance spectroscopic imaging (DF-MRSI) for evaluation of tumor recurrence in patients with glioblastoma (GBM). Methods: Seven patients (4F, age range 44–65 and mean ± standard deviation 59.3 ± 7.5 years) with previously treated GBM were scanned using a recently developed 3D DF-MRSI sequence at 3T. Short TE 3D DF-MRSI and water reference 3D-MRSI scans were collected with a nominal spatial resolution of 0.7 cm³. DF volume data in eight slices covered 12 cm of brain in the cranio-caudal axis. Data were analyzed using the ‘LCModel’ program and a basis set containing nine peaks ranging in frequency between 6.83 to 8.49 ppm. The DF8.18 (assigned to amides) and DF7.90 peaks were selected for the creation of metabolic images and statistical analysis. Longitudinal MR images and clinical history were used to classify brain lesions as either recurrent tumor or treatment effect, which may include necrosis. DF-MRSI data were compared between lesion groups (recurrent tumor, treatment effect) and normal-appearing brain. Results: Of the seven brain tumor patients, two were classified as having recurrent tumor and the rest were classified as treatment effect. Amide metabolite levels from recurrent tumor regions were significantly (p < 0.05) higher compared to both normal-appearing brain and treatment effect regions. Amide levels in lesion voxels classified as treatment effect were significantly lower than normal brain. Conclusions: 3D DF-MRSI in human brain tumors at 3T is feasible and was well tolerated by all patients enrolled in this preliminary study. Amide levels measured by 3D DF-MRSI were significantly different between treatment effect and tumor regrowth. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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14 pages, 3220 KiB

Open AccessArticle

A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking

by Karthik K. Ramesh, Karen M. Xu, Anuradha G. Trivedi, Vicki Huang, Vahid Khalilzad Sharghi, Lawrence R. Kleinberg, Eric A. Mellon, Hui-Kuo G. Shu, Hyunsuk Shim and Brent D. Weinberg

Cancers 2023, 15(15), 3956; https://doi.org/10.3390/cancers15153956 - 3 Aug 2023

Cited by 2 | Viewed by 1676

Abstract

Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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15 pages, 1530 KiB

Open AccessArticle

The Impact of ioMRI on Glioblastoma Resection and Clinical Outcomes in a State-of-the-Art Neuro-Oncological Setup

by Wei Zhang, Sebastian Ille, Maximilian Schwendner, Benedikt Wiestler, Bernhard Meyer and Sandro M. Krieg

Cancers 2023, 15(14), 3563; https://doi.org/10.3390/cancers15143563 - 10 Jul 2023

Viewed by 837

Abstract

Intraoperative magnetic resonance imaging (ioMRI) aims to improve gross total resection (GTR) in glioblastoma (GBM) patients. Despite some older randomized data on safety and feasibility, ioMRI’s actual impact in a modern neurosurgical setting utilizing a larger armamentarium of techniques has not been sufficiently investigated to date. We therefore aimed to analyze its effects on residual tumor, patient outcome, and progression-free survival (PFS) in GBM patients in a modern high-volume center. Patients undergoing ioMRI for resection of supratentorial GBM were enrolled between March 2018 and June 2020. ioMRI was performed in all cases at the end of resection when surgeons expected complete macroscopic tumor removal. Extent of resection (EOR) was performed by volumetric analysis, with GTR defined as an EOR ≥ 95%, respectively. Progression-free survival (PFS) was analyzed through univariate and multivariate Cox proportional regression analyses. In total, we enrolled 172 patients. Mean EOR increased from 93.9% to 98.3% (p < 0.0001) due to ioMRI, equaling an increase in GTR rates from 78.5% to 93.0% (p = 0.0002). Residual tumor volume decreased from 1.3 ± 4.2 cm³ to 0.6 ± 2.5 cm³ (p = 0.0037). Logistic regression revealed recurrent GBM as a risk factor leading to subtotal resection (STR) (odds ratio (OR) = 3.047, 95% confidence interval (CI) 1.165–7.974, p = 0.023). Additional resection after ioMRI led to equally long PFS compared to patients with complete tumor removal before ioMRI (hazard ratio (HR) = 0.898, 95%-CI 0.543–1.483, p = 0.67). ioMRI considerably reduces residual tumor volume and helps to achieve comparable PFS, even in patients with unexpected residual tumor after initial resection before ioMRI. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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11 pages, 2671 KiB

Open AccessArticle

Spectroscopic MRI-Based Biomarkers Predict Survival for Newly Diagnosed Glioblastoma in a Clinical Trial

by Anuradha G. Trivedi, Karthik K. Ramesh, Vicki Huang, Eric A. Mellon, Peter B. Barker, Lawrence R. Kleinberg, Brent D. Weinberg, Hui-Kuo G. Shu and Hyunsuk Shim

