Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma

Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma | 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 Research Article Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma Jun Wu, Jue Lu, Xinli Zhang, Xiaotong Guo, Qian Qin, Jiaqi Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8770485/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are two different types of malignant brain tumors, precise preoperative differentiation is crucial for guiding optimal treatments. This study aimed to evaluate the diagnostic utility of time-dependent diffusion MRI (t d -dMRI)-derived microstructural parameters in differentiating PCNSL from GBM and to correlate these parameters with histopathologic findings. Methods This study included 32 GBM and 19 PCNSL patients who underwent 3.0-T MRI with oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences. Microstructural parameters [intracellular volume fraction ( V in ), cell diameter, cellularity, extracellular diffusivity ( D ex )] were compared between groups. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of these indices. Histopathologic validation was performed by correlating t d -dMRI parameters with hematoxylin-eosin (H&E)–stained sections. Results In enhancing tumor regions, PCNSL showed significantly lower cell diameter and D ex , but higher V in and cellularity than GBM (all p < 0.001). V in demonstrated the highest diagnostic accuracy (AUC = 0.901; sensitivity = 0.737; specificity = 0.906). No significant differences were observed in peritumoral regions. V in correlated strongly with histopathologic nuclear volume fraction (r = 0.76; p < 0.0001). Conclusion T d -dMRI-derived microstructural parameters, particularly V in , effectively differentiated PCNSL from GBM, providing a novel approach to improve preoperative diagnosis. Time-dependent diffusion MRI primary central nervous system lymphoma glioblastoma microstructure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are among the most prevalent malignant primary brain tumors [ 1 , 2 ]. The standard treatment for GBM is surgical resection followed by concurrent chemoradiotherapy [ 3 ]. For PCNSL, the first-line regimen is high-dose methotrexate–based combination therapy [ 4 ]. Surgical resection in PCNSL not only delays chemotherapy but also increases the risk of surgery-related complications or rapid disease progression. Therefore, accurate preoperative differentiation is vital for guiding optimal therapeutic strategies. Magnetic resonance imaging (MRI) is the most common noninvasive tool for the preoperative diagnosis of GBM and PCNSL. In contrast-enhanced T1-weighted imaging (CE-T1WI), GBM typically shows heterogeneous ring enhancement with central necrosis, whereas PCNSL often presents as homogeneously enhancing masses [ 5 ]. However, considerable overlap exists in their imaging features, and atypical manifestations of either GBM or PCNSL increase the difficulty of differential diagnosis [ 6 , 7 ]. Recent studies have shown that multiparametric MRI techniques improve the differentiation of complex cases. Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) have demonstrated good diagnostic performance in distinguishing PCNSL from GBM [ 6 , 8 , 9 ]. However, ADC values are influenced by multiple pathological factors, including inflammation, cell proliferation, and necrosis, and therefore cannot provide accurate information on specific tumor cell microstructures [ 10 ]. Time-dependent diffusion MRI (t d -dMRI) is an emerging technique developed from conventional pulsed gradient spin-echo (PGSE) DWI. Constrained by gradient strength, PGSE probes diffusion over relatively long diffusion times and is more sensitive to length scales > 13 µm, which exceed typical axon and cell sizes in the central nervous system (CNS) [ 11 ]. By introducing oscillation frequency into gradient pulses, oscillating gradient spin-echo (OGSE) sequences shorten the effective diffusion time of measurable water molecules and enhance sensitivity to smaller spatial scales of restriction [ 12 ]. By combining measurements across a range of diffusion times, typically achieved with PGSE and OGSE, t d -dMRI acquires diffusion signals across different scales, thereby capturing microscopic structural changes such as cell diameter, cellularity, and intracellular volume fraction ( V in ), along with other cellular-level microstructural features [ 13 , 14 ]. Recently, t d -dMRI has shown great potential in tumor diagnosis and differentiation [ 15 , 16 ], histological and molecular classification [ 10 ], and prediction of prognostic features and treatment response [ 17 , 18 ]. Given the distinct pathological profiles of GBM and PCNSL, we hypothesized that t d -dMRI-derived microstructural markers would differentiate these entities, with findings correlating with histopathologic features. This study aimed to evaluate the ability of t d -dMRI-based microstructural parameters to differentiate PCNSL from GBM and to further investigate their diagnostic performance. Methods Patients The study received approval from the Institutional Review Board of our institution (Wuhan Union Hospital, NO. UHCT22438) and written informed consent was obtained from all patients. Between June 2021 and March 2025, 66 patients suspected of PCNSL or GBM who underwent preoperative t d -dMRI were enrolled. Inclusion required (1) pathological confirmation of either tumor type, and (2) age > 18 years. The exclusion criteria were as follows: (1) absence of any required MRI sequence (T1WI, T2-FLAIR, 3D-enhanced T1WI, DWI with both OGSE and PGSE) or images with severe artifacts; (2) lesion size < 1 cm; and (3) prior antitumor therapy before MRI. For patients with multiple lesions, the largest lesion was analyzed. The enrollment flowchart is shown in Fig. 1. Figure 1 Flowchart shows participant enrollment. PCNSL = primary central nervous system lymphoma, GBM = glioblastoma. Data acquisition All scans were performed on a 3.0-T Ingenia CX Philips scanner (80 mT/m gradient, 200 mT/m/ms switching rate) with a 32-channel head coil. Routine structural MRI included T1WI, T2-FLAIR, and 3D-enhanced T1WI, with a total scan duration of 17 minutes. T d -dMRI integrated PGSE and OGSE sequences with different diffusion times to assess diffusion time dependence of microstructural components, as previously described [ 19 ]. OGSE was acquired at oscillating frequencies of 25 Hz (effective t d = 12 ms, 1 cycle, b = 0/250/500/800/1200 s/mm 2 ) and 50 Hz (effective t d = 6 ms, 2 cycles, b = 0/100/200/300 s/mm 2 ). PGSE was acquired with diffusion duration/separation = 15.9/77.3 ms at b-value of 0/250/500/1000/1500 s/mm 2 , for OGSE corresponding diffusion duration/separation values of 40.9/48.8 ms. See Supplementary Text for details. Image analysis OGSE datasets were coregistered to PGSE using b0 images in ITK-SNAP ( www.itksnap.org ) to correct for the