Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

Diagnostic Performance of Fast Brain MRI Compared with Routine Clinical MRI in Patients with Glioma Grades 3 and 4: A Pilot Study

BACKGROUND AND PURPOSE

EPIMix is a fast brain MRI technique not previously investigated in patients with grade 3 and 4 gliomas. This pilot study aimed to investigate the diagnostic performance of EPIMix in the radiological treatment evaluation of adult patients with grade 3 and 4 gliomas compared with routine clinical MRI (rcMRI).

MATERIALS AND METHODS

Patients with grade 3 and 4 gliomas investigated with rcMRI and EPIMix were retrospectively included in the study. Three readers (R1-R3) participated in the radiological assessment applying the Response Assessment for Neuro-Oncology (RANO 2.0) criteria, of whom two (R1 and R2) independently evaluated EPIMix and later rcMRI by measuring contrast-enhancing and non-contrast-enhancing tumor regions at each follow-up. For cases with discrepant evaluations, an unblinded side-by-side (EPIMix and rcMRI) reading was performed together with a third reader (R3). Comparisons between methods (EPIMix versus rcMRI) were performed using the weighted Cohen κ. The sensitivity and specificity to progressive disease (PD) on a follow-up scan were calculated for EPIMix compared with rcMRI with receiver operating characteristic curves (ROC) to assess the area under the curve (AUC).

RESULTS

Of 35 patients (mean age, 53 years; 31% women), a total of 93 MRIs encompassing 58 follow-up investigations showed PD at a blinded reading in 33% of EPIMix (19/58, R1-2), while in 31% (18/58 exams, R1), and 34% (20/58 exams, R2) of rcMRI. An almost perfect agreement for tumor category assessment was found between EPIMix and rcMRI (EPIMixR1 versus rcMRIR1 κ = 0.96; EPIMixR2 versus rcMRIR2 κ = 0.89). The sensitivity for EPIMix to detect PD was 1.00 (0.81-1.00) for R1 and 0.90 (0.68-0.99) for R2, while the specificity was 0.97 (0.86-1.00) for R1 and R2. The AUC for PD was 0.99 for R1 (EPIMixR1 versus rcMRIR1) and 0.94 for R2 (EPIMixR2 versus rcMRIR2). The P value of the DeLong test AUCR1 versus AUCR2 was P = .20 (R1-R2).

CONCLUSIONS

In this pilot study, EPIMix was used as a fast MRI alternative for treatment evaluation of patients with glioma grades 3 and 4, with high but slightly lower diagnostic performance than rcMRI.

Repeatability and Reproducibility of Pseudocontinuous Arterial Spin-Labeling-Measured Brain Perfusion in Healthy Volunteers and Patients with Glioblastoma

BACKGROUND AND PURPOSE

Arterial spin-labeling (ASL) MRI has gained recognition as a quantitative perfusion imaging method for managing patients with brain tumors. Limited studies have so far investigated the reproducibility of ASL-derived perfusion in these patients. This study aimed to evaluate intrasession repeatability and intersession reproducibility of perfusion measurements using 3D pseudocontinuous ASL (pCASL) with TSE Cartesian acquisition with spiral profile reordering (TSE-CASPR) in healthy volunteers (HV) and patients with glioblastoma (GBM) at 3T and to compare them against 3D pCASL with gradient and spin echo (GRASE).

MATERIALS AND METHODS

This prospective study (NCT03922984) was approved by the institutional review board, and written informed consent was obtained from all subjects. HV underwent repeat pCASL evaluations 2-4 weeks apart between November 2021 and October 2022. Patients with GBM were recruited for longitudinal MRI from September 2019 to February 2023. Intrasession repeatability (HV and GBM) and intersession reproducibility (HV only) of pCASL were assessed using linear regression, Bland-Altman analyses, the intraclass correlation coefficient (ICC) with 95% CI, and within-subject coefficients of variation (wsCV).

RESULTS

Twenty HV (9 men; mean age, 25.1 [SD, 1.7] years; range, 23-30 years) and 21 patients with GBM (15 men; mean age, 59.8 [SD, 14.3] years; range, 28-81 years) were enrolled. In imaging sessions, 3D pCASL-measured perfusion with TSE-CASPR and GRASE, respectively, achieved high R 2 values (0.88-0.95; 0.93-0.96), minimal biases (-0.46-0.81; -0.08-0.35 mL/100 g/min), high ICCs [95% CI], 0.96-0.98 [0.94-0.98]; 0.96-0.98 [0.92-0.99]), and low wsCV (6.64%-9.07%; 5.20%-8.16%) in HV (n = 20) and patients with GBM (n = 21). Across imaging sessions, 3D pCASL in HV (n = 20) achieved high R 2 values (0.71; 0.82), minimal biases (-1.2; -0.90 mL/100 g/min), high ICC [95% CI] values (0.85 [0.81-0.89]; 0.90 [0.87-0.93]), and low wsCV values (13.82%; 9.98%).

CONCLUSIONS

Our study demonstrated excellent intrasession repeatability of 3D pCASL-measured cerebral perfusion in HV and patients with GBM and good-to-excellent intersession reproducibility in HV. 3D pCASL with GRASE performed slightly better than 3D pCASL with TSE-CASPR in HV; however, in patients with GBM, 3D pCASL with TSE-CASPR showed better performance in tumor regions with a nearly 2-fold higher SNR. ASL-measured perfusion could serve as a noncontrast quantitative imaging biomarker to facilitate the management of patients with GBM.

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 ± 13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment (AI-VTRA) algorithm was developed using automated, volumetric tumor segmentation. AI-VTRA results were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BT-RADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated. For all BT-RADS categories, AI-VTRA showed moderate agreement with radiologist response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P = .007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P = .012). In summary, our proposed AI-VTRA, following BT-RADS criteria, yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.

Sonodynamic therapy with a single neoadjuvant, diffuse delivery of low-intensity ultrasound with 5-ALA in treatment naïve glioblastoma results in tumor-specific cytotoxic edema and increased apoptosis

PURPOSE

Sonodynamic therapy, which combines a tumor cell-selective sonosensitizer with ultrasound, is gaining attention as a promising new treatment approach for glioblastoma. The objective of this case study is to report on the first applications of 5-aminolevulinic acid (5-ALA) in combination with low-intensity, non-targeted ultrasound as neo-adjuvant treatment in therapy naïve glioblastoma.

METHODS

Three patients with therapy naïve newly diagnosed glioblastoma were treated once before cytoreductive surgery with 5-ALA in combination with hemispheric, low-intensity, non-targeted ultrasound, assuming cell death to be triggered by non-ablative activation of 5-ALA-induced, tumor selective porphyrins.

RESULTS

No adverse effects were noted. Post-procedural MRI indicated a decrease in apparent diffusion coefficient values in tumors, suggesting cytotoxic effects. Relative cerebral blood volumes and leakage were increased for two patients with available perfusion imaging. Tissue obtained during surgery suggested increased cleaved-caspase III expression, a marker of apoptosis.

CONCLUSION

We saw an immediate marked imaging response indicating cytotoxic edema and indications of a histopathology response from just a single treatment. Correlation to clinical outcomes and extension of overall survival remains to be seen. A Phase 1 safety study has been submitted for regulatory approval.

