Imaging and target volume delineation in glioma

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Methyl-11C-L-methionine positron emission tomography for radiotherapy planning for recurrent malignant glioma

OBJECTIVE

To investigate differences in uptake regions between methyl-11C-L-methionine positron emission tomography (11C-MET PET) and gadolinium (Gd)-enhanced magnetic resonance imaging (MRI), and their impact on dose distribution, including changing of the threshold for tumor boundaries.

METHODS

Twenty consecutive patients with grade 3 or 4 glioma who had recurrence after postoperative radiotherapy (RT) between April 2016 and October 2017 were examined. The study was performed using simulation with the assumption that all patients received RT. The clinical target volume (CTV) was contoured using the Gd-enhanced region (CTV(Gd)), the tumor/normal tissue (T/N) ratios of 11C-MET PET of 1.3 and 2.0 (CTV (T/N 1.3), CTV (T/N 2.0)), and the PET-edge method (CTV(P-E)) for stereotactic RT planning. Differences among CTVs were evaluated. The brain dose at each CTV and the dose at each CTV defined by 11C-MET PET using MRI as the reference were evaluated.

RESULTS

The Jaccard index (JI) for concordance of CTV (Gd) with CTVs using 11C-MET PET was highest for CTV (T/N 2.0), with a value of 0.7. In a comparison of pixel values of MRI and PET, the correlation coefficient for cases with higher JI was significantly greater than that for lower JI cases (0.37 vs. 0.20, P = 0.007). D50% of the brain in RT planning using each CTV differed significantly (P = 0.03) and that using CTV (T/N 1.3) were higher than with use of CTV (Gd). V90% and V95% for each CTV differed in a simulation study for actual treatment using CTV (Gd) (P = 1.0 × 10-7 and 3.0 × 10-9, respectively) and those using CTV (T/N 1.3) and CTV (P-E) were lower than with CTV (Gd).

CONCLUSIONS

The region of 11C-MET accumulation is not necessarily consistent with and larger than the Gd-enhanced region. A change of the tumor boundary using 11C-MET PET can cause significant changes in doses to the brain and the CTV.

Commissioning and implementing a Quality Assurance program for dedicated radiation oncology MRI scanners

PURPOSE

ACR and AAPM task group's guidelines addressing commissioning for dedicated MR simulators were recently published. The goal of the current paper is to present the authors' 2-year experience regarding the commissioning and introduction of a QA program based on these guidelines and an associated automated workflow.

METHODS

All mandatory commissioning tests suggested by AAPM report 284 were performed and results are reported for two MRI scanners (MAGNETOM Sola and Aera). Visual inspection, vendor clinical or service platform, third-party software, or in-house python-based code were used. Automated QA and data analysis was performed via vendor, in-house or third-party software. QATrack+ was used for QA data logging and storage. 3D geometric distortion, B0 inhomogeneity, EPI, and parallel imaging performance were evaluated.

RESULTS

Contrasting with AAPM report 284 recommendations, homogeneity and RF tests were performed monthly. The QA program allowed us to detect major failures over time (shimming, gradient calibration and RF interference). Automated QA, data analysis, and logging allowed fast ACR analysis daily and monthly QA to be performed in 3 h. On the Sola, the average distortion is 1 mm for imaging radii of 250 mm or less. For radii of up to 200 mm, the maximum, average (standard deviation) distortion is 1.2  and 0.4 mm (0.3 mm). Aera values are roughly double the Sola for radii up to 200 mm. EPI geometric distortion, ghosting ratio, and long-term stability were found to be under the maximum recommended values. Parallel imaging SNR ratio was stable and close to the theoretical value (ideal g-factor). No major failures were detected during commissioning.

CONCLUSION

An automated workflow and enhanced QA program allowed to automatically track machine and environmental changes over time and to detect periodic failures and errors that might otherwise have gone unnoticed. The Sola is more geometrically accurate, with a more homogenous B0 field than the Aera.

Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO)

BACKGROUND

It is not unusual to see some parts of tissues are excluded in the field of view of CT simulation images. A typical mitigation is to avoid beams entering the missing body parts at the cost of sub-optimal planning.

METHODS

This study is to solve the problem by developing 3 methods, (1) deep learning (DL) mechanism for missing tissue generation, (2) using patient body outline (PBO) based on surface imaging, and (3) hybrid method combining DL and PBO. The DL model was built upon a Globally and Locally Consistent Image Completion to learn features by Convolutional Neural Networks-based inpainting, based on Generative Adversarial Network. The database used comprised 10,005 CT training slices of 322 lung cancer patients and 166 CT evaluation test slices of 15 patients. CT images were from the publicly available database of the Cancer Imaging Archive. Since existing data were used PBOs were acquired from the CT images. For evaluation, Structural Similarity Index Metric (SSIM), Root Mean Square Error (RMSE) and Peak signal-to-noise ratio (PSNR) were evaluated. For dosimetric validation, dynamic conformal arc plans were made with the ground truth images and images generated by the proposed method. Gamma analysis was conducted at relatively strict criteria of 1%/1 mm (dose difference/distance to agreement) and 2%/2 mm under three dose thresholds of 1%, 10% and 50% of the maximum dose in the plans made on the ground truth image sets.

RESULTS

The average SSIM in generation part only was 0.06 at epoch 100 but reached 0.86 at epoch 1500. Accordingly, the average SSIM in the whole image also improved from 0.86 to 0.97. At epoch 1500, the average values of RMSE and PSNR in the whole image were 7.4 and 30.9, respectively. Gamma analysis showed excellent agreement with the hybrid method (equal to or higher than 96.6% of the mean of pass rates for all scenarios).

CONCLUSIONS

It was first demonstrated that missing tissues in simulation imaging could be generated with high similarity, and dosimetric limitation could be overcome. The benefit of this study can be significantly enlarged when MR-only simulation is considered.

Association of MRI Volume Parameters in Predicting Patient Outcome at Time of Initial Diagnosis of Glioblastoma

PURPOSE

Patients with glioblastoma (GBM) may demonstrate varying patterns of infiltration and relapse. Improving the ability to predict these patterns may influence the management strategies at the time of initial diagnosis. This study aims to examine the impact of the ratio (T2/T1) of the non-enhancing volume in T2-weighted images (T2) to the enhancing volume in MRI T1-weighted gadolinium-enhanced images (T1gad) on patient outcome.

