Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathologically-confirmed tissue samples. Machine learning models were developed using 254 pre-surgical multi-parametric MRI patches surrounding coordinates of tissue samples taken during recurrent surgery. A test AUROC of 0.74 ± 0.08 for distinguishing recurrent tumors, and 0.99 ± 0.01 for normal-appearing brain, demonstrated the feasibility of spatially mapping heterogeneity. Important features were consistent with current literature, and uncertainty was correlated with model failures (p ≤ 0.05). Volumetrics derived from prediction maps of recurrent tumors generated using a separate cohort of 56 patients with recurrent high-grade gliomas were significantly associated with survival. These results demonstrate a step towards clinical applicability of spatially mapping glioma recurrence.

Background The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a key predictive biomarker for chemotherapy response in glioblastoma (GBM). Current reliance on complex molecular testing necessitates the development of alternative predictive methods for postoperative decision support during the waiting period. Although both preoperative MRI and histopathological images contain valuable biological information, their combined potential for predicting MGMT status remains unexplored. We aimed to develop and validate a deep learning radiopathomics model (DLRPM) that integrates MRI and pathological images for predicting MGMT promoter methylation. Methods A retrospective collection of pathologically confirmed isocitrate dehydrogenase (IDH) wild-type GBM patients (n=207) from three centers was performed, all of whom underwent MRI scanning within 2 weeks prior to surgery. The pre-trained ResNet50 was used as the feature extractor. Features of 1024 dimensions were extracted from MRI and pathological images, respectively, and the features were screened for modeling. Then feature fusion was performed by calculating the normalized multimode MRI fusion features and pathological features, and prediction models of MGMT based on deep learning radiomics, pathomics, and radiopathomics (DLRM, DLPM, DLRPM) were constructed and applied to internal and external validation cohorts. Results In the training, internal and external validation cohorts, the DLRPM further improved the predictive performance, with a significantly better predictive performance than the DLRM and DLPM, with AUCs of 0.920 (95% CI 0.870–0.968), 0.854 (95% CI 0.702–1.000), and 0.840 (95% CI 0.625–1.000) and the corresponding accuracy was 83.2%, 82.1% and 80.0%, respectively. The AUCs of DLRM were 0.786, 0.771, and 0.600 respectively, with accuracy rates of 67.3%, 67.9%, and 60.0% respectively. The AUCs of DLPM were 0.864, 0.844, and 0.780 respectively, with accuracy rates of 79.6%, 78.6%, and 66.7% respectively. Conclusion By integrating MRI and histopathological images, the DLRPM narrows the diagnostic gap created by the waiting period for molecular testing, offering a practical approach to predicting MGMT methylation status. The model’s robust performance across validation cohorts demonstrates its potential as a practical clinical tool to supplement or reduce reliance on invasive tissue sampling, thereby aiding in personalized treatment planning for GBM patients. Future studies will focus on prospective validation and explore its utility in predicting other molecular markers and treatment outcomes.

Myostatin (MSTN), a member of the TGF-β superfamily, is recognized for its role in regulating muscle growth and cancer cachexia, but its involvement in glioma remains unclear. We examined MSTN expression and its clinical significance in glioma using public datasets and single-cell sequencing data. Immunohistochemistry and western blot further supported elevated MSTN protein levels in GBM tissues and cell lines. Knocking down MSTN in U87 and T98G cells revealed its impact on proliferation, migration, invasion, ROS accumulation, and apoptosis. Our analysis demonstrated that MSTN was significantly upregulated in glioma tissues and cell lines. Furthermore, ROC analysis indicated the diagnostic potential of MSTN in glioblastoma (GBM), lower-grade glioma (LGG), and the combined GBM-LGG cohort. High MSTN expression was associated with poorer survival in specific glioma subgroups, tumor heterogeneity, genomic instability-related features, and immune-related bioinformatic signatures. In vitro, siRNA-mediated MSTN depletion was associated with reduced glioblastoma cell proliferation, colony formation, migration, and invasion, together with increased ROS accumulation and apoptosis. Our findings suggest that MSTN upregulation in glioma could serve as a potential biomarker and might contribute to malignant phenotypes of glioma cells. Further studies are required to clarify the mechanistic role and clinical relevance of MSTN in glioma.