The function of chaperones in the radioresistance of glioblastoma: a new insight into the current knowledge

Radiotherapy remains a cornerstone of brain tumor treatment; however, its effectiveness is frequently undermined by the development of radioresistance. This review highlights the pivotal role of molecular chaperones in promoting radioresistance and explores the potential to increase radioresistance in brain cancers, particularly glioblastoma (GBM). Among chaperones, heat shock proteins (HSPs), such as HSP70 and HSP90, have been identified as key contributors to radioresistance, acting through mechanisms that include the maintenance of protein homeostasis, enhancement of DNA repair processes, and protection of cancer stem cells. Specifically, HSP70 and HSP90 are crucial in stabilizing oncogenic proteins and preventing apoptosis, thus enabling tumor survival during radiotherapy. Also, HSP27 and GRP78 are involved in the radioresistance of brain tumors mainly by suppressing cell death and enhancing tumor stem cell propagation. Emerging evidence also suggests that targeting these chaperones, in combination with radiotherapy, can enhance tumor radiosensitivity, offering promising therapeutic strategies. Recent studies have revealed novel aspects of chaperone-mediated autophagy and interaction with non-coding RNAs, providing deeper insights into the molecular mechanisms underlying radioresistance. This review also addresses the potential of combining chaperone-targeted therapies, such as HSP90 inhibitors, with radiotherapy to overcome resistance. Ultimately, understanding these mechanisms may pave the way for innovative clinical applications and personalized therapeutic approaches in brain tumor treatment.

Advanced imaging characterization of post-chemoradiation glioblastoma stratified by diffusion MRI phenotypes known to predict favorable anti-VEGF response

PURPOSE

Recurrent glioblastomas showing a survival benefit from anti-VEGF agents are known to exhibit a distinct diffusion MRI phenotype. We aim to characterize advanced imaging features of this glioblastoma subset.

METHODS

MRI scans from 87 patients with IDH-wildtype glioblastoma were analyzed. All patients had completed standard chemoradiation and were anti-VEGF-naïve. Contrast-enhancing tumor segmentations were used to extract: the lowest peak of the double gaussian distribution of apparent diffusion coefficient values (ADCL) calculated from diffusion MRI, relative cerebral blood flow (rCBV) values from perfusion MRI, MTRasym @ 3ppm from pH-weighted amine CEST MRI, quantitative T2 and T2* relaxation times (qT2 and qT2*), T1w subtraction map values, and contrast-enhancing tumor volume. Lesions were categorized as high- or low-ADCL using a cutoff of 1240 µm2/s, according to previous studies.

RESULTS

High-ADCL lesions showed significantly lower rCBV (1.02 vs. 1.28, p = 0.0057), higher MTRasym @ 3ppm (2.36% vs. 2.10%, p = 0.0043), and higher qT2 (114.8 ms vs. 100.9 ms, p = 0.0094), compared to low-ADCL lesions. No group differences were seen in contrast-enhancing tumor volume, T1w subtraction map values, and qT2*, nor in clinical variables such as sex category, MGMT status, and EGFR status. Finally, no clear group-specific preferential locations were seen.

CONCLUSION

Post-chemoradiation glioblastomas with a diffusion MRI phenotype that is known to predict a favorable response to anti-VEGF (ADCL ≥1240 µm2/s) have distinct biological features, with different perfusion and metabolic characteristics, and T2 relaxation times.

Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging

BACKGOUND AND PURPOSE

This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high acceleration that is notoriously susceptible to artifacts.

MATERIALS AND METHODS

A total of 4,573 anatomical MR sequences from 1,392 patients undergoing clinically indicated MRI of the brain were used to create a synthetic data set using physics-based, simulated artifacts commonly found in EPI. By using multiple MRI contrasts, we hypothesized the GAN would learn to correct common artifacts while preserving the inherent contrast information, even for contrasts the network has not been trained on. A modified Pix2PixGAN architecture with an Attention-R2UNet generator was used for the model. Three training strategies were employed: (1) An "all-in-one" model trained on all the artifacts at once; (2) a set of "single models", one for each artifact; and a (3) "stacked transfer learning" approach where a model is first trained on one artifact set, then this learning is transferred to a new model and the process is repeated for the next artifact set. Lastly, the "Stacked Transfer Learning" model was tested on ADC maps from single-shot diffusion MRI data in N = 49 patients diagnosed with recurrent glioblastoma to compare visual quality and lesion measurements between the natively acquired images and AI-corrected images.

RESULTS

The "stacked transfer learning" approach had superior artifact reduction performance compared to the other approaches as measured by Mean Squared Error (MSE = 0.0016), Structural Similarity Index (SSIM = 0.92), multiscale SSIM (MS-SSIM = 0.92), peak signal-to-noise ratio (PSNR = 28.10), and Hausdorff distance (HAUS = 4.08mm), suggesting that leveraging pre-trained knowledge and sequentially training on each artifact is the best approach this application. In recurrent glioblastoma, significantly higher visual quality was observed in model predicted images compared to native images, while quantitative measurements within the tumor regions remained consistent with non-corrected images.

CONCLUSIONS

The current study demonstrates the feasibility of using a physics-based method for synthesizing a large data set of images with realistic artifacts and the effectiveness of utilizing this synthetic data set in a "stacked transfer learning" approach to training a GAN for reduction of EPI-based artifacts.

RAS70 peptide targets multiforme glioblastoma by binding to the plasma membrane heat shock protein HSP70

Multiforme glioblastoma-homing peptides, particularly targeting plasma membrane-bound heat shock protein mHsp70, demonstrate great application potential for tumor theranostics. In the current study, to further increase the bioavailability as well as penetration capacity through the blood-brain barrier (BBB) of the mHsp70-targeted peptide TKDNNLLGRFELSG, which is known to bind to the oligomerization sequence of mHsp70 chaperone, the latter was conjugated with tripeptide RGD (forming chimeric peptide termed RAS70). In the model BBB system RAS70 efficiently crossed the barrier accumulating in the glioblastoma cells. Subsequently, in the orthotopic glioma models, intravenous administration of the fluorescently labeled agent (RAS70-sCy7.5) resulted in the tumor retention of peptide (further confirmed by histological studies). Thus, as shown by the biodistribution studies employing epifluorescence imaging, accumulation of RAS70-sCy7.5 in C6 glioma was significantly enhanced as compared to scramble peptide. Local application of the RAS70-sCy7.5 peptide that was sprayed over the dissected brain tissues helped to efficiently delineate the tumors in glioma-bearing animals employing an intraoperative fluorescent imaging system. Tumor-specific internalization of the peptide was further confirmed on the ex vivo primary GBM samples obtained from adult neurooncological patients. In conclusion, RAS70 peptide demonstrated high glioma-homing properties which could be employed for the intraoperative tumor visualization as well as for developing a potential carrier for drug delivery.

Preoperative prediction of diffuse glioma type and grade in adults: a gadolinium-free MRI-based decision tree

OBJECTIVES

To develop a gadolinium-free MRI-based diagnosis prediction decision tree (DPDT) for adult-type diffuse gliomas and to assess the added value of gadolinium-based contrast agent (GBCA) enhanced images.

MATERIALS AND METHODS

This study included preoperative grade 2-4 adult-type diffuse gliomas (World Health Organization 2021) scanned between 2010 and 2021. The DPDT, incorporating eleven GBCA-free MRI features, was developed using 18% of the dataset based on consensus readings. Diagnosis predictions involved grade (grade 2 vs. grade 3/4) and molecular status (isocitrate dehydrogenase (IDH) and 1p/19q). GBCA-free diagnosis was predicted using DPDT, while GBCA-enhanced diagnosis included post-contrast images. The accuracy of these predictions was assessed by three raters with varying experience levels in neuroradiology using the test dataset. Agreement analyses were applied to evaluate the prediction performance/reproducibility.

