Evaluation of treatment response in patients with recurrent grade 4 glioma using hyperpolarized [1- 13C]pyruvate MRI

Evaluation of treatment response in patients with recurrent grade 4 glioma using hyperpolarized [1- 13C]pyruvate MRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evaluation of treatment response in patients with recurrent grade 4 glioma using hyperpolarized [1- 13 C]pyruvate MRI Sana Vaziri, Janine M Lupo, Adam W Autry, Marisa LaFontaine, Javier Villanueva-Meyer, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9238794/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Hyperpolarized (HP) [1- 13 C]pyruvate MRI can noninvasively detect dynamic metabolic activity in the brain. This study utilizes HP-[1- 13 C]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. Biological sciences/Cancer Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Health sciences/Oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gliomas are the most common and malignant primary brain tumors in adults, with glioblastoma (GBM) being the most aggressive and prevalent form 1 . The latest WHO 2021 classification for grade 4 glioma now includes both isocitrate dehydrogenase (IDH)-mutant, grade 4 astrocytoma, and IDH-wildtype GBM categories 2 . For these grade 4 gliomas, standard-of-care treatment includes maximal surgical resection of the tumor followed by fractional radiation therapy (RT) and temozolomide (TMZ), a DNA alkylating agent 3 . Because patients eventually experience recurrence, a variety of second-line therapies are often integrated into the clinical management of recurrent disease as single or combined chemotherapeutic regimens. These therapies often include: (i) Lomustine (also known as CCNU, chloroethyl-cyclohexyl-nitrosourea), which is a standard second-line treatment after TMZ 4 ; (ii) Everolimus, an mTORC1 inhibitor that decreases downstream protein synthesis and cell growth by blocking the mTOR pathway; or (iii) bevacizumab, an anti-angiogenic monoclonal antibody that inhibits the blood vessel formation. Despite these treatments, survival rates remain low, with fewer than 6% of patients living beyond five years after diagnosis, and a median overall survival of approximately 14–16 months 5 – 7 . In response to the limited efficacy of existing treatments, new therapeutic strategies are actively being developed to combat high-grade disease. Standard clinical practice for planning treatment and response assessment for patients with glioma relies entirely on anatomical MRI-visible contrast-enhancing lesions (CEL) and/or T2 hyperintensity lesions (T2L) 8 , despite the fact that these features solely indicate structural changes caused by the breakdown of the blood-brain barrier or presence of fluid, respectively. Interpreting images, especially after therapy, can be challenging due to tumor heterogeneity and treatment-induced changes, highlighting the need for advanced MR techniques to inform therapy and clinical decisions. The known metabolic reprogramming—specifically, Warburg metabolic upregulation of glycolysis over oxidative phosphorylation 9 —suggests that incorporating metabolic imaging that can target this pathway could be useful for evaluating these patients. Hyperpolarized carbon-13 (HP- 13 C) metabolic MR imaging provides a unique way to noninvasively and dynamically monitor metabolic pathways on a rapid timescale after the administration of the non-toxic, nonradioactive probe 10 . HP [1- 13 C]pyruvate is the most widely used 13 C probe in human brain research, capable of detecting the Warburg effect in tumor 11 . Previous studies in high-grade gliomas demonstrated that HP [1- 13 C]pyruvate MRI can image, in vivo , the increased conversion of [1- 13 C]pyruvate to [1- 13 C]lactate compared to 13 C-bicarbonate within tumors 12 , 13 , and the ratio of those [1- 13 C]pyruvate to [1- 13 C]lactate was higher in recurrent tumors compared to treatment effects 13 . Interestingly, an increase in k PL , the conversion rate from pyruvate to lactate, was also detected in normal-appearing white matter (NAWM) using HP [1- 13 C]pyruvate MRI 14 shortly after receiving antiangiogenic therapy, demonstrating its potential to identify early treatment-induced changes before they become visible on standard MRI. The goal of this study was to characterize dynamic metabolic changes observed in both recurrent grade 4 lesions and NAWM in relation to treatment response during and after therapy. This involved monitoring patients with recurrent grade 4 glioma —either participating in a clinical trial (ID: NCT03681028, started on 12/19/2018) or seen in clinic— before and during chemotherapy for recurrent disease, using serial HP [1- 13 C]pyruvate MRI. We hypothesized that HP [1- 13 C]pyruvate MRI would detect early metabolic changes associated with response that preceded changes on standard MRI in both the lesion and, in cases where patients received antiangiogenic therapy, NAWM. Methods Patient Population A total of 20 patients with recurrent grade 4 gliomas were enrolled in this IRB-approved study and provided informed consent. Tumor recurrence was confirmed through repeat surgery (19/20) or by the RANO criteria 8 (1/20). Patients were then treated with different types of chemotherapy and monitored with serial MR exams that included both clinical 1 H MRI and HP [1- 13 C]pyruvate MRI (N = 41). Of these patients, 12 had a baseline scan prior to receiving treatment for recurrence. When feasible, follow-up imaging was performed at approximately bimonthly intervals. The number and timing of follow-up scans varied across patients due to clinical scheduling constraints and external factors, including disruptions related to the COVID-19 pandemic. Of the 20 participants, 11 experienced disease progression (confirmed by subsequent surgery (N = 10) or RANO 2.0 criteria (N = 1) 8 ) within 6 months of their baseline scan. Clinical characteristics and treatment regimens for each patient are summarized in Table 1 . Patients were divided into treatment-based groups. The first group, BEV+ , consisted of patients who received bevacizumab as part of their treatment. The second group, EVER+ , included those treated with everolimus in combination with other agents. The remaining patients who received multi-agent therapy with combinations of alkylating agents such as temozolomide (TMZ) or CCNU, along with other agents, like olaparib, abemaciclib, and afatinib, were grouped together into the ALKYL+ group. Additionally, two patients served as controls: P19 was treated exclusively with TMZ, and P20 was treated only with CCNU. For the purpose of lesion analysis, patients were categorized according to their survival outcomes as either early progressors with a progression-free survival (PFS) less than 6 months, or late progressors with a PFS that exceeded 6 months. Table 1 Clinical summary of the patient population, including treatment regimens and scan intervals. No Age Sex Dx Treatment PFS6 OS (yr) Baseline Acquired Post-Tx F1 Scan 1 29 F rAS4 Bevacizumab + [RT, TMZ] 1 0.7 No 2m, 3m, 5m 2 39 M rAS4 Bevacizumab 0 5.0 No 1m 3 55 M rGBM Bevacizumab + [CCNU, Afatinib, Abemaciclib] 1 0.8 Yes 4m 4 63 M rGBM Bevacizumab + CCNU 0 0.9 Yes 1m, 3m 5 56 F rGBM Bevacizumab + [Carboplatin, Trametinib] 1 0.3 Yes 1m 6 55 M rGBM Everolimus + [CCNU, Trametinib, Abeciclib] 1 0.7 Yes 4m 7 62 M rGBM Everolimus + [TMZ, Olaparib] 1 0.8 No 2m 8 45 M rGBM Everolimus + [TMZ, Afatinib] 1 0.9 Yes 2m 9 68 M rGBM Everolimus + [CCNU, Dasatinib] 1 0.6 No 2m, 4m 10 33 M rAS4 TMZ + [Olaparib, Abemaciclib] 0 1.4 No 2m, 3m 11 69 F rGBM CCNU + [Afatinib, Abemaciclib] 0 1.1 Yes 2m, 4m, 5m 12 57 M rGBM TMZ + [Afatinib, Olaparib] 0 3.7 No 2m, 4m 13 58 F rGBM CCNU + [Afatanib, Abecliclab] 0 0.4 Yes 2m 14 40 F rGBM TMZ + [Olaparib, Abemaciclib] 0 3.0 No 2m 15 57 M rGBM TMZ + [Propranolol,Trametinib,Abemaciclib] 1 1.1 Yes 4m 16 55 M rGBM CCNU + Afatinib 1 0.6 Yes 2m 17 55 F rGBM TMZ + [Afatinib, Olaparib] 0 1.6 Yes 4m 18 40 M rGBM CCNU + [Afatinib, Abemaciclib] 1 2.0 No 2m 19 59 F rGBM TMZ 0 1.1 Yes 1m, 3m 20 50 M rGBM CCNU 1 0.3 Yes 1m rGBM: recurrent GBM; rAS4: recurrent grade 4 astrocytoma MR Data Acquisition All MR studies were performed using a GE 3T MR750 scanner with a dual-tuned 24-channel 13 C/8-channel 1 H head array (Rapid Biomedical, Germany). The standard-of-care 1 H MR clinical protocol included anatomical 3D pre and post-gadolinium (Gd) T1 IRSPGR (TR/TE/TI = 8/3/400 ms, 1.5 mm slices, 15 o flip angle, 25.6cm x 25.6cm in-plane FOV, 256 × 256 matrix) and T2 weighted FLAIR (TR/TE/TI = 6250/138/1702 ms, 1.5mm slices, 25.6cm x 25.6cm in-plane FOV, 256 × 256 matrix) images. The HP [1- 13 C]pyruvate samples were prepared using the clinical SPINlab system (General Electric, Niskayuna, NY). After passing the quality control, the solution was injected intravenously in patients (0.43 mL/kg at 5 mL/s) 14 , and then dynamic HP- 13 C data were acquired using a frequency-selective echo-planar imaging (EPI) sequence that independently excited each metabolite (TR/TE = 62.5ms/21.7ms, 8 slice, 20 dynamic timepoints, temporal resolution = 3-s, spatial resolution 1.5-cm isotropic with a 16 x 16 matrix size, flip angles constant 𝛼 pyruvate /𝛼 lactate /𝛼 bicarbonate = 20/30/30) 15 , except for one dataset, which was obtained with 2.0 cm isotropic spatial resolution. For 3 patients (P16, P19, and P20), HP- 13 C data were obtained from the same volume of interest using a template registered from the standard MNI152 space to the subject's space on the scan day via T1 images 16 . This approach enabled a direct voxel-wise comparison of HP- 13 C parameters across different time points. The other data acquisitions were prescribed axially. Dynamic HP [1- 13 C]pyruvate MRI Processing Raw dynamic EPI data were pre-whitened, channel-combined, and denoised as described previously 17 – 20 . The resulting dynamic metabolite maps for [1- 13 C]pyruvate, [1- 13 C]lactate, and [1- 13 C]bicarbonate were temporally summed to generate area-under-the-dynamic curve (AUC) metabolite maps. Noise standard deviations for dynamic metabolite images were estimated from the background using the brain mask generated from applying FSL’s Brain Extraction Tool 21 on the T1 post-Gd image. Voxel-wise apparent signal-to-noise ratio (aSNR) maps