Cancers 2023, 15(13), 3524; https://doi.org/10.3390/cancers15133524 - 7 Jul 2023

Viewed by 1271

Abstract

Despite aggressive treatment, glioblastoma has a poor prognosis due to its infiltrative nature. Spectroscopic MRI-measured brain metabolites, particularly the choline to N-acetylaspartate ratio (Cho/NAA), better characterizes the extent of tumor infiltration. In a previous pilot trial (NCT03137888), brain regions with Cho/NAA ≥ 2x normal were treated with high-dose radiation for newly diagnosed glioblastoma patients. This report is a secondary analysis of that trial where spectroscopic MRI-based biomarkers are evaluated for how they correlate with progression-free and overall survival (PFS/OS). Subgroups were created within the cohort based on pre-radiation treatment (pre-RT) median cutoff volumes of residual enhancement (2.1 cc) and metabolically abnormal volumes used for treatment (19.2 cc). We generated Kaplan–Meier PFS/OS curves and compared these curves via the log-rank test between subgroups. For the subgroups stratified by metabolic abnormality, statistically significant differences were observed for PFS (p = 0.019) and OS (p = 0.020). Stratification by residual enhancement did not lead to observable differences in the OS (p = 0.373) or PFS (p = 0.286) curves. This retrospective analysis shows that patients with lower post-surgical Cho/NAA volumes had significantly superior survival outcomes, while residual enhancement, which guides high-dose radiation in standard treatment, had little significance in PFS/OS. This suggests that the infiltrating, non-enhancing component of glioblastoma is an important factor in patient outcomes and should be treated accordingly. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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9 pages, 1256 KiB

Open AccessArticle

Simulated Adaptive Radiotherapy for Shrinking Glioblastoma Resection Cavities on a Hybrid MRI–Linear Accelerator

by Beatriz Guevara, Kaylie Cullison, Danilo Maziero, Gregory A. Azzam, Macarena I. De La Fuente, Karen Brown, Alessandro Valderrama, Jessica Meshman, Adrian Breto, John Chetley Ford and Eric A. Mellon

Cancers 2023, 15(5), 1555; https://doi.org/10.3390/cancers15051555 - 2 Mar 2023

Cited by 6 | Viewed by 2376

Abstract

During radiation therapy (RT) of glioblastoma, daily MRI with combination MRI–linear accelerator (MRI–Linac) systems has demonstrated significant anatomic changes, including evolving post-surgical cavity shrinkage. Cognitive function RT for brain tumors is correlated with radiation doses to healthy brain structures, especially the hippocampi. Therefore, this study investigates whether adaptive planning to the shrinking target could reduce normal brain RT dose with the goal of improving post-RT function. We evaluated 10 glioblastoma patients previously treated on a 0.35T MRI–Linac with a prescription of 60 Gy delivered in 30 fractions over six weeks without adaptation (“static plan”) with concurrent temozolomide chemotherapy. Six weekly plans were created per patient. Reductions in the radiation dose to uninvolved hippocampi (maximum and mean) and brain (mean) were observed for weekly adaptive plans. The dose (Gy) to the hippocampi for static vs. weekly adaptive plans were, respectively: max 21 ± 13.7 vs. 15.2 ± 8.2 (p = 0.003) and mean 12.5 ± 6.7 vs. 8.4 ± 4.0 (p = 0.036). The mean brain dose was 20.6 ± 6.0 for static planning vs. 18.7 ± 6.8 for weekly adaptive planning (p = 0.005). Weekly adaptive re-planning has the potential to spare the brain and hippocampi from high-dose radiation, possibly reducing the neurocognitive side effects of RT for eligible patients. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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Review

Jump to: Research

17 pages, 6743 KiB

Open AccessReview

Intraoperative Imaging and Optical Visualization Techniques for Brain Tumor Resection: A Narrative Review

by Othman Bin-Alamer, Hussam Abou-Al-Shaar, Zachary C. Gersey, Sakibul Huq, Justiss A. Kallos, David J. McCarthy, Jeffery R. Head, Edward Andrews, Xiaoran Zhang and Constantinos G. Hadjipanayis

Cancers 2023, 15(19), 4890; https://doi.org/10.3390/cancers15194890 - 9 Oct 2023

Cited by 3 | Viewed by 1833

Abstract

Advancements in intraoperative visualization and imaging techniques are increasingly central to the success and safety of brain tumor surgery, leading to transformative improvements in patient outcomes. This comprehensive review intricately describes the evolution of conventional and emerging technologies for intraoperative imaging, encompassing the surgical microscope, exoscope, Raman spectroscopy, confocal microscopy, fluorescence-guided surgery, intraoperative ultrasound, magnetic resonance imaging, and computed tomography. We detail how each of these imaging modalities contributes uniquely to the precision, safety, and efficacy of neurosurgical procedures. Despite their substantial benefits, these technologies share common challenges, including difficulties in image interpretation and steep learning curves. Looking forward, innovations in this field are poised to incorporate artificial intelligence, integrated multimodal imaging approaches, and augmented and virtual reality technologies. This rapidly evolving landscape represents fertile ground for future research and technological development, aiming to further elevate surgical precision, safety, and, most critically, patient outcomes in the management of brain tumors. Full article

(This article belongs to the Special Issue Advanced Imaging in Brain Tumor Patient Management)

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