RESULTS

The test dataset included 303 patients (age (SD): 56.7 (14.2) years, female/male: 114/189, low-grade/high-grade: 54/249, IDH-mutant/wildtype: 82/221, 1p/19q-codeleted/intact: 34/269). Per-rater GBCA-free predictions achieved ≥ 0.85 (95%-CI: 0.80-0.88) accuracy for grade and ≥ 0.75 (95%-CI: 0.70-0.80) for molecular status, while GBCA-enhanced predictions reached ≥ 0.87 (95%-CI: 0.82-0.90) and ≥ 0.77 (95%-CI: 0.71-0.81), respectively. No accuracy difference was observed between GBCA-free and GBCA-enhanced predictions. Group inter-rater agreement was moderate for GBCA-free (0.56 (95%-CI: 0.46-0.66)) and substantial for GBCA-enhanced grade prediction (0.68 (95%-CI: 0.58-0.78), p = 0.008), while substantial for both GBCA-free (0.75 (95%-CI: 0.69-0.80) and GBCA-enhanced (0.77 (95%-CI: 0.71-0.82), p = 0.51) molecular status predictions.

CONCLUSION

The proposed GBCA-free diagnosis prediction decision tree performed well, with GBCA-enhanced images adding little to the preoperative diagnostic accuracy of adult-type diffuse gliomas.

KEY POINTS

Question Given health and environmental concerns, is there a gadolinium-free imaging protocol to preoperatively evaluate gliomas comparable to the gadolinium-enhanced standard practice? Findings The proposed gadolinium-free diagnosis prediction decision tree for adult-type diffuse gliomas performed well, and gadolinium-enhanced MRI demonstrated only limited improvement in diagnostic accuracy. Clinical relevance Even inexperienced raters effectively classified adult-type diffuse gliomas using the gadolinium-free diagnosis prediction decision tree, which, until further validation, can be used alongside gadolinium-enhanced images to respect standard practice, despite this study showing that gadolinium-enhanced images hardly improved diagnostic accuracy.

Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors

OBJECTIVES

This study examines clustering based on shape radiomic features and tumor volume to identify IDH-wildtype glioma phenotypes and assess their impact on overall survival (OS).

MATERIALS AND METHODS

This retrospective study included 436 consecutive patients diagnosed with IDH-wt glioma who underwent preoperative MR imaging. Alongside the total tumor volume, nine distinct shape radiomic features were extracted using the PyRadiomics framework. Different imaging phenotypes were identified using partition around medoids (PAM) clustering on the training dataset (348/436). The prognostic efficacy of these phenotypes in predicting OS was evaluated on the test dataset (88/436). External validation was performed using the public UCSF glioma dataset (n = 397). A decision-tree algorithm was employed to determine the relevance of features associated with cluster affiliation.

RESULTS

PAM clustering identified two clusters in the training dataset: Cluster 1 (n = 233) had a higher proportion of patients with higher sphericity and elongation, while Cluster 2 (n = 115) had a higher proportion of patients with higher maximum 3D diameter, surface area, axis lengths, and tumor volume (p < 0.001 for each). OS differed significantly between clusters: Cluster 1 showed a median OS of 23.8 compared to 11.4 months of Cluster 2 in the holdout test dataset (p = 0.002). Multivariate Cox regression showed improved performance with cluster affiliation over clinical data alone (C index 0.67 vs 0.59, p = 0.003). Cluster-based models outperformed the models with tumor volume alone (evidence ratio: 5.16-5.37).

CONCLUSION

Data-driven clustering reveals imaging phenotypes, highlighting the improved prognostic power of combining shape-radiomics with tumor volume, thereby outperforming predictions based on tumor volume alone in high-grade glioma survival outcomes.

CLINICAL RELEVANCE STATEMENT

Shape-radiomics and volume-based cluster analyses of preoperative MRI scans can reveal imaging phenotypes that improve the prediction of OS in patients with IDH-wild type gliomas, outperforming currently known models based on tumor size alone or clinical parameters.

KEY POINTS

Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.

Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset

PURPOSE

Glioma is a complex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and mutations.

METHODS

A total of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a 3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using a machine learning framework consisting of ReliefF feature ranking and logistic model tree classification algorithms.

RESULTS

The results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 ± 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 ± 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 ± 32.72 selected features.

CONCLUSIONS

Radiomics and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

Extension of T2 Hyperintense Areas in Patients With a Glioma: A Comparison Between High-Quality 7 T MRI and Clinical Scans

Gliomas are highly heterogeneous and often include a nonenhancing component that is hyperintense on T2 weighted MRI. This can often not be distinguished from secondary gliosis and surrounding edema. We hypothesized that the extent of these T2 hyperintense areas can more accurately be determined on high-quality 7 T MRI scans. We investigated the extension, volume, and complexity (shape) of T2 hyperintense areas in patients with glioma on high-quality 7 T MRI scans compared to clinical MRI scans. T2 hyperintense areas of 28 patients were visually compared and manually segmented on 7 T MRI and corresponding clinical (1.5 T/3 T) MRI scans, and the volume and shape markers were calculated and subsequently compared between scans. We showed extension of the T2 hyperintense areas via the corpus callosum to the opposite hemisphere in four patients on the 7 T scans that was not visible on the clinical scan. Furthermore, we found a significantly larger volume of the T2 hyperintense areas on the 7 T scans compared with the clinical scans (7 T scans: 28 mL [12.5-59.1]; clinical scans: 11.9 mL [11.8-56.6]; p = 0.01). We also found a higher complexity of the T2 hyperintense areas on the 7 T scans compared with the clinical scans (convexity, solidity, concavity index and fractal dimension [p < 0.001]). Our study suggests that high-quality 7 T MRI scans may show more detail on the exact extension, size, and complexity of the T2 hyperintense areas in patients with a glioma. This information could aid in more accurate planning of treatment, such as surgery and radiotherapy.

"Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic Spin-and-Gradient-Echo Echoplanar Imaging

BACKGROUND AND PURPOSE

Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics.

MATERIALS AND METHODS

Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation.

RESULTS

Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction.

CONCLUSIONS

Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

Improved detection of brain metastases using contrast-enhanced 3D black-blood TSE sequences compared to post-contrast 3D T1 GRE: a comparative study on 1.5-T MRI

OBJECTIVES

Brain metastases are the most common intracranial malignancy in adults, and their detection is crucial for treatment planning. Post-contrast 3D T1 gradient-recalled echo (GRE) sequences are commonly used for this purpose, but contrast-enhanced 3D T1 turbo spin-echo (TSE) sequences with motion-sensitized driven-equilibrium (MSDE) technique ("black blood") may offer improved detection. This study aimed to compare the effectiveness of contrast-enhanced 3D black blood sequences to standard 3D T1 GRE sequences in detecting brain metastases on a 1.5-T MRI.

MATERIALS AND METHODS

A retrospective analysis of 183 patients with suspected or follow-up brain metastases between May 2022 and September 2023 was conducted. Among these patients, 107 were included in the final analysis. Both post-contrast 3D T1 GRE and 3D black blood sequences were acquired on the same scanner with similar acquisition times. Two neuroradiologists independently evaluated the images for the number, size, and location of metastases. Interobserver variability and statistical analysis were performed.

RESULTS

Among the 107 patients (mean age 60.8 years ± 13.2 years; 55 males, 52 females), 3D black blood sequences detected a significantly higher number of brain metastases, particularly small lesions (< 5 mm), compared to 3D T1 GRE sequences (p < 0.05). There was no significant difference in detecting large metastases (≥ 5 mm) between the sequences. In addition, the black blood sequences provided better conspicuity of metastases in the majority of patients (85%).

CONCLUSION

Contrast-enhanced 3D T1 TSE with MSDE ("black blood") sequences offer improved detection of brain metastases, especially small lesions, on 1.5-T MRI compared to standard 3D T1 GRE sequences.

KEY POINTS

Question Accurate identification of the number and location of brain metastases using MRI is essential for planning and managing effective treatment. Findings Contrast-enhanced 3D T1 TSE black blood sequences detected significantly more small brain metastases than standard 3D T1 GRE sequences on 1.5-T MRI. Clinical relevance The use of 3D black blood sequences on 1.5-T MRI may have the potential to improve the accuracy of detection of brain metastases, leading to better treatment planning and potentially improved patient outcomes.