were calculated for each metabolite using the denoised AUC images. SNR masks were created to filter out voxels with aSNR less than 5. After SNR masking of individual metabolites, ratio maps for lactate-to-pyruvate (Lac/Pyr), bicarbonate-to-pyruvate (Bic/Pyr), and bicarbonate-to-lactate (Bic/Lac) were derived from AUC maps. To facilitate comparison of metabolite signals across subjects and time points, voxel-wise raw AUC maps were transformed into percentile rank maps restricted to brain voxels. For each percentile map (Pyr%, Lac%, and Bic%), AUC values from brain-masked voxels were ranked using a tied ranking procedure that accounted for identical values, then normalized by the total number of brain voxels (without SNR filtering), and scaled to a 0–100 percentile range 13 . The resulting percentile rank maps will be referred to as Pyr%, Lac%, and Bic%. Voxel-wise apparent rate constants for pyruvate-to-lactate conversion ( k PL ) and pyruvate-to-bicarbonate conversion k PB were quantified from dynamic metabolite data using an inputless kinetic model 14 , 22 . Modeling error masks for k PL and k PB maps were created by filtering voxels with modeling error greater than 30% and 50% of the estimated value, respectively. Standard H MRI Image Processing & Region Definition Automated gray and white matter segmentation was performed on the T1 pre-Gd images 23 , 24 . White matter masks were refined by first aligning the T1 pre-Gd images using a non-linear registration 25 to the AAL3 atlas 26 and then removing the cortex. The T2L and CEL were semi-automatically segmented from the 3D FLAIR and T1 post-Gd images, respectively, using 3D Slicer 27 . The non-enhancing lesion (NEL) was then defined as the T2L minus the CEL. Similarly, NAWM was defined by subtracting any of the T2L that overlapped with the automatically-segmented white matter mask. Regions of interest (ROIs) defined at 1 H image resolution were then mapped to the lower-resolution 13 C matrix using a mutually exclusive labeling scheme: 13 C voxels containing at least 30% of the 1 H T2L were classified as 13 C T2L. Of the remaining 13 C voxels outside the T2L, those with at least 50% overlap with ¹H NAWM were classified as 13 C NAWM voxels. Subsequent lesion analyses at 13 C resolution focused solely on T2L voxels, as the CEL volumes were relatively small for reliable serial comparison at the 13 C spatial resolution in this study population. Data Analysis Lesion volumes (CEL and NEL) were measured at the native 1 H resolution for each patient scan. Percentile rank parameters (%Pyr, %Lac, %Bic) were summarized 13 within each ROI (NAWM and T2L, calculated at 13 C resolution voxels) by computing the median value across all voxels within the region for each patient scan. After applying SNR masks and modeling error masks, metabolite ratios and conversion rates were summarized in a similar manner. For lesion analyses, T2L values were then normalized by the corresponding patient-specific median NAWM value for each parameter. Group-level statistics were then calculated by taking the mean and standard deviation of these patient-wise median (and NAWM-normalized T2L) values across subjects within each group at each time point. Results Changes in Lesion Volume Changes in lesion volume per patient after treatment are shown in Fig. 1 a. Since most patients (19/20) had undergone surgical resection before treatment, the CEL volume was relatively small at baseline (median = 5.4 cm 3 , [0.0-17.6], N = 1 had no CEL), while the NEL volumes varied among patients (median = 39.7 cm 3 , [3.53–112.6]). Group averages by treatment category are shown in Fig. 1 b. Compared to the pre-treatment baseline, changes in CEL and NEL volumes varied across treatment groups. Group averages by survival status (early vs late progressors) are presented in Fig. 1 c. Despite differences among treatment groups, patients with longer PFS (PFS > 6 months) generally exhibit slower early post-treatment growth, especially in the CEL. Early Treatment Effects of Metabolism in the NAWM Longitudinal comparison of metabolite ratios and conversion rates in the NAWM revealed distinct differences among patient groups, as shown in Fig. 2 . Early changes over time in HP-¹³C metrics within the NAWM suggest increased relative lactate-to-pyruvate metabolism at the first follow-up for the BEV+ , which showed an initial increase in lactate/pyruvate (average increase from baseline of 14.3%, at 1 month) and k PL (average increase from baseline of 56.2% increase at 1 month) that then returned to normal by 2–5 months. In contrast, the EVER+ group demonstrated a larger early decrease in lactate/pyruvate at 2 months (average decrease of 13.1%) and in k PL at 2 months (average decrease of 22.2%), followed by partial recovery. These trends highlight distinct temporal patterns of HP-¹³C metabolism among the treatment groups, with BEV+ showing early transient increases and EVER+ presenting early decreases that partially normalize later. Voxel-wise Longitudinal Analysis of Representative Case Studies Two patients, with data collected using the same templates 16 , were compared directly on a voxel-by-voxel basis: the first representing an early progressor ( P16 ) and the second representing a late progressor ( P19 ), as shown in Table 1 . Representative slices for each patient at baseline and early post-treatment time points are shown in Figs. 3 and 4 . Boxplots and voxel-wise comparisons within the T2L at follow-up time points, relative to the pre-treatment baseline, are shown in Fig. 5 . In both patients, voxel-wise changes in Lac%, Pyr%, Bic%, and k PL within the T2L, shown as voxels connected by lines, indicate varied treatment responses across tumors, while the changes in median values (boxplots) between the two time points remain small. Within voxels classified as within the T2L at both timepoints (Fig. 4 , dashed and solid lines), P16 experienced an average of 9.4% decrease in Pyr%, a 7.7% decrease in Lac%, a 38.7% decrease in Bic% (n = 24 voxels for all metabolites), and a 10.1% decrease in k PL (n = 17 voxels). Comparatively, P19, who experienced a longer PFS (9 months vs. 3 months), showed only a 2.61% decrease in Pyr%, a 14.72% increase in Lac%, a 12.94% increase in Bic%, and a 8.75% decrease in k PL after 2 months of treatment (n = 4 voxels). Early Treatment Effects of Metabolism in the Lesion Figure 6 displays longitudinal changes in the normalized Pyr%, Lac%, Bic%, metabolite ratios, and conversion rates within the T2L voxels for each group, using patient-wise normalization based on the median values in the NAWM, given the treatment-induced changes illustrated in Fig. 2 . Within the lesion, the initial change patterns are similar to those in the NAWM, with the BEV+ increasing on Lac/Pyr (24.3% from baseline at 1 month) and the EVER+ decreasing in Lac/Pyr (average of 7.6% from baseline at 2 months) after NAWM normalization. Figure 7 presents the same analyses after grouping patients by survival status. Among patients with early progression (PFS < 6 months), increases in Lac% (average of 148.8% increase from baseline), Lac/Pyr (average of 9.6% increase from baseline) and k PL (average of 6.4% increase from baseline), and reductions in the Bic/Lac (average of 57.4% decrease from baseline) were observed as early as the 1-month follow-up. Although these values are averages across patients and driven by a single patient with T2L voxels at the 1-month timepoint, the trend toward elevated lactate production persists at 2 months following treatment. This pattern indicates a general treatment-related change in the distribution of pyruvate and lactate among early progressors. In contrast, patients without early progression demonstrate more modest longitudinal changes in Lac%, Lac/Pyr, and k PL within T2L, indicating relatively stable metabolic profiles over time. Discussion This study is the first to use serial HP [1- 13 C]pyruvate MRI to examine treatment response in patients with grade 4 glioma. The patients included in this study participated in both clinical trial and standard-of-care treatments, representing typical clinical practice. Treatment effects within the NAWM, depending on the type of therapy, have been observed as changes in metabolism. Additionally, early metabolite differences were identified in patients with early disease progression, offering valuable initial insights into the use of real-time metabolic imaging to evaluate treatment response in glioma. Because of the heterogeneous treatment regimens used at the time of recurrence and prior work revealing metabolic changes on HP [1- 13 C]pyruvate MRI due to angiogenic 14 and mTOR inhibitors 28 , subjects were grouped accordingly to facilitate analysis. Our analysis revealed treatment-dependent differences, most notably in the BEV+ group. Patients treated with bevacizumab showed increases in Lac/Pyr and k PL within the NAWM and T2L during early follow-up, as shown in Figs. 2 and 6 . Changes in glucose and pyruvate metabolism were reported after anti-angiogenic therapy 29 , with greater uptake of labeled glucose and higher levels of labeled lactate in both tumors and the contralateral brain. Additionally, the antiangiogenic therapy induces hypoxia, leading to reduced perfusion 30 , as reflected by the decrease in %Pyr in T2L, which is consistent with prior research 14 . In the EVER+ group, where mTOR inhibitors may affect brain metabolism by targeting core cellular functions, a shift from anabolic growth to catabolic maintenance occurs 31 – 33 . This is demonstrated by a reduction in lactate-to-pyruvate conversion in both NAWM and T2 lesions at the early time point. Although no statistically significant changes were observed in the normal brains of rats treated with Everolimus, the Lac/Pyr ratios in the tumors showed an early increase after normalization 28 , similar to the results shown in Fig. 6 . While longitudinal changes were observed in the ALKYL+ , these effects were less pronounced. It is important to note that the relatively lower spatial resolution of HP- 13 C imaging may lead to partial volume effects, which can hinder the interpretation of results. Additionally, lesion size and location varied across patients, which complicated direct comparisons between subjects. By