Glioma segmentation and prediction of isocitrate dehydrogenase (IDH) genotyping based on multi-parameter magnetic resonance imaging (MRI) are crucial for pre-operative diagnosis and treatment planning for patients. These two tasks have traditionally been considered independent problems. However, concurrently executing these interrelated tasks is more aligned with clinical practice. Our study enrolled multi-center datasets, including both in-house (324 patients) and publicly available datasets (218 patients), and standardized them using specific preprocessing methods to reduce discrepancies. We proposed a multi-task learning network named MTAF-Net, which integrates multi-attention guidance and multi-scale feature fusion. MTAF-Net mainly includes four components: (1) The encoder is equipped with hybrid spatial pooling module for extracting multi-scale features with more spatial information; (2) proposed a weighted semantic embedding module to embed deep semantic information into spatial information, emphasizing tumor structure; (3) constructed an attention-guided feature learning module to fuse multi-scale contextual shared features, addressing intra-class variability in tumor appearance and the often indistinct boundaries observed in MRI images; (4) designed a task attention modules to refine features that are relevant to each task. Experimental results indicate that: (1) multi-task model outperforms the single-task model; (2) MTAF-Net outperforms state-of-the-art single-task and multi-task learning methods in medical image processing on publicly available datasets (Glioma segmentation: Dice, 88.44%, HD95, 6.52 mm, IDH genotyping: AUC, 92.45%, ACC, 85.78%). Our study shows that integrating two interrelated medical image analysis tasks within a unified framework significantly improves their mutual performance, with validated reliability and generalizability across a broader clinical population, facilitating clinical translation. The source code is available at https://github.com/weiweixpu/MTAF-Net.

Hyperpolarized (HP) [1-13C]pyruvate MRI can noninvasively detect dynamic metabolic activity in the brain. This study utilizes HP-[1-13C]pyruvate MRI to monitor treatment-related metabolism and evaluate early therapeutic response in twenty patients with recurrent grade 4 glioma undergoing cytotoxic, antiangiogenic, and targeted chemotherapies. Metabolic changes within the T2-lesion and normal-appearing white matter were compared among patients with similar treatments and/or outcomes. The lactate/pyruvate ratio increased by 14.3% at 1 month in the group whose therapy included bevacizumab, while it decreased by 13.1% at 2 months in the normal-appearing white matter of patients on everolimus. In the T2-lesion, patients on bevacizumab showed a 24.3% increase, whereas patients on everolimus showed a 7.6% decrease in normalized lactate/pyruvate ratios. Patients with shorter 6-month progression-free survival showed an average 9.6% increase in the lactate/pyruvate ratio as early as 1 month after treatment. This study demonstrates the potential of real-time metabolic imaging for response monitoring in patients with glioma.

Alpha-ketoglutarate (aKG) is a central intermediate of cerebral energy metabolism and a precursor for glutamate synthesis in the brain. Alterations in aKG metabolism occur in pathological contexts, including isocitrate dehydrogenase (IDH) mutant astrocytomas and oligodendrogliomas, in which mutant IDH converts aKG to the oncometabolite 2-hydroxyglutarate. Given its central role in brain metabolism, non-invasive interrogation of aKG-dependent metabolic flux is needed. Hyperpolarized (HP) 13C MR enables real-time visualization of metabolic conversion by transiently enhancing signal intensity by several orders of magnitude. Leveraging this approach, we report the first-in-human feasibility and safety study of HP [1-13C]aKG MR spectroscopy in the healthy brain (n = 3). A standard operating procedure (SOP) was developed for sterile [1-13C]aKG dose production, achieving reproducible polarization levels averaging 30.5 ± 2.2%. Following intravenous administration, time-resolved 13C spectra in healthy volunteers demonstrated the detection of HP aKG resonance and a measurable downstream glutamate signal, consistent across repeat acquisitions, with a delayed temporal profile relative to aKG observed in a representative dataset. Although performed in healthy volunteers, these results establish feasibility for HP [1-13C]aKG metabolic imaging to open a new window into normal and pathological brain cellular metabolism.