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strategies. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. Advanced imaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS), improve the differentiation of tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, and 18F-fluluciclovine, facilitate metabolic profiling of high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring of biomarkers such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs), enhancing diagnostic precision. AI-driven algorithms, such as convolutional neural networks, integrate diagnostic tools to improve accuracy, reduce interobserver variability, and accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities to improve outcomes for patients with central nervous system tumors. We advocate for future research integrating these tools into clinical workflows, addressing accessibility challenges, and standardizing methodologies to ensure broad applicability in neuro-oncology.

[Clinical Practice Guidelines for the Management of Brain Metastases from Non-small Cell Lung Cancer with Actionable Gene Alterations in China (2025 Edition)]

Brain metastasis has emerged as a significant challenge in the comprehensive management of patients with non-small cell lung cancer (NSCLC), particularly in those harboring driver gene mutations. Traditional treatments such as radiotherapy and surgery offer limited clinical benefits and are often accompanied by cognitive dysfunction and a decline in quality of life. In recent years, novel small molecule tyrosine kinase inhibitors targeting epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and other pathways have been developed, effectively penetrating the blood-brain barrier while enhancing intracranial drug concentrations and improving patient outcomes. This advancement has transformed the treatment landscape for brain metastases in NSCLC. Consequently, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association and the Brain Metastasis Collaboration Group of the Lung Cancer Youth Expert Committee of the Beijing Medical Reward Foundation have jointly initiated and formulated the Clinical Practice Guidelines for the Management of Brain Metastases from Non-small Cell Lung Cancer with Actionable Gene Alterations in China (2025 Edition). This guideline integrates the latest research findings with clinical experience, adhering to multidisciplinary treatment principles, and encompasses aspects such as diagnosis, timing of intervention, and systemic and local treatment options for driver gene positive NSCLC brain metastases. Additionally, it proposes individualized treatment strategies tailored to different driver gene types, aiming to provide clinicians with a reference to enhance the overall diagnostic and therapeutic standards for NSCLC brain metastases in China.
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Identification of key elements in MRI reporting of intracranial meningiomas based on a nationwide survey of clinical experts in Germany

While MRI has become the imaging modality of choice for intracranial meningiomas, no radiologic reporting guidance exists to date that relies on a systematic collection of information relevant to the core medical disciplines involved in the management of these patients. To address this issue, a nationwide expert survey was conducted in Germany. A literature-based catalog of potential reporting elements for MRI examinations of meningioma patients was developed interdisciplinarily. Subsequently, all board-certified members of the German Societies of Neuroradiology, Neurosurgery and Radiation Oncology with expertise in managing meningioma patients were invited to vote on the relevance of the suggested items via online survey. A total of 150 experts participated in the study (104 neurosurgeons/radiation oncologists, 46 neuroradiologists). The reporting elements of tumor location, extent, growth pattern, contrast uptake, associated cysts, and impact on adjacent anatomic structures received widespread approval (> 75.0% of all participants). In addition, a vast majority (> 75.0%) supported reference to perifocal edema, signs of mass effect, and hydrocephalus. Postoperative imaging is particularly requested to describe the extent of resection (94.0%) and treatment-related changes (89.3%). Advanced methods (diffusion, perfusion, proton spectroscopy) and meningioma-specific classifications (Nauta, Zee, Sindou) were judged to be less relevant (< 50.0% agreement) to MRI reporting. To serve as a vital clinical communication tool and enable an optimal contribution to the care of meningioma patients, the radiological report should focus on the fundamental information requirements of the neuro-oncology treatment team encompassing primarily tumor location, extent, tissue imaging characteristics, and potential impairment of neighboring anatomical structures.

A comparative study of preclinical and clinical molecular imaging response to EGFR inhibition using osimertinib in glioblastoma

Background

To demonstrate the potential value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) as a rapid, non-invasive metabolic imaging surrogate for pharmacological modulation of EGFR signaling in EGFR-driven GBM, we synchronously conducted a preclinical imaging study using patient-derived orthotopic xenograft (PDOX) models and validated it in a phase II molecular imaging study in recurrent GBM (rGBM) patients using osimertinib.

Methods

A GBM PDOX mouse model study was performed concurrently with an open-label, single-arm, single-center, phase II study of osimertinib (NCT03732352) that enrolled 12 patients with rGBM with EGFR alterations. Patients received osimertinib daily and 3 18F-FDG PET scans: two 24 h apart prior to dosing, and one 48 h after dosing.

Results

GBM PDOX models suggest osimertinib has limited impact on both 18F-FDG uptake (+ 9.8%-+25.9%) and survival (+ 15.5%; P = .01), which may be explained by insufficient exposure in the brain (Kpuu: 0.30) required to robustly inhibit the EGFR alterations found in GBM. Treatment with osimertinib had subtle, but measurable decreases in the linear rate of change of 18F-FDG nSUV growth rate averaging -4.5% per day (P = .01) and change in 18F-FDG uptake was correlated with change in tumor growth rate (R2 = 0.4719, P = .0195). No metabolic (PERCIST) or radiographic (RANO) responses were seen, and no improvements in PFS or OS were observed.

Conclusions

This study demonstrated the feasibility of using FDG PET as a clinically reliable imaging biomarker for assessing EGFR inhibition in GBM, while revealing osimertinib's limited impact on both metabolic activity and tumor growth in GBM, findings that were concordant between preclinical and clinical observations.

Magnetic resonance imaging techniques for monitoring glioma response to chemoradiotherapy

PURPOSE

Treatment response assessment for gliomas currently uses changes in tumour size as measured with T1- and T2-weighted MRI. However, changes in tumour size may occur many weeks after therapy completion and are confounded by radiation treatment effects. Advanced MRI techniques sensitive to tumour physiology may provide complementary information to evaluate tumour response at early timepoints during therapy. The objective of this review is to provide a summary of the history and current knowledge regarding advanced MRI techniques for early treatment response evaluation in glioma.

METHODS

The literature survey included perfusion MRI, diffusion-weighted imaging, quantitative magnetization transfer imaging, and chemical exchange transfer MRI. Select articles spanning the history of each technique as applied to treatment response evaluation in glioma were chosen. This report is a narrative review, not formally systematic.

RESULTS

Chemical exchange saturation transfer imaging potentially offers the earliest method to detect tumour response due to changes in metabolism. Diffusion-weighted imaging is sensitive to changes in tumour cellularity later during radiotherapy and is prognostic for progression-free and overall survival. Substantial evidence suggests that perfusion MRI can differentiate between tumour recurrence and treatment effect, but consensus regarding acquisition, processing, and interpretation is still lacking. Magnetization transfer imaging shows promise for detecting subtle white matter damage which could indicate tumour invasion, but more research in this area is needed.

CONCLUSION

Advanced MRI techniques show potential for early treatment response assessment, but each technique alone lacks specificity. Multiparametric imaging may be necessary to aid biological interpretation and enable treatment guidance.

The potential of GPT-4 advanced data analysis for radiomics-based machine learning models

Background

This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI.

Methods

Radiomic features were extracted from preoperative MRI of n = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe). External validation was performed on 2 public glioma datasets D2 (n = 160) and D3 (n = 410).