METHODS AND MATERIALS

A retrospective audit was performed from established prospective databases in patients managed consecutively with radiation therapy (RT) for GBM between 2016 and 2019. Patient, tumour and treatment-related factors were assessed in relation to outcome. Volumetric data from the initial diagnostic MRI were obtained via the manual segmentation of the T1gd and T2 abnormalities. A T2/T1 ratio was calculated from these volumes. The initial relapse site was assessed on MRI in relation to the site of the original T1gad volume and surgical cavity. The major endpoints were median relapse-free survival (RFS) from the date of diagnosis and site of initial relapse (defined as either local at the initial surgical site or any distance more than 20 mm from initial T1gad abnormality). The analysis was performed for association between known prognostic factors as well as the radiological factors using log-rank tests for subgroup comparisons, with correction for multiple comparisons.

RESULTS

One hundred and seventy-seven patients with GBM were managed consecutively with RT between 2016 and 2019 and were eligible for the analysis. The median age was 62 years. Seventy-four percent were managed under a 60Gy (Stupp) protocol, whilst 26% were on a 40Gy (Elderly) protocol. Major neuroanatomical subsites were Lateral Temporal (18%), Anterior Temporal (13%) and Medial Frontal (10%). Median volumes on T1gd and T2 were 20 cm3 (q1-3:8-43) and 37 cm3 (q1-3: 17-70), respectively. The median T2/T1 ratio was 2.1. For the whole cohort, the median OS was 16.0 months (95%CI:14.1-18.0). One hundred and forty-eight patients have relapsed with a median RFS of 11.4 months (95%CI:10.4-12.5). A component of distant relapse was evident in 43.9% of relapses, with 23.6% isolated relapse. Better ECOG performance Status (p = 0.007), greater extent of resection (p = 0.020), MGMT methylation (p < 0.001) and RT60Gy Dose (p = 0.050) were associated with improved RFS. Although the continuous variable of initial T1gd volume (p = 0.39) and T2 volume (p = 0.23) were not associated with RFS, the lowest T2/T1 quartile (reflecting a relatively lower T2 volume compared to T1gd volume) was significantly associated with improved RFS (p = 0.016) compared with the highest quartile. The lowest T2/T1 ratio quartile was also associated with a lower risk of distant relapse (p = 0.031).

CONCLUSION

In patients diagnosed with GBM, the volumetric parameters of the diagnostic MRI with a ratio of T2 and T1gad abnormality may assist in the prediction of relapse-free survival and patterns of relapse. A further understanding of these relationships has the potential to impact the design of future radiation therapy target volume delineation protocols.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

An investigation of the conformity, feasibility, and expected clinical benefits of multiparametric MRI-guided dose painting radiotherapy in glioblastoma

Background

New technologies developed to improve survival outcomes for glioblastoma (GBM) continue to have limited success. Recently, image-guided dose painting (DP) radiotherapy has emerged as a promising strategy to increase local control rates. In this study, we evaluate the practical application of a multiparametric MRI model of glioma infiltration for DP radiotherapy in GBM by measuring its conformity, feasibility, and expected clinical benefits against standard of care treatment.

Methods

Maps of tumor probability were generated from perfusion/diffusion MRI data from 17 GBM patients via a previously developed model of GBM infiltration. Prescriptions for DP were linearly derived from tumor probability maps and used to develop dose optimized treatment plans. Conformity of DP plans to dose prescriptions was measured via a quality factor. Feasibility of DP plans was evaluated by dose metrics to target volumes and critical brain structures. Expected clinical benefit of DP plans was assessed by tumor control probability. The DP plans were compared to standard radiotherapy plans.

Results

The conformity of the DP plans was >90%. Compared to the standard plans, DP (1) did not affect dose delivered to organs at risk; (2) increased mean and maximum dose and improved minimum dose coverage for the target volumes; (3) reduced minimum dose within the radiotherapy treatment margins; (4) improved local tumor control probability within the target volumes for all patients.

Conclusions

A multiparametric MRI model of GBM infiltration can enable conformal, feasible, and potentially beneficial dose painting radiotherapy plans.

Radiation guidelines for gliomas

Gliomas are the most frequent primary brain tumour. The proximity of organs at risk, the infiltrating nature, and the radioresistance of gliomas have to be taken into account in the choice of prescribed dose and technique of radiotherapy. The management of glioma patients is based on clinical factors (age, KPS) and tumour characteristics (histology, molecular biology, tumour location), and strongly depends on available and associated treatments, such as surgery, radiation therapy, and chemotherapy. The knowledge of molecular biomarkers is currently essential, they are increasingly evolving as additional factors that facilitate diagnostics and therapeutic decision-making. We present the update of the recommendations of the French society for radiation oncology on the indications and the technical procedures for performing radiation therapy in patients with gliomas.

Clinicopathologic analysis of microscopic tumor extension in glioma for external beam radiotherapy planning

BACKGROUND

There is no consensus regarding the clinical target volume (CTV) margins in radiotherapy for glioma. In this study, we aimed to perform a complete macropathologic analysis examining microscopic tumor extension (ME) to more accurately define the CTV in glioma.

METHODS

Thirty-eight supra-total resection specimens of glioma patients were examined on histologic sections. The ME distance, defined as the maximum linear distance from the tumor border to the invasive tumor cells, was measured at each section. We defined the CTV based on the relationships between ME distance and clinicopathologic features.