Standard-of-care (SOC) radiation therapy (RT) planning utilizes a uniform, isotropic 2cm-expansion of the T1-contrast-enhancing or T2-hyperintensity lesion (CEL, T2L) to generate a clinical target volume (CTV), without considering anisotropic tumor infiltration. We hypothesize that incorporating vision-transformers into our segmentation-based deep-learning approach for generating CTVs from predictions of tumor progression using either pre-surgical or pre-RT metabolic and diffusion-weighted MRI will result in personalized CTVs that are more sensitive to detecting infiltrating tumor and spare more healthy brain compared to SOC-CTVs. Anatomical, diffusion-weighted, and metabolic MRI from 193 patients with glioblastoma (92 acquired pre-surgery, 101 post-surgery, pre-chemoradiation) were retrospectively used to predict regions of new contrast-enhancement or T2-hyperintensity at recurrence (60/30/10% train/validation/test split). Two segmentation-based predictive deep-learning approaches with personalized loss-functions and evaluation metrics that incorporate tumor size were employed and performance was compared to the SOC T2L+2cm expansion CTV. 8 tissue samples with known coordinates on the progression MRI scan taken from 4 patients in the test-set were assessed to further validate model predictions. Our deep-learning, vision-transformer-predicted CTV achieved significantly improved coverage of the progressed lesion compared to our prior U-Net model for the presurgery test-set [0.963(95%CI:0.889,0.986) vs 0.85(95%CI:0.770,0.871); p<0.001], and similar sensitivity to both the U-Net applied to pre-RT test-set (0.95 vs 0.92) and SOC-T2L+2cm CTV [0.985(95%CI:0.84,0.996)], but with a smaller mean treatment volume [361.82cm3 vs 373.91cm3]. For 3 test-set patients with research scans pre-surgery, pre-RT, and at progression allowing for direct comparison between pre-surgery and pre-RT models, 9-80% and 14-75% increases in sensitivity and personalized Progression-Coverage-Coefficients were consistently observed for pre-surgery models for all patients. All 8 tissue samples from regions of suspected recurrence were confirmed as recurrent tumor and located within the predicted CTV; 75% of which were outside the original pre-treatment CEL. This study demonstrates the potential benefit of using pre-surgical metabolic and diffusion MRI with a segmentation-based, deep-learning approach that utilizes vision-transformers for personalized RT planning.

Patients with lower-grade gliomas often face cognitive impairments, particularly in regions reliant on distributed networks and intact white matter tracts. These issues may arise from disruptions in the fronto-parietal central executive network (CEN). This study investigated the functional connectivity (FC) of the CEN in relation to executive functions of inhibition (DKEFS Stroop interference) and working memory (WAIS Working Memory Index), as well as processing speed (Oral Symbol Digit). 12 patients with lower-grade glioma (LrGG) (7 astrocytomas, 4 oligodendrogliomas, and 1 NOS) and 6 healthy controls were recruited. Resting-state fMRI (rsfMRI) was processed and analyzed using the CONN toolbox, with a focus on the CEN, utilizing seeds in the posterior parietal cortex and the lateral prefrontal cortex. Multivariable linear regression models were used to compare the relationship across groups (p <.05 with FDR correction). LrGG patients scored lower than controls in executive function (-.113 vs 0.0), working memory (-.27 vs.578), and processing speed (-1.02 vs -.04). Inhibition was associated with significantly stronger FC between all regions of the CEN and the superior frontal gyrus (SFG), anterior cingulate cortex (ACC), precuneus, cuneus, lateral occipital cortex, and cerebellum in controls than LrGG patients (p<.05). Working memory was associated with significantly stronger FC between the parietal regions of the CEN and the cerebellum in controls than LrGG patients (p<.05). In contrast, processing speed was associated with significantly weaker FC between the right prefrontal region of the CEN and the left parahippocampal gyrus and bilateral cerebellum in controls than LrGG patients (p<.05). Altered coordination between the CEN and the precuneus (a hub of the default mode network), the ACC (a hub of the salience network), and the cerebellum highlights the impacts that LrGG and related treatment can have on FC and cognitive performance.