applying the prescription template in atlas space and then transforming it to the individual subject space 16 , we were able to achieve voxel-wise correspondence across time points for two patients. Such voxel-wise longitudinal comparisons in patients revealed significant spatial changes in metabolism at each time point between baseline and post-treatment follow-up scans. We particularly noted differences in parameter distributions between patients across T2L voxels. This underscores the diverse metabolic profiles both within tumors and among individuals and illustrates the difficulty of directly comparing HP- 13 C parameters across different subjects. Patient-specific ranges of HP-¹³C metabolite parameters motivated the normalization of metabolite metrics relative to an internal NAWM reference before cross-patient longitudinal analysis of lesions. After this normalization, clearer patterns emerged when patients were grouped by PFS. Differences between early and late progressors became more evident at early post-treatment time points, as soon as 1–2 months, indicating that normalization improves sensitivity to progression-related metabolic changes, as shown in Fig. 7 . These findings underscore the importance of patient-specific scaling in longitudinal and cross-patient analyses of HP-¹³C metabolic imaging data. More importantly, similar to preclinical GBM models, where a decrease in lactate/pyruvate within 7–9 days after treatment with TMZ or mTOR inhibitors served as an early biomarker of treatment response 28 , 34 , 35 , we observed a pattern of an early increase in lactate/pyruvate and a decrease in the bicarbonate/lactate ratio after treatment in patients with shorter PFS. These findings provide initial insights into the early assessment of treatment response in high-grade glioma using HP [1- 13 C]pyruvate MRI. Monitoring metabolic changes in recurrent lesions from pretreatment to follow-up scans can indicate therapy effectiveness, guide treatment strategies, and influence clinical decisions. Despite promising results from this initial longitudinal study using HP [1- 13 C]pyruvate MRI to evaluate treatment response, several limitations must be recognized. Although the cohort size is sufficient, variability in treatments restricted the depth of statistical analysis. Additionally, the low resolution of HP- 13 C imaging and the small size of post-surgical lesions led to fewer patients with follow-up data and usable voxels. Partial-volume effects, caused by the spatial constraints of HP-¹³C imaging, further hinder interpretation in small or heterogeneous lesions. Future research should aim to collect longitudinal data with higher temporal resolution to more effectively monitor response to treatment and include larger homogeneous cohorts to facilitate patient-specific intervention analysis. Declarations Author Contributions: J.M.L., S.M.C., J.L.C., and Y.L. designed the study. D.B.V. helped with imaging methods. S.V., A.W.A., and M.L. collected and processed imaging data. J.V.M., N.A.O.B., J.L.C., and S.M.C. analyzed the clinical data. S.V. conducted data analysis. S.V., J.M.L., and Y.L. interpreted the data and were main contributors to writing the manuscript. All authors reviewed, read, and approved the final version. Acknowledgements: This study was supported by NIH P01CA118816, NIH P50CA097257, and the Glioblastoma Precision Medicine Program, with technical assistance from P41 EB013598. Ethics Declarations The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of California, San Francisco (IRB 15-16749). Competing Interests: All authors declare no financial or non-financial competing interests. Data Availability: The processing scripts and example data will be made available by the authors. Further inquiries can be directed to the corresponding author. References Ostrom, Q.T. et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro Oncol 24 , v1-v95 (2022). Louis, D.N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23 , 1231-1251 (2021). Singh, N., Miner, A., Hennis, L. & Mittal, S. Mechanisms of temozolomide resistance in glioblastoma - a comprehensive review. Cancer Drug Resist 4 , 17-43 (2021). Weller, M. & Le Rhun, E. How did lomustine become standard of care in recurrent glioblastoma? Cancer Treat Rev 87 , 102029 (2020). Gilbert, M.R. et al. Dose-dense temozolomide for newly diagnosed glioblastoma: a randomized phase III clinical trial. J Clin Oncol 31 , 4085-4091 (2013). Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352 , 987-996 (2005). Tan, A.C. et al. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin 70 , 299-312 (2020). Wen, P.Y. et al. RANO 2.0: Update to the Response Assessment in Neuro-Oncology Criteria for High- and Low-Grade Gliomas in Adults. J Clin Oncol 41 , 5187-5199 (2023). Warburg, O. On respiratory impairment in cancer cells. Science 124 , 269-270 (1956). Li, Y., Vigneron, D.B. & Xu, D. Current human brain applications and challenges of dynamic hyperpolarized carbon-13 labeled pyruvate MR metabolic imaging. Eur J Nucl Med Mol Imaging 48 , 4225-4235 (2021). Autry, A.W. et al. Clinical Translation of Hyperpolarized (13)C Metabolic Probes for Glioma Imaging. AJNR Am J Neuroradiol 46 , 1754-1764 (2025). Zaccagna, F. et al. Imaging Glioblastoma Metabolism by Using Hyperpolarized [1-(13)C]Pyruvate Demonstrates Heterogeneity in Lactate Labeling: A Proof of Principle Study. Radiol Imaging Cancer 4 , e210076 (2022). Autry, A.W. et al. Multi-parametric hyperpolarized (13)C/(1)H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas. Neuroimage Clin 39 , 103501 (2023). Autry, A.W. et al. Characterization of serial hyperpolarized (13)C metabolic imaging in patients with glioma. Neuroimage Clin 27 , 102323 (2020). Gordon, J.W. et al. Translation of Carbon-13 EPI for hyperpolarized MR molecular imaging of prostate and brain cancer patients. Magn Reson Med 81 , 2702-2709 (2019). Autry, A.W. et al. Advanced Hyperpolarized (13)C Metabolic Imaging Protocol for Patients with Gliomas: A Comprehensive Multimodal MRI Approach. Cancers (Basel) 16 (2024). Pruessmann, K.P., Weiger, M., Bornert, P. & Boesiger, P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Med 46 , 638-651 (2001). Wang, J., Wright, A.J., Hesketh, R.L., Hu, D.E. & Brindle, K.M. A referenceless Nyquist ghost correction workflow for echo planar imaging of hyperpolarized [1-(13) C]pyruvate and [1-(13) C]lactate. NMR Biomed 31 (2018). Zhu, Z. et al. Coil combination methods for multi-channel hyperpolarized (13)C imaging data from human studies. J Magn Reson 301 , 73-79 (2019). Vaziri, S. et al. Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-(13)C]pyruvate MRI data from patients with glioma. Neuroimage Clin 36 , 103155 (2022). Smith, S.M. Fast robust automated brain extraction. Hum Brain Mapp 17 , 143-155 (2002). Larson, P.E.Z. et al. Investigation of analysis methods for hyperpolarized 13C-pyruvate metabolic MRI in prostate cancer patients. NMR Biomed 31 , e3997 (2018). Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20 , 45-57 (2001). Li, Y. et al. Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme. Neuro Oncol 13 , 546-557 (2011). Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W. & Smith, S.M. Fsl. Neuroimage 62 , 782-790 (2012). Rolls, E.T., Huang, C.C., Lin, C.P., Feng, J. & Joliot, M. Automated anatomical labelling atlas 3. Neuroimage 206 , 116189 (2020). Kikinis, R. & Pieper, S. 3D Slicer as a tool for interactive brain tumor segmentation. Annu Int Conf IEEE Eng Med Biol Soc 2011 , 6982-6984 (2011). Chaumeil, M.M. et al. Hyperpolarized 13C MR spectroscopic imaging can be used to monitor Everolimus treatment in vivo in an orthotopic rodent model of glioblastoma. Neuroimage 59 , 193-201 (2012). Fack, F. et al. Bevacizumab treatment induces metabolic adaptation toward anaerobic metabolism in glioblastomas. Acta Neuropathol 129 , 115-131 (2015). Keunen, O. et al. Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma. Proc Natl Acad Sci U S A 108 , 3749-3754 (2011). Venkatesh, H.S. et al. Reduced phosphocholine and hyperpolarized lactate provide magnetic resonance biomarkers of PI3K/Akt/mTOR inhibition in glioblastoma. Neuro Oncol 14 , 315-325 (2012). Ersahin, T., Tuncbag, N. & Cetin-Atalay, R. The PI3K/AKT/mTOR interactive pathway. Mol Biosyst 11 , 1946-1954 (2015). Zhao, J. et al. Advancing glioblastoma treatment by targeting metabolism. Neoplasia 51 , 100985 (2024). Park, I. et al. Detection of early response to temozolomide treatment in brain tumors using hyperpolarized 13C MR metabolic imaging. J Magn Reson Imaging 33 , 1284-1290 (2011). Radoul, M. et al. MR Studies of Glioblastoma Models Treated with Dual PI3K/mTOR Inhibitor and Temozolomide:Metabolic Changes Are Associated with Enhanced Survival. Mol Cancer Ther 15 , 1113-1122 (2016). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviews received at journal 30 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9238794","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617269200,"identity":"ce5f945d-d550-4d9f-8ff3-eebe18187fe0","order_by":0,"name":"Sana Vaziri","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Vaziri","suffix":""},{"id":617269211,"identity":"d4d5b339-b3be-459c-a641-c014943f77f0","order_by":1,"name":"Janine M Lupo","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Janine","middleName":"M","lastName":"Lupo","suffix":""},{"id":617269212,"identity":"183357ee-66e0-4dd4-90d6-4e39cf36c27d","order_by":2,"name":"Adam W Autry","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"W","lastName":"Autry","suffix":""},{"id":617269214,"identity":"c35e1cde-db8d-4a74-8d3c-9180293f06b5","order_by":3,"name":"Marisa LaFontaine","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Marisa","middleName":"","lastName":"LaFontaine","suffix":""},{"id":617269217,"identity":"8d2da8a6-8e64-43c7-9a2d-29feaa447e72","order_by":4,"name":"Javier Villanueva-Meyer","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Villanueva-Meyer","suffix":""},{"id":617269219,"identity":"e6931f97-251a-4a09-bf2c-6908090a6a71","order_by":5,"name":"Daniel B Vigneron","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"B","lastName":"Vigneron","suffix":""},{"id":617269221,"identity":"b1c4c44e-0615-4e1c-a40b-69acb7a6a357","order_by":6,"name":"Nancy A Oberheim-Bush","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"A","lastName":"Oberheim-Bush","suffix":""},{"id":617269229,"identity":"a483e288-1ec4-4a89-b2e0-a04aa87662f2","order_by":7,"name":"Susan M Chang","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"M","lastName":"Chang","suffix":""},{"id":617269237,"identity":"7897fa29-f8b9-4ea6-9acd-fe70110d885f","order_by":8,"name":"Jennifer L Clarke","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"L","lastName":"Clarke","suffix":""},{"id":617269240,"identity":"984e7174-f5ac-49ba-ba53-947aa02ac61d","order_by":9,"name":"Yan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYLCCBwY2QJLxAQMDG7FaEgzSgCSzASlaGA6ToMXg+NkDDAkF5xP7+4HaPpQdJkLLmbwEoMNuJ864kczAOOMcMVoO5BiAtWyQ4D/AzNtGjJbzb0BaziVu4D/MwPyXKC03wLYcSNzAkMzAzEiMFskbbwwOJBgkG4P8crDnXDphLXzncwwffPhjJwsMMcYHP8qsCWtROMDAcADGOYBTGTKQbyBK2SgYBaNgFIxoAABUdDzVe8R/mwAAAABJRU5ErkJggg==","orcid":"","institution":"University of California San Francisco","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-27 01:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9238794/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9238794/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404361,"identity":"a710dcbc-04cd-42ab-9c4b-fd091d57bb33","added_by":"auto","created_at":"2026-04-08 09:15:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260415,"visible":true,"origin":"","legend":"\u003cp\u003eLesion volumes over the course of the study. (a). Longitudinal trajectories (relative to the start of treatment) for individual patient ROI volumes for CEL (\u003cem\u003eleft\u003c/em\u003e) and NEL (\u003cem\u003eright\u003c/em\u003e) in cubic centimeters(cc). Each line represents a single patient, with markers indicating measured volumes at scan time points. Circle markers indicate clinically stable time points, while triangles denote confirmed progression time points. (b-c) Group-averaged percent change in CEL (left) and NEL (\u003cem\u003eright\u003c/em\u003e) volumes relative to pre-treatment baseline. Values represent the mean percent change across patients within each treatment group (b) or in the PFS group (c) at each follow-up time point. Error bars denote the standard deviation when more than one patient contributed data at a given time point; error bars are omitted when only a single patient was available (\u003cem\u003eN\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/2d4d8eaf04d42afb34b28655.png"},{"id":106346838,"identity":"a95bd39f-a9a7-4357-9023-7f0b4288ea78","added_by":"auto","created_at":"2026-04-07 16:35:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178476,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal trajectories for voxel-wise metabolite ratios and conversion rates within the NAWM. The distribution of values within the ROI is summarized using the median. Values represent group-level averages of medians at each time point, computed across all patients contributing data for that treatment group and time point. Error bars denote the standard errors when more than one patient was available within the group at each time point. The number of patients contributing to each group-level estimate is indicated for BEV+, EVER+, and ALKYL+ at each time point (\u003cem\u003eN\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/4f6a17830fa319a695341c0d.png"},{"id":106404768,"identity":"381a05a2-9929-4be5-b5bd-5e5a58f736c4","added_by":"auto","created_at":"2026-04-08 09:17:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":467551,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative slice for P16 at Pre-Tx (\u003cem\u003etop row\u003c/em\u003e) and 2-month Post-Tx follow-up (\u003cem\u003ebottom row\u003c/em\u003e). Post-contrast T1 and T2 FLAIR anatomical (\u003cem\u003epanels 1-2\u003c/em\u003e), maps for Pyr%, Lac%, and Bic% (\u003cem\u003epanels 3-5\u003c/em\u003e), and k\u003csub\u003ePL \u003c/sub\u003emaps (panel 6) overlayed on the post-contrast T1 image. Arrows indicate T2 lesion.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/6d76126c6c1e07cb4035233b.png"},{"id":106403906,"identity":"963d86e9-e8c1-40c2-9f1c-6c33c873bf86","added_by":"auto","created_at":"2026-04-08 09:15:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":507326,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative slice for P19 at Pre-Tx (\u003cem\u003etop row\u003c/em\u003e) and 1-month Post-Tx follow-up (\u003cem\u003ebottom row\u003c/em\u003e). Post-contrast T1 and T2 FLAIR anatomical (panels 1-2), maps for Pyr%, Lac%, and Bic% (\u003cem\u003epanels 3-5\u003c/em\u003e), and k\u003csub\u003ePL\u003c/sub\u003e maps (\u003cem\u003epanel 6\u003c/em\u003e) overlayed on the T1 image. Arrows indicate T2 lesion.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/c10e950bdfe037eff1f62fb0.png"},{"id":106346841,"identity":"6fe59c84-5be0-4356-9043-01fbb7d312e0","added_by":"auto","created_at":"2026-04-07 16:35:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161248,"visible":true,"origin":"","legend":"\u003cp\u003eVoxel-wise comparisons between early and late progressors showing ranked metabolite percentile values (\u003cem\u003epanels 1–3\u003c/em\u003e) and k\u003csub\u003ePL\u003c/sub\u003e (\u003cem\u003epanel 4\u003c/em\u003e) within T2L voxels for P16 (\u003cem\u003etop row\u003c/em\u003e) and P19 (\u003cem\u003ebottom row\u003c/em\u003e). Boxplots show the values within the ROI at each time point, while voxels connected by lines represent HP-¹³C voxels classified within the ROI at both time points for direct voxel-wise comparison. Solid lines indicate a voxel-wise increase, while dashed lines indicate a voxel-wise decrease.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/c0737d8b8983eb1c0caa8ae9.png"},{"id":106403154,"identity":"c01952ec-8b70-435c-937c-595a103577bf","added_by":"auto","created_at":"2026-04-08 09:13:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":296069,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal trajectories for voxel-wise ranked metabolite percentile statistics and ratios (\u003cem\u003eleft\u003c/em\u003e) and metabolite conversion rates (\u003cem\u003eright\u003c/em\u003e) within the T2L. Distribution of normalized values within the ROI is summarized using the median values. Values represent group-level medians at each time point, computed across all patients contributing data to that treatment group and time point. Error bars denote the standard errors when more than one patient was available within the group at each time point. The number of patients contributing to each group-level estimate is indicated for \u003cem\u003eBEV\u003c/em\u003e+, \u003cem\u003eEVER\u003c/em\u003e+, and \u003cem\u003eALKYL\u003c/em\u003e+ at each time point. The number of patients contributing to each group-level estimate is indicated for BEV+, EVER+, and \u003cem\u003eALKYL\u003c/em\u003e+ at each time point (\u003cem\u003eN\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/06f54592bda7ffc5d4656476.png"},{"id":106346844,"identity":"7501f0ef-9c9c-49e8-bac5-4768edb0553f","added_by":"auto","created_at":"2026-04-07 16:35:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":293541,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal trajectories for voxel-wise ranked metabolite percentile and ratios (\u003cem\u003eleft\u003c/em\u003e) and metabolite conversion rates (\u003cem\u003eright\u003c/em\u003e) within T2L. All the numbers are shown after patient-wise normalization by the NAWM median value (see Figure 2). The distribution of normalized values within the ROI is summarized by the median. Values represent group-level medians at each time point, computed across all patients contributing data to that treatment group and time point. Error bars denote the standard errors when more than one patient was available within the group at each time point. The number of patients contributing to each group-level estimate is indicated at each time point (\u003cem\u003eN\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/f67a2481aa9c30c8327bb9ff.png"},{"id":106406128,"identity":"ff34ca0d-302a-448a-9c34-ebc917a6001a","added_by":"auto","created_at":"2026-04-08 09:29:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2869703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9238794/v1/69f6735b-4a18-4631-b3b1-f759a7ca7eff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluation of treatment response in patients with recurrent grade 4 glioma using hyperpolarized [1- \u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGliomas are the most common and malignant primary brain tumors in adults, with glioblastoma (GBM) being the most aggressive and prevalent form\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The latest WHO 2021 classification for grade 4 glioma now includes both isocitrate dehydrogenase (IDH)-mutant, grade 4 astrocytoma, and IDH-wildtype GBM categories\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For these grade 4 gliomas, standard-of-care treatment includes maximal surgical resection of the tumor followed by fractional radiation therapy (RT) and temozolomide (TMZ), a DNA alkylating agent\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Because patients eventually experience recurrence, a variety of second-line therapies are often integrated into the clinical management of recurrent disease as single or combined chemotherapeutic regimens. These therapies often include: (i) Lomustine (also known as CCNU, chloroethyl-cyclohexyl-nitrosourea), which is a standard second-line treatment after TMZ\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e; (ii) Everolimus, an mTORC1 inhibitor that decreases downstream protein synthesis and cell growth by blocking the mTOR pathway; or (iii) bevacizumab, an anti-angiogenic monoclonal antibody that inhibits the blood vessel formation. Despite these treatments, survival rates remain low, with fewer than 6% of patients living beyond five years after diagnosis, and a median overall survival of approximately 14\u0026ndash;16 months\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In response to the limited efficacy of existing treatments, new therapeutic strategies are actively being developed to combat high-grade disease.