patient motion. Two trainee radiologists (J.W., J.L.) manually delineated regions of interest (ROIs) on PGSE images under the supervision of an expert radiologist (J.W., 20 years’ experience in neuroradiology). Areas with necrosis, cysts, and hemorrhage were carefully excluded. The solid tumor was defined as either the contrast-enhanced region on 3D-enhanced T1WI or the hyperintense region on T2-FLAIR with low signal intensity on PGSE (when contrast enhancement was absent). Peritumoral edema was delineated as the non-enhancing T2-FLAIR hyperintense area within 10 mm of the tumor margin, as previously described [ 20 ]. Microstructural parameters derived from the IMPULSED (improved microstructural parameters using limited spectrally edited diffusion) model were assessed by two radiology trainees (J.W., J.L.), blinded to each other’s findings. The principle of the IMPULSED scheme is described in Supplementary Text. V in , cell diameter, cellularity, and extracellular diffusivity ( D ex ) were calculated using the IMPULSED model in MATLAB (version 2024b, MathWorks, USA). Histopathological Analysis High-definition (100×) hematoxylin and eosin (H&E)-stained histopathological slides were obtained from 29 patients of GBM and 14 patients of PCNSL. Nuclei in each H&E section were segmented using a pre-trained conditional generative adversarial network [ 21 ]. Pathology-based intracellular volume fraction ( f i ₙ ) was computed as: \(\:fᵢₙ={\left(\sum\:_{n}{A}_{nuclei}/{A}_{tissue}\right)}^{3/2}\) , where A nuclei represented the area of the segmented nucleus and A tissue standed for the total area of the entire tissue section [ 10 ]. Statistical Analysis Statistical analyses were performed using SPSS (version 26.0) and GraphPad Prism (version 10.0). Interobserver agreement was assessed with the intraclass correlation coefficient (ICC). ICC 0.9 indicated poor, moderate, good and excellent reproducibility, respectively [ 22 ]. Measurements from the two observers were averaged for each case and used for further analysis. Continuous variables were reported as mean ± SD, and categorical variables were expressed as frequencies. Group differences were evaluated using the independent t -test for normally distributed data or the Mann-Whitney U test for non-normally distributed data. Categorical variables were compared using the chi-squared test. Receiver operating characteristic (ROC) analysis was performed to determine optimal thresholds and to calculate the area under the curve (AUC), sensitivity, specificity, and accuracy for differential diagnosis. Spearman correlation analysis was conducted to assess associations between variables. A p -value < 0.05 was considered statistically significant. Results Clinic information A total of 66 consecutive patients were considered for inclusion. 15 patients were excluded due to incomplete scan of preoperative MRI sequences or severe artifacts and prior surgical resection. Finally, 32 patients with GBM (20 men, 12 women) and 19 with PCNSL (10 men, 9 women) were analyzed. There was no significant difference in mean age ( p = 0.785) or sex distribution ( p = 0.489). Tumor characteristics for both groups are summarized in Supplement Table S1 . ICCs with 95% confidence intervals (CI) for each t d -dMRI-derived microstructural parameter are presented in Supplement Table S2 , demonstrating excellent interobserver agreement for all metrics. Comparison of t-dMRI-based microstructural markers between PCNSLs and GBMs Representative t d -dMRI microstructural maps of enhancing tumor regions from one patient with PCNSL and one with GBM are shown in Fig. 2. Except for ADC at 50 Hz, all parameters exhibited significant group differences ( p < 0.001; Fig. 3). Specifically, GBM showed higher cell diameter, D ex , and ADC values at 0 Hz and 25 Hz, while PCNSL demonstrated elevated relative ADC changes (rADC 1 , rADC 2 ), V in , and cellularity (Table 1 ). In both tumor types, ADC maps demonstrated a time-dependent increase from 0 Hz to 50 Hz, with ADC values at 25 Hz and 50 Hz significantly higher than those at 0 Hz (all p < 0.001; Supplementary Figure S1 a-b). Figure 2 Diffusivity MRI maps in 2 participants show PGSE and OGSE data, and microstructural maps. ( a ) Diffusivity MRI maps in a 33-year-old woman with GBM; the corresponding relative apparent diffusion coefficient change (rADC 1 , rADC 2 ) was 20.80% and 35.02% respectively. ( b ) Diffusivity MRI maps in a 58-year-old man with PCNSL; the corresponding rADC 1 , rADC 2 was 18.45% and 34.43% respectively. The table (bottom) shows the corresponding specific values ( V in , diameter, cellularity, D ex , ADC 0Hz, ADC 25 Hz, ADC 50Hz ). ADC 0Hz = ADC of pulsed gradient spin-echo data, ADC 25 Hz = ADC measurement at 25 Hz, ADC 50Hz = ADC measurement at 50 Hz. Figure 3 Group differences between GBM and PCNSL for all MRI markers. ( a ) V in , ( b )diameter, ( c )cellularity, ( d ) D ex , ( e-g )ADC values measured at 0 Hz, 25 Hz, 50 Hz and ( h-i )the relative ADC values. rADC 1 = (ADC 25Hz - ADC 0Hz ) / ADC 0Hz , rADC 2 = (ADC 50Hz - ADC 0Hz ) / ADC 0Hz . * p < 0.05, ** p < 0.01, *** p < 0.001, ns show no significance. Table 1 Comparison of t d -MRI–derived microstructural parameters for distinguishing the tumor region of PCNSL and GBM patients. Parameter PCNSL(n = 19) GBM(n = 32) p value V in § 0.26 ± 0.06 0.18 ± 0.04 < 0.001 Diameter § (µm) 13.61 ± 1.63 14.51 ± 0.87 0.036 Cellularity § (µm − 1 ) 2.09 ± 0.59 1.30 ± 0.37 < 0.001 D ex # (µm 2 /msec) 1.32 ± 0.13 1.43 ± 0.14 0.003 ADC 0Hz § (µm 2 /msec) 0.82 ± 0.13 1.03 ± 0.16 < 0.001 ADC 25Hz # (µm 2 /msec) 1.05 ± 0.15 1.23 ± 0.16 < 0.001 ADC 50Hz # (µm 2 /msec) 1.36 ± 0.15 1.44 ± 0.17 0.098 rADC 1 § (%) 21.78 ± 6.51 16.30 ± 3.44 < 0.001 rADC 2 § (%) 37.33 ± 9.39 26.89 ± 4.58 < 0.001 Note: Continuous variables are expressed as mean ± standard deviation. V in = intracellular fraction, D ex = extracellular diffusivity, ADC = apparent diffusion coefficient, ADC 0Hz = ADC measurement at 0 Hz, ADC 25Hz = ADC measurement at 25 Hz, ADC 50Hz = ADC measurement at 50 Hz, rADC 1 = (ADC 25Hz -ADC 0Hz )/ADC 0Hz, rADC 2 = (ADC 50Hz -ADC 0Hz )/ADC 0Hz . § Independent sample t-test # Mann–Whitney U test Representative t d -dMRI-derived microstructural maps of the peritumoral region from patients with PCNSL and GBM are shown in Fig. 2. In both tumor types, ADC values at 50 Hz were significantly higher than those at 0 Hz and 25 Hz (all p < 0.001; Supplementary Figure S1 c-d). However, no significant differences in any t d -dMRI metrics were observed between PCNSL and GBM in the peritumoral regions (Fig. 4, Supplementary Table S3). Figure 4 Group differences between the peritumoral edema of GBM and PCNSL for all MRI markers. ( a ) V in , ( b )diameter, ( c )cellularity, ( d ) D ex , ( e-g )ADC values measured at 0 Hz, 25 Hz, 50 Hz and ( h-i )the relative ADC values. rADC 1 = (ADC 25Hz -ADC 0Hz )/ADC 0Hz , rADC 2 = (ADC 50Hz -ADC 0Hz )/ADC 0Hz . ns