BackgroundNon-small cell lung cancer (NSCLC) represents approximately 85% of all lung malignancies, with lung adenocarcinoma (LUAD) being the predominant histologic subtype. Epidermal growth factor receptor (EGFR) mutations serve as critical therapeutic targets in NSCLC; however, resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs) remains a major clinical challenge. Recent studies highlight the need to identify molecular drivers of resistance to improve therapeutic outcomes.MethodThis study analyzed tumor tissue datasets to investigate the role of the assembly factor for spindle microtubules (ASPM) in NSCLC progression and drug resistance. Bioinformatics methods revealed high expression of ASPM in tumor tissues and its association with low patient survival. Functional validation was performed using the EGFR-TKI-resistant cell line PC9 osimertinib-resistant (PC-9 OR), with ASPM-silenced models. Cellular proliferation, invasion, and EGFR protein stability analyses were conducted. Additionally, the therapeutic impact of ASPM silencing and overexpression combined with the third-generation TKI osimertinib was evaluated.ResultsASPM is significantly upregulated in NSCLC tumor tissues and is strongly associated with reduced patient survival. ASPM silencing attenuates PC-9 and PC-9 OR malignant phenotypes, including proliferation and invasion, and sensitizes resistant cells to osimertinib. In addition, inhibiting the expression of ASPM effectively reduces damage to the cell cycle and protein stability of drug-resistant cells, thereby restoring the expression and function of EGFR.ConclusionThis study identified ASPM as a novel regulator of EGFR-TKI resistance in NSCLC, with dual roles in promoting tumor aggressiveness and stabilizing EGFR signaling. Targeting ASPM may represent a promising therapeutic strategy to overcome EGFR-TKI resistance, enhance osimertinib efficacy, and expand treatment options for refractory NSCLC patients. These findings provide a foundation for developing ASPM-directed therapies in precision oncology.

The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1–2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.

An ongoing challenge faced in neuro-oncology is non-invasively distinguishing treatment-induced effects (TxE) following chemotherapy and/or radiation therapy from true tumor recurrence (rTumor). Previous research has explored the utility of AI-based models for this task but has overlooked within-lesion heterogeneity and the non-enhancing, T2-lesion. We investigated the value of integrating models trained using: 1) multi-parametric MRI (mpMRI) including anatomical, diffusion-weighted, and perfusion-weighted images, and 2) individual spectra from 1cc regions surrounding the tissue-sample locations, to improve the discrimination of treatment-effect from tumor recurrence. This retrospective study included 144 high-grade glioma patients who underwent MRI scans before surgical resection for suspected recurrence. Imaging included standard anatomical, diffusion-weighted, and dynamic-susceptibility-contrast perfusion-weighted MRI, and lactate-edited ¹H-MRSI. 324 spatially-localized tissue-samples were histopathologically classified as TxE or rTumor. The mpMRI model utilized 10 mm volumetric patches of each standardized image contrast (T2-FLAIR, T1-post-contrast, peak height and %-recovery from perfusion, ADC and FA from diffusion) centered on the tissue sample coordinates and generated 20 ensemble predictions. The AI-based spectra model predicted Ki-67, cellularity, and a composite tumor aggressiveness index from the entire 1D-spectrum reconstructed at the location of the tissue-sample. These predictions were concatenated into 4 machine-learning classifiers, which were trained on the combined feature set and on individual imaging and spectral features to assess model contributions. Balancing the dataset enhanced the performance of all models, most notably that of gradient boosting (AU-ROC: 0.655 to 0.724). The radiopathomic spectra model increased performance of the weighted ensemble by 4% to 0.73+/-0.04 AU-ROC when integrated with our previously-developed mpMRI model. Three imaging features consistently ranked among the top five predictors across classifiers. Integrating radiopathomic-derived features from an AI-based model using the entire spectrum with mpMRI features preserved diagnostic performance across all models, with a maximum improvement of 5.26% in AU-ROC. Current work is evaluating different strategies for combining models for contrast-enhancing and non-enhancing samples separately.