\u003c/p\u003e \u003cp\u003eStandard clinical practice for planning treatment and response assessment for patients with glioma relies entirely on anatomical MRI-visible contrast-enhancing lesions (CEL) and/or T2 hyperintensity lesions (T2L)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, despite the fact that these features solely indicate structural changes caused by the breakdown of the blood-brain barrier or presence of fluid, respectively. Interpreting images, especially after therapy, can be challenging due to tumor heterogeneity and treatment-induced changes, highlighting the need for advanced MR techniques to inform therapy and clinical decisions. The known metabolic reprogramming\u0026mdash;specifically, Warburg metabolic upregulation of glycolysis over oxidative phosphorylation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u0026mdash;suggests that incorporating metabolic imaging that can target this pathway could be useful for evaluating these patients.\u003c/p\u003e \u003cp\u003eHyperpolarized carbon-13 (HP-\u003csup\u003e13\u003c/sup\u003eC) metabolic MR imaging provides a unique way to noninvasively and dynamically monitor metabolic pathways on a rapid timescale after the administration of the non-toxic, nonradioactive probe\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate is the most widely used \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC probe in human brain research, capable of detecting the Warburg effect in tumor\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Previous studies in high-grade gliomas demonstrated that HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI can image, \u003cem\u003ein vivo\u003c/em\u003e, the increased conversion of [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate to [1-\u003csup\u003e13\u003c/sup\u003eC]lactate compared to \u003csup\u003e13\u003c/sup\u003eC-bicarbonate within tumors\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and the ratio of those [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate to [1-\u003csup\u003e13\u003c/sup\u003eC]lactate was higher in recurrent tumors compared to treatment effects\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Interestingly, an increase in \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e, the conversion rate from pyruvate to lactate, was also detected in normal-appearing white matter (NAWM) using HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e shortly after receiving antiangiogenic therapy, demonstrating its potential to identify early treatment-induced changes before they become visible on standard MRI.\u003c/p\u003e \u003cp\u003eThe goal of this study was to characterize dynamic metabolic changes observed in both recurrent grade 4 lesions and NAWM in relation to treatment response during and after therapy. This involved monitoring patients with recurrent grade 4 glioma \u0026mdash;either participating in a clinical trial (ID: NCT03681028, started on 12/19/2018) or seen in clinic\u0026mdash; before and during chemotherapy for recurrent disease, using serial HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI. We hypothesized that HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI would detect early metabolic changes associated with response that preceded changes on standard MRI in both the lesion and, in cases where patients received antiangiogenic therapy, NAWM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Population\u003c/h2\u003e \u003cp\u003e A total of 20 patients with recurrent grade 4 gliomas were enrolled in this IRB-approved study and provided informed consent. Tumor recurrence was confirmed through repeat surgery (19/20) or by the RANO criteria\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e (1/20). Patients were then treated with different types of chemotherapy and monitored with serial MR exams that included both clinical \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH MRI and HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI (N\u0026thinsp;=\u0026thinsp;41). Of these patients, 12 had a baseline scan prior to receiving treatment for recurrence. When feasible, follow-up imaging was performed at approximately bimonthly intervals. The number and timing of follow-up scans varied across patients due to clinical scheduling constraints and external factors, including disruptions related to the COVID-19 pandemic. Of the 20 participants, 11 experienced disease progression (confirmed by subsequent surgery (N\u0026thinsp;=\u0026thinsp;10) or RANO 2.0 criteria (N\u0026thinsp;=\u0026thinsp;1)\u003csup\u003e8\u003c/sup\u003e) within 6 months of their baseline scan. Clinical characteristics and treatment regimens for each patient are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePatients were divided into treatment-based groups. The first group, \u003cem\u003eBEV+\u003c/em\u003e, consisted of patients who received bevacizumab as part of their treatment. The second group, \u003cem\u003eEVER+\u003c/em\u003e, included those treated with everolimus in combination with other agents. The remaining patients who received multi-agent therapy with combinations of alkylating agents such as temozolomide (TMZ) or CCNU, along with other agents, like olaparib, abemaciclib, and afatinib, were grouped together into the \u003cem\u003eALKYL+\u003c/em\u003e group. Additionally, two patients served as controls: P19 was treated exclusively with TMZ, and P20 was treated only with CCNU. For the purpose of lesion analysis, patients were categorized according to their survival outcomes as either early progressors with a progression-free survival (PFS) less than 6 months, or late progressors with a PFS that exceeded 6 months.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical summary of the patient population, including treatment regimens and scan intervals.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePFS6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS (yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBaseline Acquired\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePost-Tx F1 Scan\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erAS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBevacizumab\u003c/b\u003e + [RT, TMZ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m, 3m, 5m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erAS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBevacizumab\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBevacizumab\u003c/b\u003e + [CCNU, Afatinib, Abemaciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBevacizumab\u003c/b\u003e\u0026thinsp;+\u0026thinsp;CCNU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1m, 3m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBevacizumab\u003c/b\u003e + [Carboplatin, Trametinib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEverolimus\u003c/b\u003e + [CCNU, Trametinib, Abeciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEverolimus\u003c/b\u003e + [TMZ, Olaparib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEverolimus\u003c/b\u003e + [TMZ, Afatinib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEverolimus\u003c/b\u003e + [CCNU, Dasatinib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m, 4m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erAS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTMZ\u003c/b\u003e + [Olaparib, Abemaciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m, 3m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCCNU\u003c/b\u003e + [Afatinib,\u0026nbsp;Abemaciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m, 4m, 5m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTMZ\u003c/b\u003e + [Afatinib, Olaparib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m, 4m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCCNU\u003c/b\u003e + [Afatanib, Abecliclab]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTMZ\u003c/b\u003e + [Olaparib, Abemaciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTMZ\u003c/b\u003e + [Propranolol,Trametinib,Abemaciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCCNU\u003c/b\u003e\u0026thinsp;+\u0026thinsp;Afatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTMZ\u003c/b\u003e + [Afatinib, Olaparib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCCNU\u003c/b\u003e + [Afatinib, Abemaciclib]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTMZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1m, 3m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCCNU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003erGBM: recurrent GBM; \u003cem\u003erAS4: recurrent grade 4 astrocytoma\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMR Data Acquisition\u003c/h3\u003e\n\u003cp\u003eAll MR studies were performed using a GE 3T MR750 scanner with a dual-tuned 24-channel \u003csup\u003e13\u003c/sup\u003eC/8-channel \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH head array (Rapid Biomedical, Germany). The standard-of-care \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH MR clinical protocol included anatomical 3D pre and post-gadolinium (Gd) T1 IRSPGR (TR/TE/TI\u0026thinsp;=\u0026thinsp;8/3/400 ms, 1.5 mm slices, 15\u003csup\u003eo\u003c/sup\u003e flip angle, 25.6cm x 25.6cm in-plane FOV, 256 \u0026times; 256 matrix) and T2 weighted FLAIR (TR/TE/TI\u0026thinsp;=\u0026thinsp;6250/138/1702 ms, 1.5mm slices, 25.6cm x 25.6cm in-plane FOV, 256 \u0026times; 256 matrix) images.\u003c/p\u003e \u003cp\u003eThe HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate samples were prepared using the clinical SPINlab system (General Electric, Niskayuna, NY). After passing the quality control, the solution was injected intravenously in patients (0.43 mL/kg at 5 mL/s)\u003csup\u003e14\u003c/sup\u003e, and then dynamic HP-\u003csup\u003e13\u003c/sup\u003eC data were acquired using a frequency-selective echo-planar imaging (EPI) sequence that independently excited each metabolite (TR/TE\u0026thinsp;=\u0026thinsp;62.5ms/21.7ms, 8 slice, 20 dynamic timepoints, temporal resolution\u0026thinsp;=\u0026thinsp;3-s, spatial resolution 1.5-cm isotropic with a 16 x 16 matrix size, flip angles constant \u0026#120572;\u003csub\u003epyruvate\u003c/sub\u003e/\u0026#120572;\u003csub\u003elactate\u003c/sub\u003e/\u0026#120572;\u003csub\u003ebicarbonate\u003c/sub\u003e = 20/30/30)\u003csup\u003e15\u003c/sup\u003e, except for one dataset, which was obtained with 2.0 cm isotropic spatial resolution. For 3 patients (P16, P19, and P20), HP-\u003csup\u003e13\u003c/sup\u003eC data were obtained from the same volume of interest using a template registered from the standard MNI152 space to the subject's space on the scan day via T1 images\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This approach enabled a direct voxel-wise comparison of HP-\u003csup\u003e13\u003c/sup\u003eC parameters across different time points. The other data acquisitions were prescribed axially.