show no significance. Diagnostic performance in differentiating PCNSLs from GBMs The results of the ROC curve analysis are summarized in Supplementary Table S4 In enhancing regions, all MRI parameters demonstrated reasonable diagnostic accuracy in differentiating PCNSL from GBM. V in showed the highest diagnostic performance, with an AUC of 0.901 (95% CI: 0.815–0.987, sensitivity: 0.737, specificity: 0.906, accuracy: 0.843), followed by cellularity, rADC 2 , and rADC 1 , with AUCs of 0.885, 0.878, and 0.873, respectively. ADC at 50 Hz showed lower discriminative power compared with the above t d -dMRI indices, with an AUC of 0.640. The corresponding ROC curves are presented in Fig. 5. Figure 5 ROC curves of MRI markers. ADC 0Hz = ADC measurement at 0 Hz, ADC 25Hz = ADC measurement at 25 Hz, ADC 50Hz = ADC measurement at 50 Hz, rADC 1 = (ADC 25Hz -ADC 0Hz ) /ADC 0Hz , rADC 2 = (ADC 50Hz -ADC 0Hz )/ ADC 0Hz . Histopathologic validation H&E-stained sections were used to validate t d -dMRI parameters (Fig. 6a and 6b). The IMPULSED-derived V in correlated strongly with the volume fraction quantified from the segmented nuclei ( f nuclei ) (r = 0.76, p < 0.0001; n = 43). Cellularity was closely associated with ADC values at 0 Hz, whereas td-dMRI–derived cell diameter showed poor correlation with them, likely due to glioma cell pleomorphism in GBM (Supplementary Figure S2 ). Figure 6 Correlation between pathologic findings and the microstructural mapping results. ( a ) Hematoxylin and eosin (H&E)–stained slices (magnification, ×100) of a GBM (n = 29) and a PCNSL (n = 14) patients. Nuclei was automatically quantified with a conditional generative adversarial network. ( b ) The graph shows correlation between V in from IMPULSED model and pathologic examination–based f nuclei in 43 participants. Discussion This study demonstrated that t d -dMRI–derived parameters revealed distinct group differences, with significantly lower V in , diameter, D ex , and ADC values, and higher cellularity and relative ADC changes (rADC 1 , rADC 2 ) in PCNSL patients. Among these, V in showed superior diagnostic performance, underscoring t d -dMRI’s potential to differentiate PCNSL from GBM. These microstructural markers ( V in , cellularity) reflected specific pathologic features, indicating t d -dMRI’s capacity to provide cellular- or even subcellular-level insights. Given the divergent therapeutic strategies, accurate differentiation between PCNSL and GBM is critical for optimizing patient outcomes—timely high-dose methotrexate for PCNSL versus maximal tumor resection for GBM. Prior studies have evaluated advanced MRI techniques for this purpose. Yu et al. reported that multiparametric models incorporating relative mean ADC (rADC(mean)), maximum cerebral blood flow (CBF(max)), and non-enhancing tumor volume (nET) achieved superior diagnostic performance compared with single- or two-variable models (AUC 0.96, sensitivity 90%, specificity 96.55%) [ 6 ]. Diffusional kurtosis imaging (DKI)–derived water kurtosis and diffusional metrics have been shown to correlate with nuclear-to-cytoplasmic ratio, highlighting DKI’s value in distinguishing PCNSL from high-grade gliomas (HGGs) [ 23 ]. Multiparametric MRI–based radiomics and deep learning approaches have also yielded strong diagnostic performance (AUC 0.899 and 0.977, respectively) [ 24 , 25 ], although these methods cannot directly characterize microstructural features. Würtemberger et al. further demonstrated that diffusion microstructure imaging (DMI) combined with diffusion-weighted imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) achieved high predictive values in separating PCNSL from GBM, with mean diffusivity (MD) and intracellular volume fraction (ICVF) performing best (AUC 0.960) [ 26 ]. However, these approaches mainly emphasized axonal-related features rather than microstructural characteristics of tumor cells and did not account for diffusion-time effects, a unique advantage of t d -dMRI. Our findings demonstrated that t d -dMRI–derived microstructural indices not only effectively distinguished PCNSL from GBM but also revealed multifaceted tissue-level disparities. Among all MRI markers, V in showed the highest diagnostic performance (AUC = 0.901), with PCNSL exhibiting significantly higher V in than GBM. This finding was consistent with Haopeng P et al., who reported a higher N/C ratio in PCNSL compared with HGGs [ 23 ], suggesting that the elevated N/C ratio may underlie the increased V in observed in PCNSL. PCNSL also demonstrated higher cellularity than GBM. The mean cell diameter in PCNSL (13.61 ± 1.63 µm) was smaller than in GBM (14.51 ± 0.87 µm), aligning with reported ranges of 10–20 µm for PCNSL and 10–33 µm for GBM [ 27 , 28 ]. D ex which reflects extracellular space architecture was lower in PCNSL than in GBM. This is attributable to the uniform high cellular density and compact extracellular space of PCNSL, in contrast with the heterogeneous moderate cellularity and medium-sized extracellular space of GBM, influenced by extracellular matrix components, hemorrhage, and necrosis [ 29 , 30 ]. These structural distinctions explain the lower D ex in PCNSL, as increased cellular density and reduced extracellular space restrict free molecular diffusion. ADC values reflect water diffusional restriction and can indicate tumor cellularity, necrosis, and cystic degeneration [ 31 ]. Several studies have reported that ADC values within enhancing regions of PCNSL are generally lower than those of GBM, due to higher tumor cell density and N/C ratio [ 32 – 34 ]. Our results corroborated these findings: ADC 0Hz and ADC 25Hz were useful for distinguishing PCNSL from GBM. Notably, ADC values increased as diffusion time decreased in both tumor types, highlighting the time-dependent nature of diffusion. However, ADC 50Hz lacked discriminatory value, suggesting that shorter diffusion times may reduce sensitivity to microstructural differences. This emphasizes the importance of selecting optimized effective diffusion times in clinical DWI. Kamimura et al. recently demonstrated that t d -dMRI parameters may aid in differentiating PCNSL from GBM, with mean relative ADC changes achieving the highest discriminative performance (AUC = 0.920) [ 28 ]. However, their method relied on ADC values derived from OGSE (Δ eff = 7.1 ms) and conventional PGSE (Δ eff = 44.5 ms), which are not directly linked to tumor microstructural features. In contrast, our study applied OGSE at 25 Hz and 50 Hz with multiple b values, integrating IMPULSED-modeled parameters (diameter, cellularity, V in , D ex ) to characterize tumor microstructure. These multidimensional metrics capture PCNSL–GBM differences beyond what ADC alone can provide. The edema surrounding PCNSL arises from tumor compression of adjacent brain tissue, venous obstruction, and increased capillary permeability [ 35 ]. In contrast, GBM exhibits infiltrative growth