Results

GPT-4 achieved the highest accuracy of 0.820 (95% CI = 0.819-0.821) on the D3 dataset with N4/WS normalization, significantly outperforming the benchmark model's accuracy of 0.678 (95% CI = 0.677-0.680) (P < .001). Class-wise analysis showed performance variations across different glioma types. In the IDH-wildtype group, GPT-4 had a recall of 0.997 (95% CI = 0.997-0.997), surpassing the benchmark's 0.742 (95% CI = 0.740-0.743). For the IDH-mut 1p/19q-non-codel group, GPT-4's recall was 0.275 (95% CI = 0.272-0.279), lower than the benchmark's 0.426 (95% CI = 0.423-0.430). In the IDH-mut 1p/19q-codel group, GPT-4's recall was 0.199 (95% CI = 0.191-0.206), below the benchmark's 0.730 (95% CI = 0.721-0.738). On the D2 dataset, GPT-4's accuracy was significantly lower (P < .001) than the benchmark's, with N4/WS achieving 0.668 (95% CI = 0.666-0.671) compared with 0.719 (95% CI = 0.717-0.722) (P < .001). Class-wise analysis revealed the same pattern as observed in D3.

Conclusions

GPT-4 can autonomously develop radiomics-based MLMs, achieving performance comparable to handcrafted MLMs. However, its poorer class-wise performance due to unbalanced datasets shows limitations in handling complete end-to-end ML pipelines.

A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Efficacy of Adding Veliparib to Temozolomide for Patients With MGMT-Methylated Glioblastoma: A Randomized Clinical Trial

Importance

The prognosis for patients with glioblastoma is poor following standard therapy with surgical resection, radiation, temozolomide, and tumor-treating fields.

Objectives

To evaluate the combination of veliparib and temozolomide in glioblastoma based on preclinical data demonstrating significant chemosensitizing effects of the polyadenosine diphosphate-ribose polymerase 1/2 inhibitor veliparib when combined with temozolomide.

Design, Setting, and Participants

Patients with newly diagnosed glioblastoma with MGMT promoter hypermethylation who had completed concomitant radiation and temozolomide were enrolled between December 15, 2014, and December 15, 2018, in this Alliance for Clinical Trials in Oncology trial. The data for this analysis were locked on April 21, 2023.

Interventions

Patients were randomized and treated with standard adjuvant temozolomide (150-200 mg/m2 orally, days 1-5) combined with either placebo or veliparib (40 mg orally, twice daily, days 1-7) for 6 cycles.

Main Outcomes and Measures

The primary end point for the phase 3 portion of the trial was overall survival (OS).

Results

There were 322 patients randomized during the phase 2 accrual period and an additional 125 patients randomized to complete the phase 3 accrual, for a total of 447 patients in the final phase 3 analysis. The median (range) age for patients was 60 (20-85) years and 190 patients (42.5%) were female. The median OS was 24.8 months (90% CI, 22.6-27.7) for the placebo arm and 28.1 months (90% CI, 24.3-33.3) for the veliparib arm (P = .17). The difference in survival did not meet the prespecified efficacy end point. However, there was a separation of the survival curves that favored the veliparib arm over 24 to 48 months of follow-up. The experimental combination was well tolerated with an acceptable elevation in grade 3 or 4 hematologic toxic effects.

Conclusions and Relevance

This trial found that adding veliparib to adjuvant temozolomide did not significantly extend OS in patients with newly diagnosed, MGMT-hypermethylated glioblastoma.

Trial Registration

ClinicalTrials.gov Identifier: NCT02152982.

Impact of Sex Hormones on Glioblastoma: Sex-Related Differences and Neuroradiological Insights

Glioblastoma (GBM) displays significant gender disparities, being 1.6 times more prevalent in men, with a median survival time of 15.0 months for males compared to 25.5 months for females. These differences may be linked to gonadal steroid hormones, particularly testosterone, which interacts with the androgen receptor (AR) to promote tumor proliferation. Conversely, estrogen (E2), progesterone (P4), and P4 metabolites exert more complex effects on GBM. Despite these insights, the identification of reliable hormonal tumor markers remains challenging, and studies investigating hormone therapies yield inconclusive results due to small sample sizes and heterogeneous tumor histology. Additionally, genetic, epigenetic, and immunological factors play critical roles in sex disparities, with female patients demonstrating increased O6-Methylguanine-DNA methyltransferase promoter methylation and greater genomic instability. These complexities highlight the need for personalized therapeutic strategies that integrate hormonal influences alongside other sex-specific biological characteristics in the management of GBM. In this review, we present the current understanding of the potential role of sex hormones in the natural history of GBM.

Glioma subtype prediction based on radiomics of tumor and peritumoral edema under automatic segmentation

Comprehensive and non-invasive preoperative molecular diagnosis is important for prognostic and therapy decision-making in adult-type diffuse gliomas. We employed a deep learning method for automatic segmentation of brain gliomas directly from conventional magnetic resonance imaging (MRI) scans of the tumor core and peritumoral edema regions based on available glioma MRI data provided in the BraTS2021. Three-dimensional volumes of interest were segmented from 424 cases of glioma imaging data retrospectively obtained from two medical centers using the segmentation method and radiomic features were extracted. We developed a subtype prediction model based on extracted radiomic features and analyzed significance and correlations between glioma morphological characteristics and pathological features using data from patients with adult-type diffuse glioma. The automated segmentation achieved mean Dice scores of 0.884 and 0.889 for the tumor core and whole tumor, respectively. The area under the receiver operating characteristic curve for the prediction of adult-type diffuse gliomas subtypes was 0.945. "Glioblastoma, IDH-wildtype", "Astrocytoma, IDH-mutant", and "Oligodendroglioma, IDH-mutant, 1p/19q-coded" showed AUCs of 0.96, 0.914, and 0.961, respectively, for subtype prediction. Glioma morphological characteristics, molecular and pathological levels, and clinical data showed significant differences and correlations. An automatic segmentation model for gliomas based on 3D U-Nets was developed, and the prediction model for gliomas built using the parameters obtained from the automatic segmentation model showed high overall performance.

SEOM-GEINO clinical guidelines for grade 2 gliomas (2023)

The 2021 World Health Organization (WHO) classification has updated the definition of grade 2 gliomas and the presence of isocitrate dehydrogenase (IDH) mutation has been deemed the cornerstone of diagnosis. Though slow-growing and having a low proliferative index, grade 2 gliomas are incurable by surgery and complementary treatments are vital to improving prognosis. This guideline provides recommendations on the multidisciplinary treatment of grade 2 astrocytomas and oligodendrogliomas based on the best evidence available.

Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma

Glioblastoma is the most commonly occurring malignant brain tumor, with a high mortality rate despite current treatments. Its classification has evolved over the years to include not only histopathological features but also molecular findings. Given the heterogeneity of glioblastoma, molecular biomarkers for diagnosis have become essential for initiating treatment with current therapies, while new technologies for detecting specific variations using computational tools are being rapidly developed. Advances in molecular genetics have made possible the creation of tailored therapies based on specific molecular targets, with various degrees of success. This review provides an overview of the latest advances in the fields of histopathology and radiogenomics and the use of molecular markers for management of glioblastoma, as well as the development of new therapies targeting the most common molecular markers. Furthermore, we offer a summary of the results of recent preclinical and clinical trials to recognize the current trends of investigation and understand the possible future directions of molecular targeted therapies in glioblastoma.

[A predictive model for survival outcomes of glioma patients based on multi-parametric, multi-regional MRI radiomics features and clinical features]

OBJECTIVE

To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.

METHODS

We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone, tumor core, and whole tumor on T1, T2, and T1-weighted contrast-enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) sequences. The cases were divided into a training set (271 cases) and a test set (117 cases). Random survival forest algorithms were used to select the radiomics features associated with overall survival (OS) in the training set to construct a radiomic score (Rad-score), based on which the patients were classified into high- and low-risk groups for Kaplan-Meier survival analysis. Cox proportional hazard regression models for the 3 different tumor zones were constructed, and their performance for predicting 1- and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients. The best-performing model was used for constructing a nomogram for survival predictions.

RESULTS

Five radiomics features from the tumor core, 7 from the peritumoral edema zone, and 5 from the whole tumor were selected. In both the training and test sets, the high- and low-risk groups had significantly different OS (P < 0.05), and age, IDH status and Rad-score were independent factors affecting OS. The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set, 0.764 and 0.800 in the test set, and 0.938 and 0.917 in external validation, respectively.