\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cem\u003eDynamic HP [1-\u003c/em\u003e\u003csup\u003e\u003cem\u003e13\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eC]pyruvate MRI Processing\u003c/em\u003e\u003c/div\u003e \u003cp\u003eRaw dynamic EPI data were pre-whitened, channel-combined, and denoised as described previously\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The resulting dynamic metabolite maps for [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate, [1-\u003csup\u003e13\u003c/sup\u003eC]lactate, and [1-\u003csup\u003e13\u003c/sup\u003eC]bicarbonate were temporally summed to generate area-under-the-dynamic curve (AUC) metabolite maps. Noise standard deviations for dynamic metabolite images were estimated from the background using the brain mask generated from applying FSL\u0026rsquo;s Brain Extraction Tool\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e on the T1 post-Gd image. Voxel-wise apparent signal-to-noise ratio (aSNR) maps were calculated for each metabolite using the denoised AUC images. SNR masks were created to filter out voxels with aSNR less than 5. After SNR masking of individual metabolites, ratio maps for lactate-to-pyruvate (Lac/Pyr), bicarbonate-to-pyruvate (Bic/Pyr), and bicarbonate-to-lactate (Bic/Lac) were derived from AUC maps. To facilitate comparison of metabolite signals across subjects and time points, voxel-wise raw AUC maps were transformed into percentile rank maps restricted to brain voxels. For each percentile map (Pyr%, Lac%, and Bic%), AUC values from brain-masked voxels were ranked using a tied ranking procedure that accounted for identical values, then normalized by the total number of brain voxels (without SNR filtering), and scaled to a 0\u0026ndash;100 percentile range\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The resulting percentile rank maps will be referred to as Pyr%, Lac%, and Bic%. Voxel-wise apparent rate constants for pyruvate-to-lactate conversion (\u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e) and pyruvate-to-bicarbonate conversion \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePB\u003c/sub\u003e were quantified from dynamic metabolite data using an inputless kinetic model\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Modeling error masks for \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePB\u003c/sub\u003e maps were created by filtering voxels with modeling error greater than 30% and 50% of the estimated value, respectively.\u003c/p\u003e\n\u003ch3\u003eStandard H MRI Image Processing \u0026 Region Definition\u003c/h3\u003e\n\u003cp\u003eAutomated gray and white matter segmentation was performed on the T1 pre-Gd images\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. White matter masks were refined by first aligning the T1 pre-Gd images using a non-linear registration\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e to the AAL3 atlas\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and then removing the cortex. The T2L and CEL were semi-automatically segmented from the 3D FLAIR and T1 post-Gd images, respectively, using 3D Slicer\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The non-enhancing lesion (NEL) was then defined as the T2L minus the CEL. Similarly, NAWM was defined by subtracting any of the T2L that overlapped with the automatically-segmented white matter mask. Regions of interest (ROIs) defined at \u003csup\u003e1\u003c/sup\u003eH image resolution were then mapped to the lower-resolution \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC matrix using a mutually exclusive labeling scheme: \u003csup\u003e13\u003c/sup\u003eC voxels containing at least 30% of the \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH T2L were classified as \u003csup\u003e13\u003c/sup\u003eC T2L. Of the remaining \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC voxels outside the T2L, those with at least 50% overlap with \u0026sup1;H NAWM were classified as \u003csup\u003e13\u003c/sup\u003eC NAWM voxels. Subsequent lesion analyses at \u003csup\u003e13\u003c/sup\u003eC resolution focused solely on T2L voxels, as the CEL volumes were relatively small for reliable serial comparison at the \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC spatial resolution in this study population.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eLesion volumes (CEL and NEL) were measured at the native \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH resolution for each patient scan. Percentile rank parameters (%Pyr, %Lac, %Bic) were summarized\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e within each ROI (NAWM and T2L, calculated at \u003csup\u003e13\u003c/sup\u003eC resolution voxels) by computing the median value across all voxels within the region for each patient scan. After applying SNR masks and modeling error masks, metabolite ratios and conversion rates were summarized in a similar manner. For lesion analyses, T2L values were then normalized by the corresponding patient-specific median NAWM value for each parameter. Group-level statistics were then calculated by taking the mean and standard deviation of these patient-wise median (and NAWM-normalized T2L) values across subjects within each group at each time point.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eChanges in Lesion Volume\u003c/h2\u003e \u003cp\u003eChanges in lesion volume per patient after treatment are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. Since most patients (19/20) had undergone surgical resection before treatment, the CEL volume was relatively small at baseline (median\u0026thinsp;=\u0026thinsp;5.4 cm\u003csup\u003e3\u003c/sup\u003e, [0.0-17.6], N\u0026thinsp;=\u0026thinsp;1 had no CEL), while the NEL volumes varied among patients (median\u0026thinsp;=\u0026thinsp;39.7 cm\u003csup\u003e3\u003c/sup\u003e, [3.53\u0026ndash;112.6]). Group averages by treatment category are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. Compared to the pre-treatment baseline, changes in CEL and NEL volumes varied across treatment groups. Group averages by survival status (early vs late progressors) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec. Despite differences among treatment groups, patients with longer PFS (PFS\u0026thinsp;\u0026gt;\u0026thinsp;6 months) generally exhibit slower early post-treatment growth, especially in the CEL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEarly Treatment Effects of Metabolism in the NAWM\u003c/h3\u003e\n\u003cp\u003eLongitudinal comparison of metabolite ratios and conversion rates in the NAWM revealed distinct differences among patient groups, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Early changes over time in HP-\u0026sup1;\u0026sup3;C metrics within the NAWM suggest increased relative lactate-to-pyruvate metabolism at the first follow-up for the \u003cem\u003eBEV+\u003c/em\u003e, which showed an initial increase in lactate/pyruvate (average increase from baseline of 14.3%, at 1 month) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e (average increase from baseline of 56.2% increase at 1 month) that then returned to normal by 2\u0026ndash;5 months. In contrast, the \u003cem\u003eEVER+\u003c/em\u003e group demonstrated a larger early decrease in lactate/pyruvate at 2 months (average decrease of 13.1%) and in \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e at 2 months (average decrease of 22.2%), followed by partial recovery. These trends highlight distinct temporal patterns of HP-\u0026sup1;\u0026sup3;C metabolism among the treatment groups, with \u003cem\u003eBEV+\u003c/em\u003e showing early transient increases and \u003cem\u003eEVER+\u003c/em\u003e presenting early decreases that partially normalize later.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVoxel-wise Longitudinal Analysis of Representative Case Studies\u003c/h2\u003e \u003cp\u003eTwo patients, with data collected using the same templates\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, were compared directly on a voxel-by-voxel basis: the first representing an early progressor (\u003cem\u003eP16\u003c/em\u003e) and the second representing a late progressor (\u003cem\u003eP19\u003c/em\u003e), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Representative slices for each patient at baseline and early post-treatment time points are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Boxplots and voxel-wise comparisons within the T2L at follow-up time points, relative to the pre-treatment baseline, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In both patients, voxel-wise changes in Lac%, Pyr%, Bic%, and \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e within the T2L, shown as voxels connected by lines, indicate varied treatment responses across tumors, while the changes in median values (boxplots) between the two time points remain small. Within voxels classified as within the T2L at both timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, dashed and solid lines), P16 experienced an average of 9.4% decrease in Pyr%, a 7.7% decrease in Lac%, a 38.7% decrease in Bic% (n\u0026thinsp;=\u0026thinsp;24 voxels for all metabolites), and a 10.1% decrease in k\u003csub\u003ePL\u003c/sub\u003e (n\u0026thinsp;=\u0026thinsp;17 voxels). Comparatively, P19, who experienced a longer PFS (9 months vs. 3 months), showed only a 2.61% decrease in Pyr%, a 14.72% increase in Lac%, a 12.94% increase in Bic%, and a 8.75% decrease in \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e after 2 months of treatment (n\u0026thinsp;=\u0026thinsp;4 voxels).