RESULTS

Between February 2016 and July 2020, a total of 814 slides were examined, corresponding to 162 slides for low-grade glioma (LGG) and 652 slides for high-grade glioma (HGG). The ME value was 0.69 ± 0.43 cm for LGG and 1.29 ± 0.54 cm for HGG (P < 0.001). After multivariate analysis, tumor grade, O⁶-methylguanine-DNA-methyltransferase promoter methylated status (MGMT_m), isocitrate dehydrogenase wild-type status (IDH_wt), and 1p/19q non-co-deleted status (non-codel) were positively correlated with ME distance (all P < 0.05). We defined the CTV of glioma based on tumor grade. To take into account approximately 95% of the ME, a margin of 1.00 cm, 1.50 cm, and 2.00 cm were chosen for grade II, grade III, and grade IV glioma, respectively. Paired analysis of molecularly defined patients confirmed that tumors that had all three molecular alterations (i.e., MGMT_m/IDH_wt/non-codel) were the most aggressive subgroups (all P < 0.05). For these patients, the margin could be up to 1.50 cm, 2.00 cm, and 2.50 cm for grade II, grade III, and grade IV glioma, respectively, to cover the subclinical lesions in 95% of cases.

CONCLUSIONS

The ME was different between the grades of gliomas. It may be reasonable to recommend 1.00 cm, 1.50 cm, and 2.00 cm CTV margins for grade II, grade III, and grade IV glioma, respectively. Considering the highly aggressive nature of MGMT_m/IDH_wt/non-codel tumors, for these patients, the margin could be further expanded by 0.5 cm. These recommendations would encompass microscopic disease extension in 95% of cases.

TRIAL REGISTRATION

The trial was registered with Chinese Clinical Trial Registry ( ChiCTR2100049376 ).

Task group 284 report: magnetic resonance imaging simulation in radiotherapy: considerations for clinical implementation, optimization, and quality assurance

The use of dedicated magnetic resonance simulation (MR-SIM) platforms in Radiation Oncology has expanded rapidly, introducing new equipment and functionality with the overall goal of improving the accuracy of radiation treatment planning. However, this emerging technology presents a new set of challenges that need to be addressed for safe and effective MR-SIM implementation. The major objectives of this report are to provide recommendations for commercially available MR simulators, including initial equipment selection, siting, acceptance testing, quality assurance, optimization of dedicated radiation therapy specific MR-SIM workflows, patient-specific considerations, safety, and staffing. Major contributions include guidance on motion and distortion management as well as MRI coil configurations to accommodate patients immobilized in the treatment position. Examples of optimized protocols and checklists for QA programs are provided. While the recommendations provided here are minimum requirements, emerging areas and unmet needs are also highlighted for future development.

FLAIRectomy in Supramarginal Resection of Glioblastoma Correlates With Clinical Outcome and Survival Analysis: A Prospective, Single Institution, Case Series

BACKGROUND

Extent of tumor resection (EOTR) in glioblastoma surgery plays an important role in improving survival.

OBJECTIVE

To analyze the efficacy, safety and reliability of fluid-attenuated inversion-recovery (FLAIR) magnetic resonance (MR) images used to guide glioblastoma resection (FLAIRectomy) and to volumetrically measure postoperative EOTR, which was correlated with clinical outcome and survival.

METHODS

A total of 68 glioblastoma patients (29 males, mean age 65.8) were prospectively enrolled. Hyperintense areas on FLAIR images, surrounding gadolinium-enhancing tissue on T1-weighted MR images, were screened for signal changes suggesting tumor infiltration and evaluated for supramaximal resection. The surgical protocol included 5-aminolevulinic acid (5-ALA) fluorescence, neuromonitoring, and intraoperative imaging tools. 5-ALA fluorescence intensity was analyzed and matched with the different sites on navigated MR, both on postcontrast T1-weighted and FLAIR images. Volumetric evaluation of EOTR on T1-weighted and FLAIR sequences was compared.

RESULTS

FLAIR MR volumetric evaluation documented larger tumor volume than that assessed on contrast-enhancing T1 MR (72.6 vs 54.9 cc); residual tumor was seen in 43 patients; postcontrast T1 MR volumetric analysis showed complete resection in 64 cases. O6-methylguanine-DNA methyltransferase promoter was methylated in 8/68 (11.7%) cases; wild type Isocytrate Dehydrogenase-1 (IDH-1) was found in 66/68 patients. Progression free survival and overall survival (PFS and OS) were 17.43 and 25.11 mo, respectively. Multiple regression analysis showed a significant correlation between EOTR based on FLAIR, PFS (R2 = 0.46), and OS (R2 = 0.68).

CONCLUSION

EOTR based on FLAIR and 5-ALA fluorescence is feasible. Safety of resection relies on the use of neuromonitoring and intraoperative multimodal imaging tools. FLAIR-based EOTR appears to be a stronger survival predictor compared to gadolinium-enhancing, T1-based resection.

Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

Introduction

In this study, we performed a consecutive macropathologic analysis to assess microscopic extension (ME) in high-grade glioma (HGG) to determine appropriate clinical target volume (CTV) margins for radiotherapy.

Materials and methods

The study included HGG patients with tumors located in non-functional areas, and supratotal resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model.

Results

Between June 2017 and July 2019, 652 pathologic slides obtained from 30 HGG patients were analyzed. The mean ME distance was 1.70 cm (range, 0.63 to 2.87 cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O⁶-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: Y_ME = 0.672 + 0.513X_Grade + 0.380X_SVZ + 0.439X_MGMT + 0.320X_IDH + 0.333X_1p/19q. The R-square value of goodness of fit was 0.780. The receiver operating characteristic curve proved that the area under the curve was 0.964 (P < 0.001).

Conclusion

ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients.

Trial registration The trial was registered with Chinese Clinical Trial Registry (ChiCTR2100046106). Registered 4 May 2021-Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01819-0.

Evaluation of gliomas peritumoral diffusion and prediction of IDH1 mutation by IVIM-DWI

Glioma characterized by high morbidity and mortality, is one of the most common brain tumors. The application of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in differentiating glioma grading and IDH1 mutation status were poorly investigated. 78 glioma patients confirmed by pathological and imaging methods were enrolled. Glioma patients were measured using IVIM-DWI, then related parameters such as cerebral blood flow (CBF), perfusion fraction (f), pseudo diffusivity (D*), and true diffusivity (D), were derived. Receiver operating characteristic (ROC) curves were made to calculate specificity and sensitivity. The values of CBF1, CBF3, D*1, rCBF1-2, rCBF3-2, and age in group high-grade gliomas (HGG) were significantly higher than that of in group low-grade gliomas (LGG). The values of CBF1, CBF3, rCBF1-2, rCBF3-2, D*1, and age in group IDH1^mut were significantly lower than that of in group IDH1^wt. The levels of D1 and f1 were remarkably higher in the group IDH1^mut than group IDH1^wt. rCBF1-2 had a remarkably positive correlation with CBF1 (r=0.852, p<0.001). f1 showed a markedly negative correlation with CBF1 (r= -0.306, p=0.007). IVIM-DWI presented efficacy in differentiating glioma grading and IDH1 mutation status.