CONCLUSION

The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Whole-body fluorescence cryotomography identifies a fast-acting, high-contrast, durable contrast agent for fluorescence-guided surgery

Imaging of tumor-specific fluorescent contrast agents to guide tumor removal has been shown to improve outcomes and is now standard practice for some neurosurgical procedures. However, many agents require administration hours before surgery, a practical challenge, and may exhibit inconsistent concordance with contrast-enhanced MRI (CE-MRI), the current standard for diagnosing and guiding glioma removal. A fluorescent agent that accurately marks tumor shortly after administration and is otherwise similar to CE-MRI would help overcome these shortcomings. Methods: We used whole-body 3-D fluorescence cryo-imaging and co-registered CE-MRI volumes to evaluate several fluorescent contrast agent candidates for diagnostic performance and concordance with CE-MRI. Mice with brain tumors were administered a cocktail of fluorescent agent candidates and a MRI contrast agent, and then imaged with MRI and fluorescence cryo-imaging at several timepoints after administration. The high-resolution 3-D cryo-imaging volumes of the fluorescent agents were used to determine diagnostic performance metrics and correlation with CE-MRI. Results: While all agents showed positive metrics, one agent, tetramethylrhodamine conjugated to a small polyethylene glycol chain (TMR-PEG1k), outperformed the others, exhibiting minimal normal brain signal, high tumor-to-background-ratio, diagnostic accuracy, and cross-correlation to CE-MRI at all post-administration timepoints (10-90 min) and tumor lines examined. Conclusion: These favorable properties establish TMR-PEG1k as a promising candidate for surgical guidance.

Digital "flipbooks" for enhanced visual assessment of simple and complex brain tumors

Typical longitudinal radiographic assessment of brain tumors relies on side-by-side qualitative visualization of serial magnetic resonance images (MRIs) aided by quantitative measurements of tumor size. However, when assessing slowly growing tumors and/or complex tumors, side-by-side visualization and quantification may be difficult or unreliable. Whole-brain, patient-specific "digital flipbooks" of longitudinal scans are a potential method to augment radiographic side-by-side reads in clinical settings by enhancing the visual perception of changes in tumor size, mass effect, and infiltration across multiple slices over time. In this approach, co-registered, consecutive MRI scans are displayed in a slide deck, where one slide displays multiple brain slices of a single timepoint in an array (eg, 3 × 5 "mosaic" view of slices). The flipbooks are viewed similarly to an animated flipbook of cartoons/photos so that subtle radiographic changes are visualized via perceived motion when scrolling through the slides. Importantly, flipbooks can be created easily with free, open-source software. This article describes the step-by-step methodology for creating flipbooks and discusses clinical scenarios for which flipbooks are particularly useful. Example flipbooks are provided in Supplementary Material.

Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma

BACKGROUND AND PURPOSE

Resting-state functional MRI (rs-fMRI) can be used to estimate functional connectivity (FC) between different brain regions, which may be of value for identifying cognitive impairment in patients with brain tumors. Unfortunately, neither rs-fMRI nor neurocognitive assessments are routinely assessed clinically, mostly due to limitations in examination time and cost. Since DSC perfusion MRI is often used clinically to assess tumor vascularity and similarly uses a gradient-echo-EPI sequence for T2*-sensitivity, we theorized a "pseudo-rs-fMRI" signal could be derived from DSC perfusion to simultaneously quantify FC and perfusion metrics, and these metrics can be used to estimate cognitive impairment in patients with brain tumors.

MATERIALS AND METHODS

Twenty-four consecutive patients with gliomas were enrolled in a prospective study that included DSC perfusion MRI, resting-sate functional MRI (rs-fMRI), and neurocognitive assessment. Voxelwise modeling of contrast bolus dynamics during DSC acquisition was performed and then subtracted from the original signal to generate a residual "pseudo-rs-fMRI" signal. Following the preprocessing of pseudo-rs-fMRI, full rs-fMRI, and a truncated version of the full rs-fMRI (first 100 timepoints) data, the default mode, motor, and language network maps were generated with atlas-based ROIs, Dice scores were calculated for the resting-state network maps from pseudo-rs-fMRI and truncated rs-fMRI using the full rs-fMRI maps as reference. Seed-to-voxel and ROI-to-ROI analyses were performed to assess FC differences between cognitively impaired and nonimpaired patients.

RESULTS

Dice scores for the group-level and patient-level (mean±SD) default mode, motor, and language network maps using pseudo-rs-fMRI were 0.905/0.689 ± 0.118 (group/patient), 0.973/0.730 ± 0.124, and 0.935/0.665 ± 0.142, respectively. There was no significant difference in Dice scores between pseudo-rs-fMRI and the truncated rs-fMRI default mode (P = .97) or language networks (P = .30), but there was a difference in motor networks (P = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), P = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), P = .0013, P = .29 compared with pseudo-rs-fMRI).

CONCLUSIONS

DSC perfusion MRI-derived pseudo-rs-fMRI data can be used to perform typical rs-fMRI FC analyses that may identify cognitive decline in patients with brain tumors while still simultaneously performing perfusion analyses.

Modality redundancy for MRI-based glioblastoma segmentation

PURPOSE

Automated glioblastoma segmentation from magnetic resonance imaging is generally performed on a four-modality input, including T1, contrast T1, T2 and FLAIR. We hypothesize that information redundancy is present within these image combinations, which can possibly reduce a model's performance. Moreover, for clinical applications, the risk of encountering missing data rises as the number of required input modalities increases. Therefore, this study aimed to explore the relevance and influence of the different modalities used for MRI-based glioblastoma segmentation.

METHODS

After the training of multiple segmentation models based on nnU-Net and SwinUNETR architectures, differing only in their amount and combinations of input modalities, each model was evaluated with regard to segmentation accuracy and epistemic uncertainty.

RESULTS

Results show that T1CE-based segmentation (for enhanced tumor and tumor core) and T1CE-FLAIR-based segmentation (for whole tumor and overall segmentation) can reach segmentation accuracies comparable to the full-input version. Notably, the highest segmentation accuracy for nnU-Net was found for a three-input configuration of T1CE-FLAIR-T1, suggesting the confounding effect of redundant input modalities. The SwinUNETR architecture appears to suffer less from this, where said three-input and the full-input model yielded statistically equal results.

CONCLUSION

The T1CE-FLAIR-based model can therefore be considered as a minimal-input alternative to the full-input configuration. Addition of modalities beyond this does not statistically improve and can even deteriorate accuracy, but does lower the segmentation uncertainty.

Accelerating brain three-dimensional T2 fluid-attenuated inversion recovery using artificial intelligence-assisted compressed sensing: a comparison study with parallel imaging

Background

Shortening the acquisition time of brain three-dimensional T2 fluid-attenuated inversion recovery (3D T2 FLAIR) by using acceleration techniques has the potential to reduce motion artifacts in images and facilitate clinical application. This study aimed to assess the image quality of brain 3D T2 FLAIR accelerated by artificial intelligence-assisted compressed sensing (ACS) in comparison to 3D T2 FLAIR accelerated by parallel imaging (PI).

Methods

In this prospective cohort study, 102 consecutive participants, including both healthy individuals and those with suspected brain diseases, were recruited and underwent both ACS- and PI-3D T2 FLAIR scans with a 3.0-Tesla magnetic resonance imaging system from February 2023 to October 2023 in Beijing Tiantan Hospital, Capital Medical University. Quantitative assessment involved white matter (WM) and gray matter (GM) signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), whole-image sharpness, and tumor volume. Qualitative assessment included the scoring of overall image quality, GM-WM border sharpness, and diagnostic confidence in lesion detection.