with tumor cell dispersion in peritumoral zones [ 36 , 37 ]. Based on these differences, we explored whether microstructural parameters could differentiate peritumoral regions in PCNSL and GBM. However, no significant differences were observed in any peritumoral t d -dMRI metrics. Previous studies show conflicting results regarding ADC in peritumoral regions: Ko et al. and Cindil et al. reported significantly higher ADC in PCNSL than GBM [ 38 , 39 ], whereas Wang et al. found no significant difference in peritumoral edema [ 8 , 20 , 28 , 40 ]. Taken together, our results suggest that t d -dMRI–based microstructural markers in peritumoral regions may have limited diagnostic value for differentiating these tumors. Histological validation confirmed strong concordance between t d -dMRI parameters and tissue architecture, highlighting its potential to enhance the accuracy of noninvasive diagnostics. Notably, correlation analysis revealed a nonlinear relationship between MRI-derived cell diameter and PGSE-based ADC, suggesting that these metrics provide complementary insights into tumor microstructure. This study has several limitations. First, given the small sample size (n = 19 for PCNSL, n = 32 for GBM), evaluating combined diagnostic models may increase the risk of overfitting, so we limited our analysis to individual parameters. Second, ROIs were manually defined; automated tumor segmentation could reduce inter-operator variability and improve accuracy. Third, histopathologic slides were obtained from limited tumor areas, whereas t d -dMRI parameters were derived from the entire tumor, limiting precise spatial correlation. Finally, we did not integrate t d -dMRI microstructural parameters with other MRI techniques, which might further enhance diagnostic performance. Conclusions Time-dependent diffusion MRI–derived microstructural parameters, particularly V in , demonstrated strong diagnostic performance in differentiating PCNSL from GBM. These findings underscore the clinical relevance of t d -dMRI as a novel tool to refine preoperative differential diagnosis of histopathologically distinct brain tumors. Abbreviations ADC Apparent diffusion coefficient ASL Arterial spin labeling AUC Area under the receiver operating characteristic curve dMRI Diffusion MRI DKI Diffusional kurtosis imaging DMI Diffusion microstructure imaging DTI Diffusion tensor imaging DWI Diffusion weighted imaging GBM Glioblastoma HGG High-grade glioma ICC Intraclass correlation coefficient IMPULSED Imaging microstructural parameters using limited spectrally edited diffusion MD Mean diffusivity N/C Nuclear-to-cytoplasmic NODDI Neurite orientation dispersion and density imaging OGSE Oscillating gradient spin echo PCNSL Primary central nervous system lymphoma PGSE Pulsed gradient spin echo ROC Receiver operating characteristic ROI Region of interest T d -dMRI Time-dependent diffusion MRI Declarations Ethics approval and consent to participate This study adhered to the declaration of Helsinki and received approval from the Institutional Review Board of our institution (Wuhan Union Hospital, NO. UHCT22438). Written informed consent was obtained from all patients in this study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This study has received funding by the National Natural Science Foundation of China (82202230, 82371945). Author Contribution JW and XG: Conceptualization, Supervision, Writing-review & editing; JW and JW: Funding acquisition; XLZ and XTG: Data Curation, Data analysis, Writing-original draft preparation; JQC and QQ: Visualization, Validation; NZ, PS and XG: Writing – review & editing, Methodology, Software; JW and JL: Writing – original draft, Writing – review & editing, Visualization, Formal analysis. All authors read and approved the final manuscript. Acknowledgements No. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to data privacy and intellectual property issues but are available from the corresponding author on reasonable request. 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Presence of apoptosis distinguishes primary central nervous system lymphoma from glioblastoma during intraoperative consultation. Clin Neuropathol. 2018;37:105–11. https://doi.org/10.5414/np301075 . Surov A, Meyer HJ, Wienke A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget. 2017;8:59492–9. https://doi.org/10.18632/oncotarget.17752 . Shim WH, Kim HS, Choi CG, Kim SJ. Comparison of Apparent Diffusion Coefficient and Intravoxel Incoherent Motion for Differentiating among Glioblastoma, Metastasis, and Lymphoma Focusing on Diffusion-Related Parameter. PLoS ONE. 2015;10:e0134761. https://doi.org/10.1371/journal.pone.0134761 . Ozturk K, Soylu E, Cayci Z. Differentiation between primary CNS lymphoma and atypical glioblastoma according to major genomic alterations using diffusion and susceptibility-weighted MR imaging. Eur J Radiol. 2021;141:109784. https://doi.org/10.1016/j.ejrad.2021.109784 . Kickingereder P, Wiestler B, Sahm F, et al. Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology. 2014;272:843–50. https://doi.org/10.1148/radiol.14132740 . Oh J, Cha S, Aiken AH, et al. Quantitative apparent diffusion coefficients and T2 relaxation times in characterizing contrast enhancing brain tumors and regions of peritumoral edema. J Magn Reson Imaging. 2005;21:701–8. https://doi.org/10.1002/jmri.20335 . Claes A, Idema AJ, Wesseling P. Diffuse glioma growth: a guerilla war. Acta Neuropathol. 2007;114:443–58. https://doi.org/10.1007/s00401-007-0293-7 . Giese A, Bjerkvig R, Berens ME, Westphal M. Cost of migration: invasion of malignant gliomas and implications for treatment. J Clin Oncol. 2003;21:1624–36. https://doi.org/10.1200/jco.2003.05.063 . Ko CC, Tai MH, Li CF, et al. Differentiation between Glioblastoma Multiforme and Primary Cerebral Lymphoma: Additional Benefits of Quantitative Diffusion-Weighted MR Imaging. PLoS ONE. 2016;11:e0162565. https://doi.org/10.1371/journal.pone.0162565 . Cindil E, Sendur HN, Cerit MN, et al. Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma. Neuroradiology. 2021;63:331–42. https://doi.org/10.1007/s00234-020-02522-9 . Wang S, Kim S, Chawla S, et al. Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol. 2011;32:507–14. https://doi.org/10.3174/ajnr.A2333 . Additional Declarations No competing interests reported. Supplementary Files ESI.docx suppl.Tabs.