Isocitrate dehydrogenase (IDH)-mutant gliomas demonstrate metabolic reprogramming and remain challenging to assess using conventional MRI alone. 1H MR spectroscopic imaging (MRSI) of steady-state metabolism and real-time hyperpolarized carbon-13 (HP-13C) MRI of dynamic metabolism are of interest for evaluating response to treatment. This study aimed to improve characterization of IDH-mutant glioma through multimodal analysis of 1H/HP-13C MRI and histopathology in patients before surgery for suspected tumor progression. Six IDH-mutant glioma patients (1F/5M, 46.2±7.6 years; 5 astrocytoma [2 grade 2, 1 grade 3, 2 grade 4], 1 grade 3 oligodendroglioma) received multiparametric 1H/HP-13C MR examinations on a 3-Tesla MR scanner before surgery. All patients had confirmed tumor progression; one grade 2 underwent malignant transformation to grade 3. During surgery, 21 tissue samples were collected with imaging coordinates and analyzed for histopathology. Imaging parameters were reprocessed to center on recorded biopsy locations to provide normalized apparent diffusion coefficients (nADC)/relative cerebral blood volume (nCBV)/relative cerebral blood flow (nCBF)/peak height (nPH), %recovery, choline-to-N-acetylaspartate index (CNI), pyruvate-to-lactate conversion rate (kPL), ratios of lactate-to-pyruvate (Lac/Pyr) and bicarbonate-to-pyruvate (Bic/Pyr). Tissue samples were assessed for treatment effects, blood vessels (BV), carbonic anhydrase 9 (CA9), and Ki-67 expressions. Due to limited sample sizes, summary statistics and trends were assessed. 3 tissue samples with pure treatment effects showed higher Bic/Pyr ratio (median [min max]: 0.05 [0.04, 0.15] vs. 0.03 [0.005, 0.31]) and lower CNI (0.20 [-0.24, 0.64] vs. 8.01 [0.58, 14.88]) compared to 15 recurrent tumor tissue samples. In recurrent tumor tissue samples, higher tumor proliferation (Ki67) was associated with higher kPL, CNI, nPH, nCBV, nCBF, and nADC, while no clear association was observed between vascularity (BV) or hypoxia (CA9) indexes and imaging parameters. This study showcases our initial experience in using multiparametric 1H/HP-13C MR imaging to characterize recurrent IDH-mutant gliomas and identify noninvasive imaging markers corresponding with tissue histopathology.

PURPOSE:This study aimed to develop DeepGBM-Recure, an integrated artificial intelligence (AI) system for optimizing precision radiotherapy and individualized surveillance in glioblastoma (GBM) by automating postoperative risk stratification and spatial targeting of recurrence hotspots. METHODS:This DeepGBM-Recure system comprises three synergistic modules: 1) Automated segmentation of peri-cavitary hyperintense regions on postoperative fluid-attenuated inversion recovery (FLAIR) images using a 3D nnU-Net framework; 2) Patient-level early recurrence prediction based on radiomics features and random forest classification; 3) Voxel-wise spatial mapping of high-risk subregions via supervoxel analysis. The system was trained and validated on data from 145 patients across two centers and externally tested on data from 39 patients across another two centers. RESULTS:On the test set, the nnU-Net segmentation model achieved a mean Dice coefficient of 0.85 ± 0.09. The patient-level and voxel-level prediction models achieved area under the ROC curves (AUCs) of 0.76 and 0.80, respectively. Notably, the voxel-level model exhibited strong spatial concordance between predicted high-risk heatmaps and ground-truth recurrence regions. Performance was further supported by calibration curves, decision curve analysis, and clinical application in representative cases, demonstrating favorable predictive accuracy in real-world scenarios. CONCLUSION:DeepGBM-Recure represents a pioneering integrated solution that combines automated anatomical delineation, individualized risk stratification, and spatial recurrence guidance, offering a clinically applicable tool for precision radiotherapy and individualized surveillance. Prospective multi-center trials with larger cohorts are warranted to validate clinical utility and facilitate integration into real-world decision-making workflows.