Evaluation of interim MRI changes during limited-field radiation therapy for glioblastoma and implications for treatment planning

BACKGROUND AND PURPOSE

Consensus for defining gross tumor volume (GTV) and clinical target volume (CTV) for limited-field radiation therapy (LFRT) of GBM are not well established. We leveraged a department MRI simulator to image patients before and during LFRT to address these questions.

MATERIALS AND METHODS

Supratentorial GBM patients receiving LFRT (46 Gy + boost to 60 Gy) underwent baseline MRI (MRI1) and interim MRI during RT (MRI2). GTV1 was defined as T1 enhancement + surgical cavity on MRI1 without routine inclusion of T2 abnormality (unless tumor did not enhance). The initial CTV margin was 15 mm from GTV1, and the boost CTV margin was 5-7 mm. The GTV1 characteristics were categorized into three groups: identical T1 and T2 abnormality (Group A), T1 only with larger T2 abnormality not included (Group B), and T2 abnormality when tumor lacked enhancement (Group C). GTV2 was contoured on MRI2 and compared with GTV1 plus 5-15 mm expansions.

RESULTS

Among 120 patients treated from 2014-2019, 29 patients (24%) underwent replanning based on MRI2. On MRI2, 84% of GTV2 were covered by GTV1 + 5 mm, 93% by GTV1 + 7 mm, and 98% by GTV1 + 15 mm. On MRI1, 43% of GTV1 could be categorized into Group A, 39% Group B, and 18% Group C. Group B's patterns of failure, local control, or progression-free survival were similar to Group A/C.

CONCLUSIONS

Initial CTV margin of 15 mm followed by a boost CTV margin of 7 mm is a reasonable approach for LFRT of GBM. Omitting routine inclusion of T2 abnormality from GTV delineation may not jeopardize disease control.

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy

PURPOSE

The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region.

METHODS

Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs. To further verify the cGAN performance, we also used a U-net network as a comparison. Mean absolute error, structural similarity index, peak signal-to-noise ratio, dice similarity coefficient, and dose distribution were evaluated between the actual CTs and sCTs generated from different models.

RESULTS

The results show that the cGAN model with multi-channel (i.e., T1 + T2 + T1C + T1DixonC-water) as input to predict sCT achieves higher accuracy than any single MR sequence model. The T1-weighted MR model achieves better results than T2, T1C, and T1DixonC-water models. The comparison between cGAN and U-net shows that the sCTs predicted by cGAN retains additional image details are less blurred and more similar to the actual CT.

CONCLUSIONS

Conditional generative adversarial network with multiple MR sequences as model input shows the best accuracy. The T1-weighted MR images provide sufficient image information and are suitable for sCT prediction in clinical scenarios with limited acquisition sequences or limited acquisition time.

MRI basics for radiation oncologists

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Issues of MRI that are relevant for radiation oncologists are addressed.

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Radiation oncology requires dedicated scan protocols.

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Use of diagnostic protocols is not recommended for radiotherapy.

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MR images must be made in treatment position with the standard positioning devices.

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Safety screening prior to entering the MRI room is crucial.

MRI is increasingly used in radiation oncology to facilitate tumor and organ-at-risk delineation and image guidance. In this review, we address issues of MRI that are relevant for radiation oncologists when interpreting MR images offered for radiotherapy. Whether MRI is used in combination with CT or in an MRI-only workflow, it is generally necessary to ensure that MR images are acquired in treatment position, using the positioning and fixation devices that are commonly applied in radiotherapy. For target delineation, often a series of separate image sets are used with distinct image contrasts, acquired within a single exam. MR images can suffer from image distortions. While this can be avoided with dedicated scan protocols, in a diagnostic setting geometrical fidelity is less relevant and is therefore less accounted for. Since geometrical fidelity is of utmost importance in radiation oncology, it requires dedicated scan protocols. The strong magnetic field of an MRI scanner and the use of radiofrequency radiation can cause safety hazards if not properly addressed. Safety screening is crucial for every patient and every operator prior to entering the MRI room.

[Target volume concepts in radiotherapy and their implications for imaging]

CLINICAL ISSUE

Successful radiotherapy requires precise localization of the tumor and requires high-quality imaging for developing a treatment plan.

STANDARD TREATMENT

Irradiation of the tumor region, including a safety margin.

TREATMENT INNOVATIONS

The target volume consists of the gross tumor volume (GTV) containing visible parts of the tumor, the clinical target volume (CTV) covering the GTV plus invisible tumor extensions, and the planning target volume (PTV) to account for uncertainties. The non-GTV parts of the CTV are based on historical patient data. The PTV margins are based on a calculation of possible uncertainties during planning, setup, or treatment. Normal tissue deserves the identical care in contouring, since its tolerance may limit the tumor dose, taking into account the contours of organs at risk. Serial risk organs benefit from defining a planning organ of risk volume (PRV) to better limit the dose delivered to them.

DIAGNOSTIC WORK-UP

The better the imaging, the more reliable the definition of the GTV and treatment success will be. Multiple imaging sequences are desirable to support the delineation of the tumor. They may result in different CTVs that, depending on their tumor burden, may require different doses.

PERFORMANCE

The definition of standardized target volumes according to the ICRU reports 50, 62, and 83 forms the basis for an individualized radiation treatment planning according to unified criteria on a high-quality level.

ACHIEVEMENTS

Radio-oncology is by nature interdisciplinary, the diagnostic radiologist being an indispensable team partner. A regular dialogue between the disciplines is pivotal for target volume definition and treatment success.

PRACTICAL RECOMMENDATIONS

Imaging for target volume definition requires highest quality imaging, the use of functional imaging methods and close cooperation with a diagnostic radiologist experienced in this field.