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEarly Treatment Effects of Metabolism in the Lesion\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays longitudinal changes in the normalized Pyr%, Lac%, Bic%, metabolite ratios, and conversion rates within the T2L voxels for each group, using patient-wise normalization based on the median values in the NAWM, given the treatment-induced changes illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Within the lesion, the initial change patterns are similar to those in the NAWM, with the \u003cem\u003eBEV+\u003c/em\u003e increasing on Lac/Pyr (24.3% from baseline at 1 month) and the \u003cem\u003eEVER+\u003c/em\u003e decreasing in Lac/Pyr (average of 7.6% from baseline at 2 months) after NAWM normalization. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the same analyses after grouping patients by survival status. Among patients with early progression (PFS\u0026thinsp;\u0026lt;\u0026thinsp;6 months), increases in Lac% (average of 148.8% increase from baseline), Lac/Pyr (average of 9.6% increase from baseline) and \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e (average of 6.4% increase from baseline), and reductions in the Bic/Lac (average of 57.4% decrease from baseline) were observed as early as the 1-month follow-up. Although these values are averages across patients and driven by a single patient with T2L voxels at the 1-month timepoint, the trend toward elevated lactate production persists at 2 months following treatment. This pattern indicates a general treatment-related change in the distribution of pyruvate and lactate among early progressors. In contrast, patients without early progression demonstrate more modest longitudinal changes in Lac%, Lac/Pyr, and \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e within T2L, indicating relatively stable metabolic profiles over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to use serial HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI to examine treatment response in patients with grade 4 glioma. The patients included in this study participated in both clinical trial and standard-of-care treatments, representing typical clinical practice. Treatment effects within the NAWM, depending on the type of therapy, have been observed as changes in metabolism. Additionally, early metabolite differences were identified in patients with early disease progression, offering valuable initial insights into the use of real-time metabolic imaging to evaluate treatment response in glioma.\u003c/p\u003e \u003cp\u003eBecause of the heterogeneous treatment regimens used at the time of recurrence and prior work revealing metabolic changes on HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI due to angiogenic\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and mTOR inhibitors\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, subjects were grouped accordingly to facilitate analysis. Our analysis revealed treatment-dependent differences, most notably in the \u003cem\u003eBEV+\u003c/em\u003e group. Patients treated with bevacizumab showed increases in Lac/Pyr and \u003cem\u003ek\u003c/em\u003e\u003csub\u003ePL\u003c/sub\u003e within the NAWM and T2L during early follow-up, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Changes in glucose and pyruvate metabolism were reported after anti-angiogenic therapy\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, with greater uptake of labeled glucose and higher levels of labeled lactate in both tumors and the contralateral brain. Additionally, the antiangiogenic therapy induces hypoxia, leading to reduced perfusion\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, as reflected by the decrease in %Pyr in T2L, which is consistent with prior research\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In the \u003cem\u003eEVER+\u003c/em\u003e group, where mTOR inhibitors may affect brain metabolism by targeting core cellular functions, a shift from anabolic growth to catabolic maintenance occurs\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This is demonstrated by a reduction in lactate-to-pyruvate conversion in both NAWM and T2 lesions at the early time point. Although no statistically significant changes were observed in the normal brains of rats treated with Everolimus, the Lac/Pyr ratios in the tumors showed an early increase after normalization\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, similar to the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. While longitudinal changes were observed in the \u003cem\u003eALKYL+\u003c/em\u003e, these effects were less pronounced.\u003c/p\u003e \u003cp\u003eIt is important to note that the relatively lower spatial resolution of HP-\u003csup\u003e13\u003c/sup\u003eC imaging may lead to partial volume effects, which can hinder the interpretation of results. Additionally, lesion size and location varied across patients, which complicated direct comparisons between subjects. By applying the prescription template in atlas space and then transforming it to the individual subject space\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, we were able to achieve voxel-wise correspondence across time points for two patients. Such voxel-wise longitudinal comparisons in patients revealed significant spatial changes in metabolism at each time point between baseline and post-treatment follow-up scans. We particularly noted differences in parameter distributions between patients across T2L voxels. This underscores the diverse metabolic profiles both within tumors and among individuals and illustrates the difficulty of directly comparing HP-\u003csup\u003e13\u003c/sup\u003eC parameters across different subjects.\u003c/p\u003e \u003cp\u003ePatient-specific ranges of HP-\u0026sup1;\u0026sup3;C metabolite parameters motivated the normalization of metabolite metrics relative to an internal NAWM reference before cross-patient longitudinal analysis of lesions. After this normalization, clearer patterns emerged when patients were grouped by PFS. Differences between early and late progressors became more evident at early post-treatment time points, as soon as 1\u0026ndash;2 months, indicating that normalization improves sensitivity to progression-related metabolic changes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. These findings underscore the importance of patient-specific scaling in longitudinal and cross-patient analyses of HP-\u0026sup1;\u0026sup3;C metabolic imaging data.\u003c/p\u003e \u003cp\u003eMore importantly, similar to preclinical GBM models, where a decrease in lactate/pyruvate within 7\u0026ndash;9 days after treatment with TMZ or mTOR inhibitors served as an early biomarker of treatment response\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, we observed a pattern of an early increase in lactate/pyruvate and a decrease in the bicarbonate/lactate ratio after treatment in patients with shorter PFS. These findings provide initial insights into the early assessment of treatment response in high-grade glioma using HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI. Monitoring metabolic changes in recurrent lesions from pretreatment to follow-up scans can indicate therapy effectiveness, guide treatment strategies, and influence clinical decisions.\u003c/p\u003e \u003cp\u003eDespite promising results from this initial longitudinal study using HP [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI to evaluate treatment response, several limitations must be recognized. Although the cohort size is sufficient, variability in treatments restricted the depth of statistical analysis. Additionally, the low resolution of HP-\u003csup\u003e13\u003c/sup\u003eC imaging and the small size of post-surgical lesions led to fewer patients with follow-up data and usable voxels. Partial-volume effects, caused by the spatial constraints of HP-\u0026sup1;\u0026sup3;C imaging, further hinder interpretation in small or heterogeneous lesions. Future research should aim to collect longitudinal data with higher temporal resolution to more effectively monitor response to treatment and include larger homogeneous cohorts to facilitate patient-specific intervention analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.M.L., S.M.C., J.L.C., and Y.L. designed the study. D.B.V. helped with imaging methods. S.V., A.W.A., and M.L. collected and processed imaging data. J.V.M., N.A.O.B., J.L.C., and S.M.C. analyzed the clinical data. S.V. conducted data analysis. S.V., J.M.L., and Y.L. interpreted the data and were main contributors to writing the manuscript. All authors reviewed, read, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by NIH P01CA118816, NIH P50CA097257, and the Glioblastoma Precision Medicine Program, with technical assistance from P41 EB013598.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of California, San Francisco (IRB 15-16749).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe processing scripts and example data will be made available by the authors. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOstrom, Q.T.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. \u003cem\u003eNeuro Oncol\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, v1-v95 (2022).\u003c/li\u003e\n \u003cli\u003eLouis, D.N.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. \u003cem\u003eNeuro Oncol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1231-1251 (2021).\u003c/li\u003e\n \u003cli\u003eSingh, N., Miner, A., Hennis, L. \u0026amp; Mittal, S. Mechanisms of temozolomide resistance in glioblastoma - a comprehensive review. \u003cem\u003eCancer Drug Resist\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 17-43 (2021).\u003c/li\u003e\n \u003cli\u003eWeller, M. \u0026amp; Le Rhun, E. How did lomustine become standard of care in recurrent glioblastoma? \u003cem\u003eCancer Treat Rev\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 102029 (2020).\u003c/li\u003e\n \u003cli\u003eGilbert, M.R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Dose-dense temozolomide for newly diagnosed glioblastoma: a randomized phase III clinical trial. \u003cem\u003eJ Clin Oncol\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 4085-4091 (2013).\u003c/li\u003e\n \u003cli\u003eStupp, R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e352\u003c/strong\u003e, 987-996 (2005).