Non-invasive molecular imaging methods capable of assessing tumor biology in-vivo were investigated to improve the clinical management of patients with glioma. In this study, we developed two hyperpolarized (HP) 13C-MRI techniques for clinical translation: one to evaluate blood-brain barrier (BBB) integrity and another to assess isocitrate dehydrogenase (IDH) mutation status in glioma. To evaluate BBB disruption, HP [13C,15N2]urea probe was developed, exploiting urea’s small molecular weight—approximately 15 times smaller than gadolinium-contrast agents—and its inability to cross an intact BBB. A dynamic 3D balanced SSFP acquisition was performed in healthy volunteers following intravenous injection of HP urea solution, enabling high-SNR visualization of arterial, capillary, and venous compartments. Quantitative analysis of vascular transit and spatial distribution established normative references for evaluating BBB integrity. HP 13C-urea MRI may offer superior sensitivity compared to current gadolinium-based methods by directly detecting subtle BBB disruptions as a positive-contrast signal within brain parenchyma, without requiring gadolinium administration, particularly in non-enhancing brain tumors. To evaluate the IDH mutation status of glioma, we developed HP [1-13C]alpha-ketoglutarate (aKG) MRI to monitor the metabolic reprogramming specific to this type of tumor, which involves the conversion of aKG to the oncometabolite 2-hydroxyglutarate (2HG). A dynamic 13C MRS utilizing a spectral-spatial RF pulse to independently excite aKG and its downstream metabolites was acquired following intravenous injection of HP aKG solution. Initial studies with HP [1-13C]aKG in healthy volunteers demonstrated safety and feasibility, showing glutamate production consistent with normal IDH activity. Subsequent studies in patients with IDH-mutant glioma revealed signals consistent with 2HG, suggesting feasibility for directly assessing mutant IDH activity in-vivo. Further validation is underway. Together, these developments highlight the significant clinical-research potential of HP 13C MRI for probing tumor vasculature and IDH-driven metabolism, offering complementary non-invasive biomarkers for improved diagnosis, treatment monitoring, and therapeutic stratification in glioma.

Gliomas are the most prevalent malignant primary brain tumors in adults. While MR imaging is frequently used to monitor patients with glioma, the structural images often fail to define tumor boundaries precisely. Non-invasive metabolic imaging techniques are useful because they reveal the biochemical characteristics of the tumor and surrounding tissues, aiding in identifying the most active tumor regions. This paper presents findings from our laboratory and others on the integration of proton MRI, proton MR spectroscopic imaging (MRSI), and hyperpolarized carbon-13 MRI. These advanced methods provide valuable insights into brain metabolism—both steady-state and dynamic—across different regions, helping to better assess patients with glioma.

Hyperpolarized carbon-13 (HP-13C) MRI enables the real-time measurement of dynamic metabolism by utilizing molecular probes whose magnetization has been transiently enhanced via dynamic nuclear polarization of 13C labels. Based on preclinical and clinical investigations demonstrating Warburg-related metabolic dysfunction and tricarboxylic acid (TCA)-cycle alterations in gliomas, HP-13C techniques appear very promising for overcoming conventional challenges to evaluating tumor burden and extent, early therapeutic response, and progression among patients noninvasively. This article surveys the multifaceted translational development of HP-13C MRI in the context of glioma imaging, while emphasizing innovation concerning the pharmacy production of hyperpolarized (HP) probes-[1-13C]/[2-13C]-pyruvate and [1-13C,5-12C]-α-ketoglutarate-that serve as nonradioactive metabolic contrast agents. Borrowing from practical experience, we present specific probe indications for isocitrate dehydrogenase (IDH)-wild-type glioblastomas and IDH-mutant gliomas together with example data to show the targeted, pathway-dependent function of these agents and their utility. Additional information pertaining to HP-13C hardware, acquisition, and postprocessing techniques provides an overview of the imaging methodology as it is currently performed at a leading institution. Considering the developing markers for progressive disease in glioblastomas and rapidly advancing capability, this unique imaging technology appears poised for translational impact following further evaluation.

This study aimed to implement a multimodal 1H/HP-13C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP-13C metabolic data. A total of 100 1H/HP [1-13C]-pyruvate MR examinations (104 HP-13C datasets) were acquired from 42 patients according to the comprehensive multimodal glioma imaging protocol. Serial data coverage, accuracy of frequency reference, and acquisition delay were evaluated using a mixed-effects model to account for multiple exams per patient. Serial atlas-based HP-13C MRI demonstrated consistency in volumetric coverage measured by inter-exam dice coefficients (0.977 ± 0.008, mean ± SD; four patients/11 exams). The atlas-derived prescription provided significantly improved data quality compared to manually prescribed acquisitions (n = 26/78; p = 0.04). The water-based method for referencing [1-13C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [13C]-urea phantom (4.1 ± 3.7 Hz vs. 9.9 ± 10.7 Hz; p = 0.0007). Significantly improved capture of tracer inflow was achieved with the 2-s versus 5-s HP-13C MRI acquisition delay (p = 0.007). This study demonstrated the implementation of a comprehensive multimodal 1H/HP-13C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.