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 16 Feb, 2026 Editor assigned by journal 05 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 02 Feb, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8770485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593372774,"identity":"02303ce6-65aa-4571-a803-d94e23924dd2","order_by":0,"name":"Jun Wu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wu","suffix":""},{"id":593372775,"identity":"fc19defa-6c2c-4a98-970b-0e35bdd1272a","order_by":1,"name":"Jue Lu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jue","middleName":"","lastName":"Lu","suffix":""},{"id":593372776,"identity":"6894769e-57a9-4593-b917-76544198dbeb","order_by":2,"name":"Xinli Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinli","middleName":"","lastName":"Zhang","suffix":""},{"id":593372777,"identity":"05edd2a8-537b-4523-85b4-17f48917b556","order_by":3,"name":"Xiaotong Guo","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Guo","suffix":""},{"id":593372778,"identity":"b56b1641-d0c0-4d39-9950-1ef6a4e9a49a","order_by":4,"name":"Qian Qin","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Qin","suffix":""},{"id":593372779,"identity":"e3cc9b1f-d504-44da-8ce9-370704f9939d","order_by":5,"name":"Jiaqi Chen","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Chen","suffix":""},{"id":593372780,"identity":"782195e9-c5c7-493d-9a18-e05742244905","order_by":6,"name":"Ning Zheng","email":"","orcid":"","institution":"MSC Clinical \u0026 Technical Solutions, Philips Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Zheng","suffix":""},{"id":593372781,"identity":"482e1229-8729-48c3-904b-864dd23ce06f","order_by":7,"name":"Peng Sun","email":"","orcid":"","institution":"Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd Shenyang","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Sun","suffix":""},{"id":593372782,"identity":"5ebe85a3-6ecb-4cbe-9787-8060149fa08c","order_by":8,"name":"Xuan Gao","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Gao","suffix":""},{"id":593372783,"identity":"aaf96fbd-1ca9-41ac-a204-e4b40e95982a","order_by":9,"name":"Jing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACCTDJxsPAwHwMKpRAtBa2NCDDgGgtIMBjRpwWg9s9hp8LfvHJmPOv+faY588fBn72HAOGnztwa5Gcc8ZYemYfG4/ljLfbjXnbDBgke94YMPaewa2FXyJ3gzRvDxuPwY2z26R5GwwYDG7kGDAztuHWwiaRu/k3RMuZZ9I8fwwY7AlpAdqyTZrnB1DL+R42aR42oC0SBLRIzsj/Zs3bALKFzUxybpsxj8SZZwUHe/FoMbiRlnyb588xe4Pzh59JvPkjJ8ffnrzxwU88WsCAsQ0Y8RIJYDYPiDhAQAMQ/KkB+ooIdaNgFIyCUTAyAQAySEtOKctmuwAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-02-03 03:38:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8770485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8770485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103166611,"identity":"35985427-76a6-40d0-897d-030a86027f71","added_by":"auto","created_at":"2026-02-22 12:40:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48884,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart shows participant enrollment. PCNSL = primary central nervous system lymphoma, GBM = glioblastoma.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/95f2d46b7e9758d8cc9cfb8f.png"},{"id":103166608,"identity":"ecbc78ca-8fa8-4a3f-acf8-135b54193ef4","added_by":"auto","created_at":"2026-02-22 12:40:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1990517,"visible":true,"origin":"","legend":"\u003cp\u003eDiffusivity MRI maps in 2 participants show PGSE and OGSE data, and microstructural maps. (\u003cstrong\u003ea\u003c/strong\u003e) Diffusivity MRI maps in a 33-year-old woman with GBM; the corresponding relative apparent diffusion coefficient change (rADC\u003csub\u003e1\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e) was 20.80% and 35.02% respectively. (\u003cstrong\u003eb\u003c/strong\u003e) Diffusivity MRI maps in a 58-year-old man with PCNSL; the corresponding rADC\u003csub\u003e1\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e was 18.45% and 34.43% respectively. The table (bottom) shows the corresponding specific values (\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, diameter, cellularity, \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e,\u003csub\u003e \u003c/sub\u003eADC\u003csub\u003e0Hz, \u003c/sub\u003eADC\u003csub\u003e25 Hz, \u003c/sub\u003eADC\u003csub\u003e50Hz\u003c/sub\u003e). ADC\u003csub\u003e0Hz\u003c/sub\u003e = ADC of pulsed gradient spin-echo data, ADC\u003csub\u003e25 Hz\u003c/sub\u003e = ADC measurement at 25 Hz, ADC\u003csub\u003e50Hz\u003c/sub\u003e = ADC measurement at 50 Hz.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/18b9b1e3ebd8815fc5481ba2.png"},{"id":103166605,"identity":"71a32d96-7a4e-48f1-8259-acfdd63a13ca","added_by":"auto","created_at":"2026-02-22 12:40:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50196,"visible":true,"origin":"","legend":"\u003cp\u003eGroup differences between GBM and PCNSL for all MRI markers. (\u003cstrong\u003ea\u003c/strong\u003e)\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, (\u003cstrong\u003eb\u003c/strong\u003e)diameter, (\u003cstrong\u003ec\u003c/strong\u003e)cellularity, (\u003cstrong\u003ed\u003c/strong\u003e)\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, (\u003cstrong\u003ee-g\u003c/strong\u003e)ADC values measured at 0 Hz, 25 Hz, 50 Hz and (\u003cstrong\u003eh-i\u003c/strong\u003e)the relative ADC values. rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e - ADC\u003csub\u003e0Hz\u003c/sub\u003e) / ADC\u003csub\u003e0Hz\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz \u003c/sub\u003e- ADC\u003csub\u003e0Hz\u003c/sub\u003e) / ADC\u003csub\u003e0Hz\u003c/sub\u003e. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001，\u003cem\u003ens \u003c/em\u003eshow no significance.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/da7cde2cf8f17893bac4c18f.png"},{"id":103166609,"identity":"071d5e8c-8d32-4fc8-8361-d9769d4233ed","added_by":"auto","created_at":"2026-02-22 12:40:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51537,"visible":true,"origin":"","legend":"\u003cp\u003eGroup differences between the peritumoral edema of GBM and PCNSL for all MRI markers. (\u003cstrong\u003ea\u003c/strong\u003e)\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, (\u003cstrong\u003eb\u003c/strong\u003e)diameter, (\u003cstrong\u003ec\u003c/strong\u003e)cellularity, (\u003cstrong\u003ed\u003c/strong\u003e)\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, (\u003cstrong\u003ee-g\u003c/strong\u003e)ADC values measured at 0 Hz, 25 Hz, 50 Hz and (\u003cstrong\u003eh-i\u003c/strong\u003e)the relative ADC values. rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ADC\u003csub\u003e0Hz\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ADC\u003csub\u003e0Hz\u003c/sub\u003e. \u003cem\u003ens \u003c/em\u003eshow no significance.