A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients

Background

Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency.

Methods

Four MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice’s similarity coefficient (DSC) and Sensitivity and Specificity.

Results

Experimental study with the five patients’ data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets.

Conclusions

Efficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy.

What Neuroradiologists Need to Know About Radiation Treatment for Neural Tumors

Radiation oncologists and radiologists have a unique and mutually dependent relationship. Radiation oncologists rely on diagnostic imaging to locate the tumor and define the treatment target volume, evaluation of response to therapy, and follow-up. Accurate interpretation of post-treatment imaging requires diagnostic radiologists to have a basic understanding of radiation treatment planning and delivery. There are various radiation treatment modalities such as 3D conformal radiation therapy, intensity modulated radiation therapy and stereotactic radiosurgery as well as different radiation modalities such as photons and protons that can be used for treatment. All of these have subtle differences in how the treatment is planned and how the imaging findings might be affected. This paper provides an overview of the basic principles of radiation oncology, different radiation treatment modalities, how radiation therapy is planned and delivered, how knowledge of this process can help interpretation of images, and how the radiologist can contribute to this process.

Quantitative evaluation of diffusion tensor imaging for clinical management of glioma

Diffusion tensor imaging (DTI), assessing physiological motion of water in vivo, provides macroscopic view of microstructures of white matter in the central nervous system, and such imaging technique had been extensively used for the clinical treatment and research of glioma. This review mainly focuses on illuminating the merits of quantitative evaluation of DTI for glioma management. The content of the article includes DTI's application on tissue characterization, white matter tracts mapping, radiotherapy delineation, post-therapy outcome assessment, and multimodal imaging. At last, we elucidate a synoptic presentation of DTI limitation, which is critical for physicians making DTI-based clinical decisions in glioma management.

Findings of DTI-p maps in comparison with T2/T2-FLAIR to assess postoperative hyper-signal abnormal regions in patients with glioblastoma

PURPOSE

The aim of this study was to compare diffusion tensor imaging (DTI) isotropic map (p-map) with current radiographically (T₂/T₂-FLAIR) methods based on abnormal hyper-signal size and location of glioblastoma tumor using a semi-automatic approach.

MATERIALS AND METHODS

Twenty-five patients with biopsy-proved diagnosis of glioblastoma participated in this study. T₂, T₂-FLAIR images and diffusion tensor imaging (DTI) were acquired 1 week before radiotherapy. Hyper-signal regions on T₂, T₂-FLAIR and DTI p-map were segmented by means of semi-automated segmentation. Manual segmentation was used as ground truth. Dice Scores (DS) were calculated for validation of semiautomatic method. Discordance Index (DI) and area difference percentage between the three above regions from the three modalities were calculated for each patient.

RESULTS

Area of abnormality in the p-map was smaller than the corresponding areas in the T₂ and T₂-FLAIR images in 17 patients; with mean difference percentage of 30 ± 0.15 and 35 ± 0.15, respectively. Abnormal region in the p-map was larger than the corresponding areas in the T₂-FLAIR and T₂ images in 4 patients; with mean difference percentage of 26 ± 0.17 and 29 ± 0.28, respectively. This region in the p-map was larger than the one in the T₂ image and smaller than the one in the T₂-FLAIR image in 3 patients; with mean difference percentage of 34 ± 0.08 and 27 ± 0.06, respectively. Lack of concordance was observed ranged from 0.214-0.772 for T₂-FLAIR/p-map (average: 0.462 ± 0.18), 0.266-0.794 for T₂ /p-map (average: 0.468 ± 0.13) and 0.123-0.776 for T₂/ T₂-FLAIR (average: 0.423 ± 0.2). These regions on three modalities were segmented using a semi-automatic segmentation method with over 86% sensitivity, 90% specificity and 89% dice score for three modalities.

CONCLUSION

It is noted that T₂, T₂-FLAIR and DTI p-maps represent different but complementary information for delineation of glioblastoma tumor margins. Therefore, this study suggests DTI p-map modality as a candidate to improve target volume delineation based on conventional modalities, which needs further investigations with follow-up data to be confirmed.

Using synthetic CT for partial brain radiation therapy: Impact on image guidance

PURPOSE

Recent advancements in synthetic computed tomography (synCT) from magnetic resonance (MR) imaging data have made MRI-only treatment planning feasible in the brain, although synCT performance for image guided radiation therapy (IGRT) is not well understood. This work compares geometric equivalence of digitally reconstructed radiographs (DRRs) from CTs and synCTs for brain cancer patients and quantifies performance for partial brain IGRT.

METHODS AND MATERIALS

Ten brain cancer patients (12 lesions, 7 postsurgical) underwent MR-SIM and CT-SIM. SynCTs were generated by combining ultra-short echo time, T1, T2, and fluid attenuation inversion recovery datasets using voxel-based weighted summation. SynCT and CT DRRs were compared using patient-specific thresholding and assessed via overlap index, Dice similarity coefficient, and Jaccard index. Planar IGRT images for 22 fractions were evaluated to quantify differences between CT-generated DRRs and synCT-generated DRRs in 6 quadrants. Previously validated software was implemented to perform 2-dimensional (2D)-2D rigid registrations using normalized mutual information. Absolute (planar image/DRR registration) and relative (differences between synCT and CT DRR registrations) shifts were calculated for each axis and 3-dimensional vector difference. A total of 1490 rigid registrations were assessed.

RESULTS

DRR agreements in anteroposterior and lateral views for overlap index, Dice similarity coefficient, and Jaccard index were >0.95. Normalized mutual information results were equivalent in 75% of quadrants. Rotational registration results were negligible (<0.07°). Statistically significant differences between CT and synCT registrations were observed in 9/18 matched quadrants/axes (P < .05). The population average absolute shifts were 0.77 ± 0.58 and 0.76 ± 0.59 mm for CT and synCT, respectively, for all axes/quadrants. Three-dimensional vectors were <2 mm in 77.7 ± 10.8% and 76.5 ± 7.2% of CT and synCT registrations, respectively. SynCT DRRs were sensitive in postsurgical cases (vector displacements >2 mm in affected quadrants).