\u003c/li\u003e\n \u003cli\u003eTan, A.C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Management of glioblastoma: State of the art and future directions. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 299-312 (2020).\u003c/li\u003e\n \u003cli\u003eWen, P.Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e RANO 2.0: Update to the Response Assessment in Neuro-Oncology Criteria for High- and Low-Grade Gliomas in Adults. \u003cem\u003eJ Clin Oncol\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 5187-5199 (2023).\u003c/li\u003e\n \u003cli\u003eWarburg, O. On respiratory impairment in cancer cells. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e, 269-270 (1956).\u003c/li\u003e\n \u003cli\u003eLi, Y., Vigneron, D.B. \u0026amp; Xu, D. Current human brain applications and challenges of dynamic hyperpolarized carbon-13 labeled pyruvate MR metabolic imaging. \u003cem\u003eEur J Nucl Med Mol Imaging\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 4225-4235 (2021).\u003c/li\u003e\n \u003cli\u003eAutry, A.W.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Clinical Translation of Hyperpolarized (13)C Metabolic Probes for Glioma Imaging. \u003cem\u003eAJNR Am J Neuroradiol\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1754-1764 (2025).\u003c/li\u003e\n \u003cli\u003eZaccagna, F.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Imaging Glioblastoma Metabolism by Using Hyperpolarized [1-(13)C]Pyruvate Demonstrates Heterogeneity in Lactate Labeling: A Proof of Principle Study. \u003cem\u003eRadiol Imaging Cancer\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e210076 (2022).\u003c/li\u003e\n \u003cli\u003eAutry, A.W.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Multi-parametric hyperpolarized (13)C/(1)H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas. \u003cem\u003eNeuroimage Clin\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 103501 (2023).\u003c/li\u003e\n \u003cli\u003eAutry, A.W.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Characterization of serial hyperpolarized (13)C metabolic imaging in patients with glioma. \u003cem\u003eNeuroimage Clin\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 102323 (2020).\u003c/li\u003e\n \u003cli\u003eGordon, J.W.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Translation of Carbon-13 EPI for hyperpolarized MR molecular imaging of prostate and brain cancer patients. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 2702-2709 (2019).\u003c/li\u003e\n \u003cli\u003eAutry, A.W.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Advanced Hyperpolarized (13)C Metabolic Imaging Protocol for Patients with Gliomas: A Comprehensive Multimodal MRI Approach. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e (2024).\u003c/li\u003e\n \u003cli\u003ePruessmann, K.P., Weiger, M., Bornert, P. \u0026amp; Boesiger, P. Advances in sensitivity encoding with arbitrary k-space trajectories. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 638-651 (2001).\u003c/li\u003e\n \u003cli\u003eWang, J., Wright, A.J., Hesketh, R.L., Hu, D.E. \u0026amp; Brindle, K.M. A referenceless Nyquist ghost correction workflow for echo planar imaging of hyperpolarized [1-(13) C]pyruvate and [1-(13) C]lactate. \u003cem\u003eNMR Biomed\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e (2018).\u003c/li\u003e\n \u003cli\u003eZhu, Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Coil combination methods for multi-channel hyperpolarized (13)C imaging data from human studies. \u003cem\u003eJ Magn Reson\u003c/em\u003e \u003cstrong\u003e301\u003c/strong\u003e, 73-79 (2019).\u003c/li\u003e\n \u003cli\u003eVaziri, S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-(13)C]pyruvate MRI data from patients with glioma. \u003cem\u003eNeuroimage Clin\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 103155 (2022).\u003c/li\u003e\n \u003cli\u003eSmith, S.M. Fast robust automated brain extraction. \u003cem\u003eHum Brain Mapp\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 143-155 (2002).\u003c/li\u003e\n \u003cli\u003eLarson, P.E.Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Investigation of analysis methods for hyperpolarized 13C-pyruvate metabolic MRI in prostate cancer patients. \u003cem\u003eNMR Biomed\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, e3997 (2018).\u003c/li\u003e\n \u003cli\u003eZhang, Y., Brady, M. \u0026amp; Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. \u003cem\u003eIEEE Trans Med Imaging\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 45-57 (2001).\u003c/li\u003e\n \u003cli\u003eLi, Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme. \u003cem\u003eNeuro Oncol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 546-557 (2011).\u003c/li\u003e\n \u003cli\u003eJenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W. \u0026amp; Smith, S.M. Fsl. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 782-790 (2012).\u003c/li\u003e\n \u003cli\u003eRolls, E.T., Huang, C.C., Lin, C.P., Feng, J. \u0026amp; Joliot, M. Automated anatomical labelling atlas 3. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e206\u003c/strong\u003e, 116189 (2020).\u003c/li\u003e\n \u003cli\u003eKikinis, R. \u0026amp; Pieper, S. 3D Slicer as a tool for interactive brain tumor segmentation. \u003cem\u003eAnnu Int Conf IEEE Eng Med Biol Soc\u003c/em\u003e \u003cstrong\u003e2011\u003c/strong\u003e, 6982-6984 (2011).\u003c/li\u003e\n \u003cli\u003eChaumeil, M.M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Hyperpolarized 13C MR spectroscopic imaging can be used to monitor Everolimus treatment in vivo in an orthotopic rodent model of glioblastoma. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 193-201 (2012).\u003c/li\u003e\n \u003cli\u003eFack, F.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Bevacizumab treatment induces metabolic adaptation toward anaerobic metabolism in glioblastomas. \u003cem\u003eActa Neuropathol\u003c/em\u003e \u003cstrong\u003e129\u003c/strong\u003e, 115-131 (2015).\u003c/li\u003e\n \u003cli\u003eKeunen, O.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 3749-3754 (2011).\u003c/li\u003e\n \u003cli\u003eVenkatesh, H.S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Reduced phosphocholine and hyperpolarized lactate provide magnetic resonance biomarkers of PI3K/Akt/mTOR inhibition in glioblastoma. \u003cem\u003eNeuro Oncol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 315-325 (2012).\u003c/li\u003e\n \u003cli\u003eErsahin, T., Tuncbag, N. \u0026amp; Cetin-Atalay, R. The PI3K/AKT/mTOR interactive pathway. \u003cem\u003eMol Biosyst\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1946-1954 (2015).\u003c/li\u003e\n \u003cli\u003eZhao, J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Advancing glioblastoma treatment by targeting metabolism. \u003cem\u003eNeoplasia\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 100985 (2024).\u003c/li\u003e\n \u003cli\u003ePark, I.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Detection of early response to temozolomide treatment in brain tumors using hyperpolarized 13C MR metabolic imaging. \u003cem\u003eJ Magn Reson Imaging\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 1284-1290 (2011).\u003c/li\u003e\n \u003cli\u003eRadoul, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e MR Studies of Glioblastoma Models Treated with Dual PI3K/mTOR Inhibitor and Temozolomide:Metabolic Changes Are Associated with Enhanced Survival. \u003cem\u003eMol Cancer Ther\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1113-1122 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Imaging](https://www.nature.com/npjimaging)","snPcode":"44303","submissionUrl":"https://submission.springernature.com/new-submission/44303/3","title":"npj Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9238794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9238794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperpolarized (HP) [1-\u003csup\u003e13\u003c/sup\u003eC]pyruvate MRI can noninvasively detect dynamic metabolic activity in the brain. This study utilizes HP-[1-\u003csup\u003e13\u003c/sup\u003eC]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.\u0026nbsp; 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.\u003c/p\u003e","manuscriptTitle":"Evaluation of treatment response in patients with recurrent grade 4 glioma using hyperpolarized [1- 13C]pyruvate MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:35:52","doi":"10.21203/rs.3.rs-9238794/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T09:31:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T21:28:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T15:47:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T09:15:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216172956466767024917737022942579193256","date":"2026-04-03T17:01:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207804400161763340940445898703817813284","date":"2026-04-02T12:19:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334956667598703038613744568235325463415","date":"2026-04-02T08:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T15:46:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T00:25:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Imaging","date":"2026-04-01T00:21:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Imaging](https://www.nature.com/npjimaging)","snPcode":"44303","submissionUrl":"https://submission.springernature.com/new-submission/44303/3","title":"npj Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"868a860f-c515-4d1b-9bc2-61f9e1153867","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T09:31:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T21:28:54+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65848148,"name":"Biological sciences/Cancer"},{"id":65848149,"name":"Health sciences/Medical research"},{"id":65848150,"name":"Health sciences/Neurology"},{"id":65848151,"name":"Biological sciences/Neuroscience"},{"id":65848152,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-07T09:39:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:35:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9238794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9238794","identity":"rs-9238794","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}