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/0960d80540d7ec5f6d3e6869.png"},{"id":103166607,"identity":"9e5326e0-e67f-4481-b21b-66f30344dbf8","added_by":"auto","created_at":"2026-02-22 12:40:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50062,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of MRI markers. ADC\u003csub\u003e0Hz\u003c/sub\u003e= ADC measurement at 0 Hz, ADC\u003csub\u003e25Hz\u003c/sub\u003e = ADC measurement at 25 Hz, ADC\u003csub\u003e50Hz\u003c/sub\u003e = ADC measurement at 50 Hz, rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e) /ADC\u003csub\u003e0Hz\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ ADC\u003csub\u003e0Hz\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/893f20666235dcc8f86f636c.png"},{"id":103166612,"identity":"5b5f2793-1aef-4d2c-b7ea-4f36ea6ccacf","added_by":"auto","created_at":"2026-02-22 12:40:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1090460,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between pathologic findings and the microstructural mapping results. (\u003cstrong\u003ea\u003c/strong\u003e) Hematoxylin and eosin (H\u0026amp;E)–stained slices (magnification, ×100) of a GBM (n = 29) and a PCNSL (n = 14) patients. Nuclei was automatically quantified with a conditional generative adversarial network. (\u003cstrong\u003eb\u003c/strong\u003e) The graph shows correlation between \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e from IMPULSED model and pathologic examination–based f\u003csub\u003enuclei\u003c/sub\u003e in 43 participants.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/99768361206e10aa77dd994f.png"},{"id":103504315,"identity":"368e7449-5718-4b02-8024-13de721d27cc","added_by":"auto","created_at":"2026-02-26 13:19:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4155477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/9b1b40f3-c683-4a05-a570-57d33f2a101f.pdf"},{"id":103166613,"identity":"de1243db-438a-469f-a143-a0053836a310","added_by":"auto","created_at":"2026-02-22 12:40:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":231727,"visible":true,"origin":"","legend":"","description":"","filename":"ESI.docx","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/712bfa0a2d7c494d806c5ad0.docx"},{"id":103166606,"identity":"d8b19654-c660-4b10-ae60-8f7ae60cbda5","added_by":"auto","created_at":"2026-02-22 12:40:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14999,"visible":true,"origin":"","legend":"","description":"","filename":"suppl.Tabs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8770485/v1/ed58c550d5864c4eca0ba5f6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are among the most prevalent malignant primary brain tumors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The standard treatment for GBM is surgical resection followed by concurrent chemoradiotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For PCNSL, the first-line regimen is high-dose methotrexate\u0026ndash;based combination therapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Surgical resection in PCNSL not only delays chemotherapy but also increases the risk of surgery-related complications or rapid disease progression. Therefore, accurate preoperative differentiation is vital for guiding optimal therapeutic strategies.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) is the most common noninvasive tool for the preoperative diagnosis of GBM and PCNSL. In contrast-enhanced T1-weighted imaging (CE-T1WI), GBM typically shows heterogeneous ring enhancement with central necrosis, whereas PCNSL often presents as homogeneously enhancing masses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, considerable overlap exists in their imaging features, and atypical manifestations of either GBM or PCNSL increase the difficulty of differential diagnosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent studies have shown that multiparametric MRI techniques improve the differentiation of complex cases. Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) have demonstrated good diagnostic performance in distinguishing PCNSL from GBM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, ADC values are influenced by multiple pathological factors, including inflammation, cell proliferation, and necrosis, and therefore cannot provide accurate information on specific tumor cell microstructures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTime-dependent diffusion MRI (t\u003csub\u003ed\u003c/sub\u003e-dMRI) is an emerging technique developed from conventional pulsed gradient spin-echo (PGSE) DWI. Constrained by gradient strength, PGSE probes diffusion over relatively long diffusion times and is more sensitive to length scales\u0026thinsp;\u0026gt;\u0026thinsp;13 \u0026micro;m, which exceed typical axon and cell sizes in the central nervous system (CNS) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By introducing oscillation frequency into gradient pulses, oscillating gradient spin-echo (OGSE) sequences shorten the effective diffusion time of measurable water molecules and enhance sensitivity to smaller spatial scales of restriction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By combining measurements across a range of diffusion times, typically achieved with PGSE and OGSE, t\u003csub\u003ed\u003c/sub\u003e-dMRI acquires diffusion signals across different scales, thereby capturing microscopic structural changes such as cell diameter, cellularity, and intracellular volume fraction (\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e), along with other cellular-level microstructural features [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, t\u003csub\u003ed\u003c/sub\u003e-dMRI has shown great potential in tumor diagnosis and differentiation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], histological and molecular classification [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and prediction of prognostic features and treatment response [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the distinct pathological profiles of GBM and PCNSL, we hypothesized that t\u003csub\u003ed\u003c/sub\u003e-dMRI-derived microstructural markers would differentiate these entities, with findings correlating with histopathologic features. This study aimed to evaluate the ability of t\u003csub\u003ed\u003c/sub\u003e-dMRI-based microstructural parameters to differentiate PCNSL from GBM and to further investigate their diagnostic performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e The study received approval from the Institutional Review Board of our institution (Wuhan Union Hospital, NO. UHCT22438) and written informed consent was obtained from all patients. Between June 2021 and March 2025, 66 patients suspected of PCNSL or GBM who underwent preoperative t\u003csub\u003ed\u003c/sub\u003e-dMRI were enrolled. Inclusion required (1) pathological confirmation of either tumor type, and (2) age\u0026thinsp;\u0026gt;\u0026thinsp;18 years. The exclusion criteria were as follows: (1) absence of any required MRI sequence (T1WI, T2-FLAIR, 3D-enhanced T1WI, DWI with both OGSE and PGSE) or images with severe artifacts; (2) lesion size\u0026thinsp;\u0026lt;\u0026thinsp;1 cm; and (3) prior antitumor therapy before MRI. For patients with multiple lesions, the largest lesion was analyzed. The enrollment flowchart is shown in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Flowchart shows participant enrollment. PCNSL\u0026thinsp;=\u0026thinsp;primary central nervous system lymphoma, GBM\u0026thinsp;=\u0026thinsp;glioblastoma.