CONCLUSIONS

DRR synCT geometry was robust. Although statistically significant differences were observed between CT and synCT registrations, results were not clinically significant. Future work will address synCT generation in postsurgical settings.

Dosimetric impact of amino acid positron emission tomography imaging for target delineation in radiation treatment planning for high-grade gliomas

Background and purpose

The amino-acid positron emission tomography (PET) tracer 3,4-dihydroxy-6-[¹⁸F] fluoro-l-phenylalanine (¹⁸F-DOPA) has increased sensitivity for detecting regions of biologically aggressive tumors compared to T1 contrast-enhanced (T1-CE) magnetic resonance imaging (MRI). We performed dosimetric evaluation of treatment plans prepared with and without inclusion of ¹⁸F-DOPA-based biological target volume (BTV) evaluating its role in guiding radiotherapy of grade III/IV gliomas.

Materials and methods

Eight patients (five T1-CE, three non-contrast-enhancing [NCE]) were included in our study. MRI only-guided anatomic plans and MRI+¹⁸FDOPA-PET-guided biologic plans were prepared for each patient, and dosimetric data for target volumes and organs at risk (OAR) were compared. High-dose BTV_60Gy was defined as regions with tumor to normal brain (T/N) >2.0, while low-dose BTV_51Gy was initially based on T/N >1.3, but refined per Nuclear Medicine expert.

Results

For T1-CE tumors, planning target volumes (PTV) were larger than MRI-only anatomic target volumes. Despite increases in size of both gross target volumes and PTV, with volumetric-modulated arc therapy planning, no increase of dose to OAR was observed while maintaining similar target dose coverage. For NCE tumors, MRI+¹⁸F-DOPA PET biologic imaging identified a sub-region of the large, T2-FLAIR abnormal signal which may allow a smaller volume to receive the high dose (60 Gy) radiation.

Conclusions

For T1-CE tumors, PTVs were larger than MRI-only anatomic target volumes with no increase of dose to OARs. Therefore, MRI+¹⁸F-DOPA PET-based biologic treatment planning appears feasible in patients with high-grade gliomas.

Generating synthetic CTs from magnetic resonance images using generative adversarial networks

PURPOSE

While MR-only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real-time MR-only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN).

METHODS

Post-Gadolinium T1-Weighted and CT-SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1-weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross-validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics between the synCT and CT images.

RESULTS

GAN training took ~11 h with a new case testing time of 5.7 ± 0.6 s. For GAN, MAEs between synCT and CT-SIM were 89.3 ± 10.3 Hounsfield units (HU) and 41.9 ± 8.6 HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240-255 HU. By comparison, the CNN model had an average full FOV MAE of 102.4 ± 11.1 HU. For GAN, the mean PSNR was 26.6 ± 1.2 and SSIM was 0.83 ± 0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs.

CONCLUSIONS

We developed and validated a GAN model using a single T1-weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real-time MR-only treatment planning in the brain.

Radiotherapy of Glioblastoma 15 Years after the Landmark Stupp's Trial: More Controversies than Standards?

Background

The current standard of care of glioblastoma, the most common primary brain tumor in adults, has remained unchanged for over a decade. Nevertheless, some improvements in patient outcomes have occurred as a consequence of modern surgery, improved radiotherapy and up-to-date management of toxicity. Patients from control arms (receiving standard concurrent chemoradiotherapy and adjuvant chemotherapy with temozolomide) of recent clinical trials achieve better outcomes compared to the median survival of 14.6 months reported in Stupp’s landmark clinical trial in 2005. The approach to radiotherapy that emerged from Stupp’s trial, which continues to be a basis for the current standard of care, is no longer applicable and there is a need to develop updated guidelines for radiotherapy within the daily clinical practice that address or at least acknowledge existing controversies in the planning of radiotherapy.

The goal of this review is to provoke critical thinking about potentially controversial aspects in the radiotherapy of glioblastoma, including among others the issue of target definitions, simultaneously integrated boost technique, and hippocampal sparing.

Conclusions

In conjunction with new treatment approaches such as tumor-treating fields (TTF) and immunotherapy, the role of adjuvant radiotherapy will be further defined. The personalized approach in daily radiotherapy practice is enabled with modern radiotherapy systems.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice’s similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine positron emission tomography image improves the diagnostic accuracy of cerebral glioma grading

PURPOSE

This study aimed to compare the tumor volume definition using conventional magnetic resonance (MR) and 11C-methionine positron emission tomography (MET/PET) images in the differentiation of the pre-operative glioma grade by using whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) maps.

MATERIALS AND METHODS

Thirty-four patients with histopathologically proven primary brain low-grade gliomas (n = 15) and high-grade gliomas (n = 19) underwent pre-operative or pre-biopsy MET/PET, fluid-attenuated inversion recovery, dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging, and contrast-enhanced T1-weighted at 3.0 T. The histogram distribution derived from the nCBV maps was obtained by co-registering the whole tumor volume delineated on conventional MR or MET/PET images, and eight histogram parameters were assessed.

RESULTS

The mean nCBV value had the highest AUC value (0.906) based on MET/PET images. Diagnostic accuracy significantly improved when the tumor volume was measured from MET/PET images compared with conventional MR images for the parameters of mean, 50th, and 75th percentile nCBV value (p = 0.0246, 0.0223, and 0.0150, respectively).

CONCLUSION

Whole-tumor histogram analysis of CBV map provides more valuable histogram parameters and increases diagnostic accuracy in the differentiation of pre-operative cerebral gliomas when the tumor volume is derived from MET/PET images.

Amino-acid PET versus MRI guided re-irradiation in patients with recurrent glioblastoma multiforme (GLIAA) - protocol of a randomized phase II trial (NOA 10/ARO 2013-1)

Background

The higher specificity of amino-acid positron emission tomography (AA-PET) in the diagnosis of gliomas, as well as in the differentiation between recurrence and treatment-related alterations, in comparison to contrast enhancement in T1-weighted MRI was demonstrated in many studies and is the rationale for their implementation into radiation oncology treatment planning. Several clinical trials have demonstrated the significant differences between AA-PET and standard MRI concerning the definition of the gross tumor volume (GTV). A small single-center non-randomized prospective study in patients with recurrent high grade gliomas treated with stereotactic fractionated radiotherapy (SFRT) showed a significant improvement in survival when AA-PET was integrated in target volume delineation, in comparison to patients treated based on CT/MRI alone.