Results

ACS-3D T2 FLAIR exhibited a shorter acquisition time compared to PI-3D T2 FLAIR (105 vs. 320 seconds). ACS-3D T2 FLAIR, compared to PI-3D T2 FLAIR, demonstrated a significantly higher mean SNRWM (25.922±6.811 vs. 22.544±5.853; P<0.001), SNRGM (18.324±7.137 vs. 17.102±6.659; P=0.049), CNRWM/GM (4.613±1.547 vs. 4.160±1.552; P<0.001), and sharpness (0.413±0.049 vs. 0.396±0.034; P<0.001), while no significant differences were found for the overall image quality ratings (P=0.063) or GM-WM border sharpness ratings (P=0.125). A good agreement on tumor volume was achieved between ACS-3D T2 FLAIR and PI-3D T2 FLAIR images (intraclass correlation coefficient =0.999; 0.998-1.000; P<0.001). Images acquired with ACS demonstrated nearly equivalent diagnostic confidence to those obtained with PI (P>0.05).

Conclusions

The ACS technique offers a substantial reduction in scanning time for brain 3D T2 FLAIR compared to PI while maintaining good image quality and equivalent diagnostic confidence.

[An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma]

OBJECTIVE

To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG).

METHODS

We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in twodimensional plane. Convergence experiments were used to verify the feasibility of the model.

RESULTS

For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%.

CONCLUSION

The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.

Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance

BACKGROUND

Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking.

PURPOSE

Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment.

DATA SOURCES

MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023.

STUDY SELECTION

Original articles predicting diagnosis, progression, and survival in patients with glioma were included.

DATA ANALYSIS

The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided.

DATA SYNTHESIS

Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium (n = 33) and low (n = 2). Recurring objectives were overall survival (n = 18) and isocitrate dehydrogenase mutation (IDH; n = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n = 2, grade 2/3 glioma n = 1, grade 3 glioma n = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I2 = 52%). IDH mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies (n = 17).

LIMITATIONS

Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for IDH was limited due to a high study heterogeneity.

CONCLUSIONS

Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.

Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo

T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored. Detection sensitivity was evaluated against standard multiecho gradient echo (MEGE) QSM using 3-T in vivo data of 15 subjects with a combined total of 108 confirmed microbleeds. The two methods were compared based on the microbleed size and susceptibility measurements. In addition, simulations explored the detection sensitivity of MPRAGE-QSM at different representative magnetic field strengths and echo times using microbleeds of different size, susceptibility, and location. Results showed that in vivo microbleeds appeared to be smaller (× 0.54) and of higher mean susceptibility (× 1.9) on MPRAGE-QSM than on MEGE-QSM, but total susceptibility estimates were in closer agreement (slope: 0.97, r2: 0.94), and detection sensitivity was comparable. In simulations, QSM at 1.5 T had a low contrast-to-noise ratio that obscured the detection of many microbleeds. Signal-to-noise ratio (SNR) levels at 3 T and above resulted in better contrast and increased detection. The detection rates for microbleeds of minimum one-voxel diameter and 0.4-ppm susceptibility were 0.55, 0.80, and 0.88 at SNR levels of 1.5, 3, and 7 T, respectively. Size and total susceptibility estimates were more consistent than mean susceptibility estimates, which showed size-dependent underestimation. MPRAGE-QSM provides an opportunity to detect and quantify the size and susceptibility of microbleeds of at least one-voxel diameter at B0 of 3 T or higher with no additional time cost, when standard T2*-weighted images are not available or have inadequate spatial resolution. The total susceptibility measure is more robust against sequence variations and might allow combining data from different protocols.

Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model

Glioblastoma is the most common and aggressive primary malignant brain tumor with poor prognosis. Novel immunotherapeutic approaches are currently under investigation. Even though magnetic resonance imaging (MRI) is the most important imaging tool for treatment monitoring, response assessment is often hampered by therapy-related tissue changes. As tumor and therapy-associated tissue reactions differ structurally, we hypothesize that biomechanics could be a pertinent imaging proxy for differentiation. Longitudinal MRI and magnetic resonance elastography (MRE) were performed to monitor response to immunotherapy with a toll-like receptor 7/8 agonist in orthotopic syngeneic experimental glioma. Imaging results were correlated to histology and light sheet microscopy data. Here, we identify MRE as a promising non-invasive imaging method for immunotherapy-monitoring by quantifying changes in response-related tumor mechanics. Specifically, we show that a relative softening of treated compared to untreated tumors is linked to the inflammatory processes following therapy-induced re-education of tumor-associated myeloid cells. Mechanistically, combined effects of myeloid influx and inflammation including extracellular matrix degradation following immunotherapy form the basis of treated tumors being softer than untreated glioma. This is a very early indicator of therapy response outperforming established imaging metrics such as tumor volume. The overall anti-tumor inflammatory processes likely have similar effects on human brain tissue biomechanics, making MRE a promising tool for gauging response to immunotherapy in glioma patients early, thereby strongly impacting patient pathway.

Exploring the Impact of 3D Fast Spin Echo and Inversion Recovery Gradient Echo Sequences Magnetic Resonance Imaging Acquisition on Automated Brain Tumor Segmentation

Objective

To conduct a study comparing the performance of automated segmentation techniques using 2 different contrast-enhanced T1-weighted (CET1) magnetic resonance imaging (MRI) acquisition protocol.

Patients and Methods

We collected 100 preoperative glioblastoma (GBM) MRIs consisting of 50 IR-GRE and 50 3-dimensional fast spin echo (3D-FSE) image sets. Their gold-standard tumor segmentation mask was created based on the expert opinion of a neuroradiologist. Cases were randomly divided into training and test sets. We used the no new UNet (nnUNet) architecture pretrained on the 501-image public data set containing IR-GRE sequence image sets, followed by 2 training rounds with the IR-GRE and 3D-FSE images, respectively. For each patient, in the IR-GRE and 3D-FSE test sets, we had 2 prediction masks, one from the model fine-tuned with the IR-GRE training set and one with 3D-FSE. The dice similarity coefficients (DSCs) of the 2 sets of results for each case in the test sets were compared using the Wilcoxon tests.

Results

Models trained on 3D-FSE images outperformed IR-GRE models in lesion segmentation, with mean DSC differences of 0.057 and 0.022 in the respective test sets. For the 3D-FSE and IR-GRE test sets, the calculated P values comparing DSCs from 2 models were .02 and .61, respectively.

Conclusion

Including 3D-FSE MRI in the training data set improves segmentation performance when segmenting 3D-FSE images.

TLR agonists polarize interferon responses in conjunction with dendritic cell vaccination in malignant glioma: a randomized phase II Trial

In this randomized phase II clinical trial, we evaluated the effectiveness of adding the TLR agonists, poly-ICLC or resiquimod, to autologous tumor lysate-pulsed dendritic cell (ATL-DC) vaccination in patients with newly-diagnosed or recurrent WHO Grade III-IV malignant gliomas. The primary endpoints were to assess the most effective combination of vaccine and adjuvant in order to enhance the immune potency, along with safety. The combination of ATL-DC vaccination and TLR agonist was safe and found to enhance systemic immune responses, as indicated by increased interferon gene expression and changes in immune cell activation. Specifically, PD-1 expression increases on CD4+ T-cells, while CD38 and CD39 expression are reduced on CD8+ T cells, alongside an increase in monocytes. Poly-ICLC treatment amplifies the induction of interferon-induced genes in monocytes and T lymphocytes. Patients that exhibit higher interferon response gene expression demonstrate prolonged survival and delayed disease progression. These findings suggest that combining ATL-DC with poly-ICLC can induce a polarized interferon response in circulating monocytes and CD8+ T cells, which may represent an important blood biomarker for immunotherapy in this patient population.Trial Registration: ClinicalTrials.gov Identifier: NCT01204684.

Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI

OBJECTIVE

To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection.

MATERIALS AND METHODS

Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression).

RESULTS

In IDH wild-type gliomas (IDHwt-i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09-0.14). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%.

CONCLUSIONS

Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR1,ss), with a single contrast injection.

CLINICAL RELEVANCE STATEMENT

Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture.

KEY POINTS

• Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI)

PURPOSE

Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework.

METHODS

A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results.

RESULTS

The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions.

CONCLUSIONS

The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

OBJECTIVES

Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes.

METHODS

Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160).

RESULTS

Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone.

CONCLUSION

Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting.

CLINICAL RELEVANCE STATEMENT

Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes.

KEY POINTS

• MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.

The use of PET/MRI in radiotherapy

Positron emission tomography/magnetic resonance imaging (PET/MRI) is a hybrid imaging technique that quantitatively combines the metabolic and functional data from positron emission tomography (PET) with anatomical and physiological information from MRI. As PET/MRI technology has advanced, its applications in cancer care have expanded. Recent studies have demonstrated that PET/MRI provides unique advantages in the field of radiotherapy and has become invaluable in guiding precision radiotherapy techniques. This review discusses the rationale and clinical evidence supporting the use of PET/MRI for radiation positioning, target delineation, efficacy evaluation, and patient surveillance.Critical relevance statement This article critically assesses the transformative role of PET/MRI in advancing precision radiotherapy, providing essential insights into improved radiation positioning, target delineation, efficacy evaluation, and patient surveillance in clinical radiology practice.Key points• The emergence of PET/MRI will be a key bridge for precise radiotherapy.• PET/MRI has unique advantages in the whole process of radiotherapy.• New tracers and nanoparticle probes will broaden the use of PET/MRI in radiation.• PET/MRI will be utilized more frequently for radiotherapy.

Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images

Many deep learning based methods have been proposed for brain tumor segmentation. Most studies focus on deep network internal structure to improve the segmentation accuracy, while valuable external information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists often screen lesion regions with normal appearance as reference in mind, in this paper, we propose a novel deep framework for brain tumor segmentation, where normal brain images are adopted as reference to compare with tumor brain images in a learned feature space. In this way, features at tumor regions, i.e., tumor-related features, can be highlighted and enhanced for accurate tumor segmentation. It is known that routine tumor brain images are multimodal, while normal brain images are often monomodal. This causes the feature comparison a big issue, i.e., multimodal vs. monomodal. To this end, we present a new feature alignment module (FAM) to make the feature distribution of monomodal normal brain images consistent/inconsistent with multimodal tumor brain images at normal/tumor regions, making the feature comparison effective. Both public (BraTS2022) and in-house tumor brain image datasets are used to evaluate our framework. Experimental results demonstrate that for both datasets, our framework can effectively improve the segmentation accuracy and outperforms the state-of-the-art segmentation methods. Codes are available at https://github.com/hb-liu/Normal-Brain-Boost-Tumor-Segmentation.

Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas

BACKGROUND

Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment.

METHODS

We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment.

RESULTS

At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL.

CONCLUSIONS

We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.

Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification

OBJECTIVES

Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification.

METHODS

This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor.

RESULTS

Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05).

CONCLUSIONS

GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended.

CLINICAL RELEVANCE STATEMENT

Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas.

KEY POINTS

The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.

PARC: a phase I/II study evaluating the safety and activity of pegylated recombinant human arginase BCT-100 in relapsed/refractory cancers of children and young adults

Background

The survival for many children with relapsed/refractory cancers remains poor despite advances in therapies. Arginine metabolism plays a key role in the pathophysiology of a number of pediatric cancers. We report the first in child study of a recombinant human arginase, BCT-100, in children with relapsed/refractory hematological, solid or CNS cancers.

Procedure

PARC was a single arm, Phase I/II, international, open label study. BCT-100 was given intravenously over one hour at weekly intervals. The Phase I section utilized a modified 3 + 3 design where escalation/de-escalation was based on both the safety profile and the complete depletion of arginine (defined as adequate arginine depletion; AAD <8μM arginine in the blood after 4 doses of BCT-100). The Phase II section was designed to further evaluate the clinical activity of BCT-100 at the pediatric RP2D determined in the Phase I section, by recruitment of patients with pediatric cancers into 4 individual groups. A primary evaluation of response was conducted at eight weeks with patients continuing to receive treatment until disease progression or unacceptable toxicity.

Results

49 children were recruited globally. The Phase I cohort of the trial established the Recommended Phase II Dose of 1600U/kg iv weekly in children, matching that of adults. BCT-100 was very well tolerated. No responses defined as a CR, CRi or PR were seen in any cohort within the defined 8 week primary evaluation period. However a number of these relapsed/refractory patients experienced prolonged radiological SD.

Conclusion

Arginine depletion is a clinically safe and achievable strategy in children with cancer. The RP2D of BCT-100 in children with relapsed/refractory cancers is established at 1600U/kg intravenously weekly and can lead to sustained disease stability in this hard to treat population.

Clinical trial registration

EudraCT, 2017-002762-44; ISRCTN, 21727048; and ClinicalTrials.gov, NCT03455140.

Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O6-methylguanine-methyltransferase promoter methylation status

Background

It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.

Methods

GBM patients (n = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O6-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (n = 55) or PsP (n = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.

Results

The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.

Conclusions

Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.

Diagnostic accuracy of anti-3-[18F]-FACBC PET/MRI in gliomas

PURPOSE

The primary aim was to evaluate whether anti-3-[18F]FACBC PET combined with conventional MRI correlated better with histomolecular diagnosis (reference standard) than MRI alone in glioma diagnostics. The ability of anti-3-[18F]FACBC to differentiate between molecular and histopathological entities in gliomas was also evaluated.

METHODS

In this prospective study, patients with suspected primary or recurrent gliomas were recruited from two sites in Norway and examined with PET/MRI prior to surgery. Anti-3-[18F]FACBC uptake (TBRpeak) was compared to histomolecular features in 36 patients. PET results were then added to clinical MRI readings (performed by two neuroradiologists, blinded for histomolecular results and PET data) to assess the predicted tumor characteristics with and without PET.

RESULTS

Histomolecular analyses revealed two CNS WHO grade 1, nine grade 2, eight grade 3, and 17 grade 4 gliomas. All tumors were visible on MRI FLAIR. The sensitivity of contrast-enhanced MRI and anti-3-[18F]FACBC PET was 61% (95%CI [45, 77]) and 72% (95%CI [58, 87]), respectively, in the detection of gliomas. Median TBRpeak was 7.1 (range: 1.4-19.2) for PET positive tumors. All CNS WHO grade 1 pilocytic astrocytomas/gangliogliomas, grade 3 oligodendrogliomas, and grade 4 glioblastomas/astrocytomas were PET positive, while 25% of grade 2-3 astrocytomas and 56% of grade 2-3 oligodendrogliomas were PET positive. Generally, TBRpeak increased with malignancy grade for diffuse gliomas. A significant difference in PET uptake between CNS WHO grade 2 and 4 gliomas (p < 0.001) and between grade 3 and 4 gliomas (p = 0.002) was observed. Diffuse IDH wildtype gliomas had significantly higher TBRpeak compared to IDH1/2 mutated gliomas (p < 0.001). Adding anti-3-[18F]FACBC PET to MRI improved the accuracy of predicted glioma grades, types, and IDH status, and yielded 13.9 and 16.7 percentage point improvement in the overall diagnoses for both readers, respectively.

CONCLUSION

Anti-3-[18F]FACBC PET demonstrated high uptake in the majority of gliomas, especially in IDH wildtype gliomas, and improved the accuracy of preoperatively predicted glioma diagnoses.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT04111588, URL: https://clinicaltrials.gov/study/NCT04111588.