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData acquisition\u003c/h3\u003e\n\u003cp\u003eAll scans were performed on a 3.0-T Ingenia CX Philips scanner (80 mT/m gradient, 200 mT/m/ms switching rate) with a 32-channel head coil. Routine structural MRI included T1WI, T2-FLAIR, and 3D-enhanced T1WI, with a total scan duration of 17 minutes.\u003c/p\u003e \u003cp\u003eT\u003csub\u003ed\u003c/sub\u003e-dMRI integrated PGSE and OGSE sequences with different diffusion times to assess diffusion time dependence of microstructural components, as previously described [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. OGSE was acquired at oscillating frequencies of 25 Hz (effective t\u003csub\u003ed\u003c/sub\u003e = 12 ms, 1 cycle, b\u0026thinsp;=\u0026thinsp;0/250/500/800/1200 s/mm\u003csup\u003e2\u003c/sup\u003e) and 50 Hz (effective t\u003csub\u003ed\u003c/sub\u003e = 6 ms, 2 cycles, b\u0026thinsp;=\u0026thinsp;0/100/200/300 s/mm\u003csup\u003e2\u003c/sup\u003e). PGSE was acquired with diffusion duration/separation\u0026thinsp;=\u0026thinsp;15.9/77.3 ms at b-value of 0/250/500/1000/1500 s/mm\u003csup\u003e2\u003c/sup\u003e, for OGSE corresponding diffusion duration/separation values of 40.9/48.8 ms. See Supplementary Text for details.\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eOGSE datasets were coregistered to PGSE using b0 images in ITK-SNAP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.itksnap.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.itksnap.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to correct for the patient motion. Two trainee radiologists (J.W., J.L.) manually delineated regions of interest (ROIs) on PGSE images under the supervision of an expert radiologist (J.W., 20 years\u0026rsquo; experience in neuroradiology). Areas with necrosis, cysts, and hemorrhage were carefully excluded. The solid tumor was defined as either the contrast-enhanced region on 3D-enhanced T1WI or the hyperintense region on T2-FLAIR with low signal intensity on PGSE (when contrast enhancement was absent). Peritumoral edema was delineated as the non-enhancing T2-FLAIR hyperintense area within 10 mm of the tumor margin, as previously described [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicrostructural parameters derived from the IMPULSED (improved microstructural parameters using limited spectrally edited diffusion) model were assessed by two radiology trainees (J.W., J.L.), blinded to each other\u0026rsquo;s findings. The principle of the IMPULSED scheme is described in Supplementary Text. \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, cell diameter, cellularity, and extracellular diffusivity (\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e) were calculated using the IMPULSED model in MATLAB (version 2024b, MathWorks, USA).\u003c/p\u003e\n\u003ch3\u003eHistopathological Analysis\u003c/h3\u003e\n\u003cp\u003eHigh-definition (100\u0026times;) hematoxylin and eosin (H\u0026amp;E)-stained histopathological slides were obtained from 29 patients of GBM and 14 patients of PCNSL. Nuclei in each H\u0026amp;E section were segmented using a pre-trained conditional generative adversarial network [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Pathology-based intracellular volume fraction (\u003cem\u003ef\u003csub\u003ei\u003c/sub\u003eₙ\u003c/em\u003e) was computed as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:fᵢₙ={\\left(\\sum\\:_{n}{A}_{nuclei}/{A}_{tissue}\\right)}^{3/2}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003enuclei\u003c/em\u003e\u003c/sub\u003e represented the area of the segmented nucleus and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003etissue\u003c/em\u003e\u003c/sub\u003e standed for the total area of the entire tissue section [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS (version 26.0) and GraphPad Prism (version 10.0). Interobserver agreement was assessed with the intraclass correlation coefficient (ICC). ICC\u0026thinsp;\u0026lt;\u0026thinsp;0.5, 0.5\u0026ndash;0.75, 0.75\u0026ndash;0.9, \u0026gt; 0.9 indicated poor, moderate, good and excellent reproducibility, respectively [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Measurements from the two observers were averaged for each case and used for further analysis. Continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and categorical variables were expressed as frequencies. Group differences were evaluated using the independent \u003cem\u003et\u003c/em\u003e-test for normally distributed data or the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test for non-normally distributed data. Categorical variables were compared using the chi-squared test. Receiver operating characteristic (ROC) analysis was performed to determine optimal thresholds and to calculate the area under the curve (AUC), sensitivity, specificity, and accuracy for differential diagnosis. Spearman correlation analysis was conducted to assess associations between variables. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinic information\u003c/h2\u003e \u003cp\u003eA total of 66 consecutive patients were considered for inclusion. 15 patients were excluded due to incomplete scan of preoperative MRI sequences or severe artifacts and prior surgical resection. Finally, 32 patients with GBM (20 men, 12 women) and 19 with PCNSL (10 men, 9 women) were analyzed. There was no significant difference in mean age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.785) or sex distribution (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.489). Tumor characteristics for both groups are summarized in Supplement Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. ICCs with 95% confidence intervals (CI) for each t\u003csub\u003ed\u003c/sub\u003e-dMRI-derived microstructural parameter are presented in Supplement Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, demonstrating excellent interobserver agreement for all metrics.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of t-dMRI-based microstructural markers between PCNSLs and GBMs\u003c/h3\u003e\n\u003cp\u003eRepresentative t\u003csub\u003ed\u003c/sub\u003e-dMRI microstructural maps of enhancing tumor regions from one patient with PCNSL and one with GBM are shown in Fig.\u0026nbsp;2. Except for ADC at 50 Hz, all parameters exhibited significant group differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;3). Specifically, GBM showed higher cell diameter, \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, and ADC values at 0 Hz and 25 Hz, while PCNSL demonstrated elevated relative ADC changes (rADC\u003csub\u003e1\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e), \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, and cellularity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In both tumor types, ADC maps demonstrated a time-dependent increase from 0 Hz to 50 Hz, with ADC values at 25 Hz and 50 Hz significantly higher than those at 0 Hz (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-b).