Methods

This protocol describes a prospective, open label, randomized, multi-center phase II trial designed to test if radiotherapy target volume delineation based on FET-PET leads to improvement in progression free survival (PFS) in patients with recurrent glioblastoma (GBM) treated with re-irradiation, compared to target volume delineation based on T1Gd-MRI. The target sample size is 200 randomized patients with a 1:1 allocation ratio to both arms. The primary endpoint (PFS) is determined by serial MRI scans, supplemented by AA-PET-scans and/or biopsy/surgery if suspicious of progression. Secondary endpoints include overall survival (OS), locally controlled survival (time to local progression or death), volumetric assessment of GTV delineated by either method, topography of progression in relation to MRI- or PET-derived target volumes, rate of long term survivors (>1 year), localization of necrosis after re-irradiation, quality of life (QoL) assessed by the EORTC QLQ-C15 PAL questionnaire, evaluation of safety of FET-application in AA-PET imaging and toxicity of re-irradiation.

Discussion

This is a protocol of a randomized phase II trial designed to test a new strategy of radiotherapy target volume delineation for improving the outcome of patients with recurrent GBM. Moreover, the trial will help to develop a standardized methodology for the integration of AA-PET and other imaging biomarkers in radiation treatment planning.

Trial registration

The GLIAA trial is registered with ClinicalTrials.gov (NCT01252459, registration date 02.12.2010), German Clinical Trials Registry (DRKS00000634, registration date 10.10.2014), and European Clinical Trials Database (EudraCT-No. 2012-001121-27, registration date 27.02.2012).

Brain Tumors: An Update on Clinical PET Research in Gliomas

A previous review published in 2012 demonstrated the role of clinical PET for diagnosis and management of brain tumors using mainly FDG, amino acid tracers, and ¹⁸F-fluorothymidine. This review provides an update on clinical PET studies, most of which are motivated by prediction of prognosis and planning and monitoring of therapy in gliomas. For FDG, there has been additional evidence supporting late scanning, and combination with ¹³N ammonia has yielded some promising results. Large neutral amino acid tracers have found widespread applications mostly based on ¹⁸F-labeled compounds fluoroethyltyrosine and fluorodopa for targeting biopsies, therapy planning and monitoring, and as outcome markers in clinical trials. ¹¹C-alpha-methyltryptophan (AMT) has been proposed as an alternative to ¹¹C-methionine, and there may also be a role for cyclic amino acid tracers. ¹⁸F-fluorothymidine has shown strengths for tumor grading and as an outcome marker. Studies using ¹⁸F-fluorocholine (FCH) and ⁶⁸Ga-labeled compounds are promising but have not yet clearly defined their role. Studies on radiotherapy planning have explored the use of large neutral amino acid tracers to improve the delineation of tumor volume for irradiation and the use of hypoxia markers, in particular ¹⁸F-fluoromisonidazole. Many studies employed the combination of PET with advanced multimodal MR imaging methods, mostly demonstrating complementarity and some potential benefits of hybrid PET/MR.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

[Guidelines for the radiotherapy of gliomas]

Gliomas are the most frequent primary brain tumours. Treating these tumours is difficult because of the proximity of organs at risk, infiltrating nature, and radioresistance. Clinical prognostic factors such as age, Karnofsky performance status, tumour location, and treatments such as surgery, radiation therapy, and chemotherapy have long been recognized in the management of patients with gliomas. Molecular biomarkers are increasingly evolving as additional factors that facilitate diagnosis and therapeutic decision-making. These practice guidelines aim at helping in choosing the best treatment, in particular radiation therapy.

Biological imaging in clinical oncology: radiation therapy based on functional imaging

Radiation therapy is one of the most effective tools for cancer treatment. In recent years, intensity-modulated radiation therapy has become increasingly popular in that target dose-escalation can be done while sparing adjacent normal tissues. For this reason, the development of measures to pave the way for accurate target delineation is of great interest. With the integration of functional information obtained by biological imaging with radiotherapy, strategies using advanced biological imaging to visualize metabolic pathways and to improve therapeutic index and predict treatment response are discussed in this article.

Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients

BACKGROUND

Gliomas with mutant isocitrate dehydrogenase (IDH) produce high levels of 2-hydroxyglutarate (2HG) that can be quantitatively measured by 3D magnetic resonance spectroscopic imaging (MRSI). Current glioma MRI primarily relies upon fluid-attenuated inversion recovery (FLAIR) hyperintensity for treatment planning, although this lacks specificity for tumor cells. Here, we investigated the relationship between 2HG and FLAIR in mutant IDH glioma patients to determine whether 2HG mapping is valuable for radiotherapy planning.

METHODS

Seventeen patients with mutant IDH1 gliomas were imaged by 3 T MRI. A 3D MRSI sequence was employed to specifically image 2HG. FLAIR imaging was performed using standard clinical protocol. Regions of interest (ROIs) were determined for FLAIR and optimally thresholded 2HG hyperintensities. The overlap, displacement, and volumes of 2HG and FLAIR ROIs were calculated.

RESULTS

In 8 of 17 (47%) patients, the 2HG volume was larger than FLAIR volume. Across the entire cohort, the mean volume of 2HG was 35.3 cc (range, 5.3-92.7 cc), while the mean volume of FLAIR was 35.8 cc (range, 6.3-140.8 cc). FLAIR and 2HG ROIs had mean overlap of 0.28 (Dice coefficients range, 0.03-0.57) and mean displacement of 12.2 mm (range, 3.2-23.5 mm) between their centers of mass.

CONCLUSIONS

Our results indicate that for a substantial number of patients, the 2HG volumetric assessment of tumor burden is more extensive than FLAIR volume. In addition, there is only partial overlap and asymmetric displacement between the centers of FLAIR and 2HG ROIs. These results may have important implications for radiotherapy planning of IDH mutant glioma.

Identifying radiotherapy target volumes in brain cancer by image analysis

To establish the optimal radiotherapy fields for treating brain cancer patients, the tumour volume is often outlined on magnetic resonance (MR) images, where the tumour is clearly visible, and mapped onto computerised tomography images used for radiotherapy planning. This process requires considerable clinical experience and is time consuming, which will continue to increase as more complex image sequences are used in this process. Here, the potential of image analysis techniques for automatically identifying the radiation target volume on MR images, and thereby assisting clinicians with this difficult task, was investigated. A gradient-based level set approach was applied on the MR images of five patients with grades II, III and IV malignant cerebral glioma. The relationship between the target volumes produced by image analysis and those produced by a radiation oncologist was also investigated. The contours produced by image analysis were compared with the contours produced by an oncologist and used for treatment. In 93% of cases, the Dice similarity coefficient was found to be between 60 and 80%. This feasibility study demonstrates that image analysis has the potential for automatic outlining in the management of brain cancer patients, however, more testing and validation on a much larger patient cohort is required.

Volumetric modulated arc therapy for hippocampal-sparing radiotherapy in transformed low-grade glioma: A treatment planning case report

Timing of radiotherapy for low-grade gliomas is still controversial due to concerns of possible adverse late effects. Prevention of possible late cognitive sequelae by hippocampal avoidance has shown promise in phase II trials. A patient with progressive low-grade glioma with gradual dedifferentiation into anaplastic astrocytoma is presented along with description of radiotherapy planning process attempting to spare the hippocampus. To our knowledge, this is the first described case using volumetric modulated arc technique to spare hippocampus during transformed low-grade glioma radiotherapy. Using modern intensity-modulated radiotherapy systems it is possible to selectively spare hippocampus together with other standard organs at risk. For selected patients, an attempt to spare hippocampus can be considered as long as other dose characteristics are not significantly compromised compared to standard treatment plan created without any effort to avoid hippocampus.

Microrecording and image-guided stereotactic biopsy of deep-seated brain tumors

OBJECT

Image-guided stereotactic brain tumor biopsy cannot easily obtain samples of small deep-seated tumor or selectively sample the most viable region of malignant tumor. Image-guided stereotactic biopsy in combination with depth microrecording was evaluated to solve such problems.

METHODS

Operative records, MRI findings, and pathological specimens were evaluated in 12 patients with small deep-seated brain tumor, in which image-guided stereotactic biopsy was performed with the aid of depth microrecording. The tumors were located in the caudate nucleus (1 patient), thalamus (7 patients), midbrain (2 patients), and cortex (2 patients). Surgery was performed with a frameless stereotactic system in 3 patients and with a frame-based stereotactic system in 9 patients. Microrecording was performed to study the electrical activities along the trajectory in the deep brain structures and the tumor. The correlations were studied between the electrophysiological, MRI, and pathological findings. Thirty-two patients with surface or large brain tumor were also studied, in whom image-guided stereotactic biopsy without microrecording was performed.

RESULTS

The diagnostic yield in the group with microrecording was 100% (low-grade glioma 4, high-grade glioma 4, diffuse large B-cell lymphoma 3, and germinoma 1), which was comparable to 93.8% in the group without microrecording. The postoperative complication rate was as low as that of the conventional image-guided method without using microelectrode recording, and the mortality rate was 0%, although the target lesions were small and deep-seated in all cases. Depth microrecording revealed disappearance of neural activity in the tumor regardless of the tumor type. Neural activity began to decrease from 6.3 ± 4.5 mm (mean ± SD) above the point of complete disappearance along the trajectory. Burst discharges were observed in 6 of the 12 cases, from 3 ± 1.4 mm above the point of decrease of neural activity. Injury discharges were often found at 0.5-1 mm along the trajectory between the area of decreased and disappeared neural activity. Close correlations between electrophysiological, MRI, and histological findings could be found in some cases.

CONCLUSIONS

Image-guided stereotactic biopsy performed using depth microrecording was safe, it provided accurate positional information in real time, and it could distinguish the tumor from brain structures during surgery. Moreover, this technique has potential for studying the epileptogenicity of the brain tumor.

Sustained radiosensitization of hypoxic glioma cells after oxygen pretreatment in an animal model of glioblastoma and in vitro models of tumor hypoxia

Glioblastoma multiforme (GBM) is the most common and lethal form of brain cancer and these tumors are highly resistant to chemo- and radiotherapy. Radioresistance is thought to result from a paucity of molecular oxygen in hypoxic tumor regions, resulting in reduced DNA damage and enhanced cellular defense mechanisms. Efforts to counteract tumor hypoxia during radiotherapy are limited by an attendant increase in the sensitivity of healthy brain tissue to radiation. However, the presence of heightened levels of molecular oxygen during radiotherapy, while conventionally deemed critical for adjuvant oxygen therapy to sensitize hypoxic tumor tissue, might not actually be necessary. We evaluated the concept that pre-treating tumor tissue by transiently elevating tissue oxygenation prior to radiation exposure could increase the efficacy of radiotherapy, even when radiotherapy is administered after the return of tumor tissue oxygen to hypoxic baseline levels. Using nude mice bearing intracranial U87-luciferase xenografts, and in vitro models of tumor hypoxia, the efficacy of oxygen pretreatment for producing radiosensitization was tested. Oxygen-induced radiosensitization of tumor tissue was observed in GBM xenografts, as seen by suppression of tumor growth and increased survival. Additionally, rodent and human glioma cells, and human glioma stem cells, exhibited prolonged enhanced vulnerability to radiation after oxygen pretreatment in vitro, even when radiation was delivered under hypoxic conditions. Over-expression of HIF-1α reduced this radiosensitization, indicating that this effect is mediated, in part, via a change in HIF-1-dependent mechanisms. Importantly, an identical duration of transient hyperoxic exposure does not sensitize normal human astrocytes to radiation in vitro. Taken together, these results indicate that briefly pre-treating tumors with elevated levels of oxygen prior to radiotherapy may represent a means for selectively targeting radiation-resistant hypoxic cancer cells, and could serve as a safe and effective adjuvant to radiation therapy for patients with GBM.