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e Diffusivity MRI maps in 2 participants show PGSE and OGSE data, and microstructural maps. (\u003cb\u003ea\u003c/b\u003e) Diffusivity MRI maps in a 33-year-old woman with GBM; the corresponding relative apparent diffusion coefficient change (rADC\u003csub\u003e1\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e) was 20.80% and 35.02% respectively. (\u003cb\u003eb\u003c/b\u003e) Diffusivity MRI maps in a 58-year-old man with PCNSL; the corresponding rADC\u003csub\u003e1\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e was 18.45% and 34.43% respectively. The table (bottom) shows the corresponding specific values (\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, diameter, cellularity, \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, ADC\u003csub\u003e0Hz,\u003c/sub\u003e ADC\u003csub\u003e25 Hz,\u003c/sub\u003e ADC\u003csub\u003e50Hz\u003c/sub\u003e). ADC\u003csub\u003e0Hz\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ADC of pulsed gradient spin-echo data, ADC\u003csub\u003e25 Hz\u003c/sub\u003e = ADC measurement at 25 Hz, ADC\u003csub\u003e50Hz\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ADC measurement at 50 Hz.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3\u003c/b\u003e Group differences between GBM and PCNSL for all MRI markers. (\u003cb\u003ea\u003c/b\u003e)\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, (\u003cb\u003eb\u003c/b\u003e)diameter, (\u003cb\u003ec\u003c/b\u003e)cellularity, (\u003cb\u003ed\u003c/b\u003e)\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, (\u003cb\u003ee-g\u003c/b\u003e)ADC values measured at 0 Hz, 25 Hz, 50 Hz and (\u003cb\u003eh-i\u003c/b\u003e)the relative ADC values. rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e - ADC\u003csub\u003e0Hz\u003c/sub\u003e) / ADC\u003csub\u003e0Hz\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz\u003c/sub\u003e - ADC\u003csub\u003e0Hz\u003c/sub\u003e) / ADC\u003csub\u003e0Hz\u003c/sub\u003e. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ens\u003c/em\u003e show no significance.\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\u003eComparison of t\u003csub\u003ed\u003c/sub\u003e-MRI\u0026ndash;derived microstructural parameters for distinguishing the tumor region of PCNSL and GBM patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCNSL(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGBM(n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter\u003csup\u003e\u0026sect;\u003c/sup\u003e (\u0026micro;m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellularity\u003csup\u003e\u0026sect;\u003c/sup\u003e (\u0026micro;m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e\u003csup\u003e#\u003c/sup\u003e (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e/msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADC\u003csub\u003e0Hz\u003c/sub\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e/msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADC\u003csub\u003e25Hz\u003c/sub\u003e\u003csup\u003e#\u003c/sup\u003e (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e/msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADC\u003csub\u003e50Hz\u003c/sub\u003e\u003csup\u003e#\u003c/sup\u003e (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e/msec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADC\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADC\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e37.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e = intracellular fraction, \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e = extracellular diffusivity, ADC\u0026thinsp;=\u0026thinsp;apparent diffusion coefficient, ADC\u003csub\u003e0Hz\u003c/sub\u003e= ADC measurement at 0 Hz, ADC\u003csub\u003e25Hz\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ADC measurement at 25 Hz, ADC\u003csub\u003e50Hz\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ADC measurement at 50 Hz, rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ADC\u003csub\u003e0Hz,\u003c/sub\u003e rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ADC\u003csub\u003e0Hz\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e \u003cem\u003eIndependent sample t-test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e#\u003c/sup\u003e \u003cem\u003eMann\u0026ndash;Whitney U test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRepresentative t\u003csub\u003ed\u003c/sub\u003e-dMRI-derived microstructural maps of the peritumoral region from patients with PCNSL and GBM are shown in Fig.\u0026nbsp;2. In both tumor types, ADC values at 50 Hz were significantly higher than those at 0 Hz and 25 Hz (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec-d). However, no significant differences in any t\u003csub\u003ed\u003c/sub\u003e-dMRI metrics were observed between PCNSL and GBM in the peritumoral regions (Fig.\u0026nbsp;4, Supplementary Table S3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e Group differences between the peritumoral edema of GBM and PCNSL for all MRI markers. (\u003cb\u003ea\u003c/b\u003e)\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, (\u003cb\u003eb\u003c/b\u003e)diameter, (\u003cb\u003ec\u003c/b\u003e)cellularity, (\u003cb\u003ed\u003c/b\u003e)\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, (\u003cb\u003ee-g\u003c/b\u003e)ADC values measured at 0 Hz, 25 Hz, 50 Hz and (\u003cb\u003eh-i\u003c/b\u003e)the relative ADC values. rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ADC\u003csub\u003e0Hz\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ADC\u003csub\u003e0Hz\u003c/sub\u003e. \u003cem\u003ens\u003c/em\u003e show no significance.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance in differentiating PCNSLs from GBMs\u003c/h2\u003e \u003cp\u003eThe results of the ROC curve analysis are summarized in Supplementary Table S4 In enhancing regions, all MRI parameters demonstrated reasonable diagnostic accuracy in differentiating PCNSL from GBM. \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e showed the highest diagnostic performance, with an AUC of 0.901 (95% CI: 0.815\u0026ndash;0.987, sensitivity: 0.737, specificity: 0.906, accuracy: 0.843), followed by cellularity, rADC\u003csub\u003e2\u003c/sub\u003e, and rADC\u003csub\u003e1\u003c/sub\u003e, with AUCs of 0.885, 0.878, and 0.873, respectively. ADC at 50 Hz showed lower discriminative power compared with the above t\u003csub\u003ed\u003c/sub\u003e-dMRI indices, with an AUC of 0.640. The corresponding ROC curves are presented in Fig.\u0026nbsp;5.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;5\u003c/b\u003e ROC curves of MRI markers. ADC\u003csub\u003e0Hz\u003c/sub\u003e= ADC measurement at 0 Hz, ADC\u003csub\u003e25Hz\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ADC measurement at 25 Hz, ADC\u003csub\u003e50Hz\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ADC measurement at 50 Hz, rADC\u003csub\u003e1\u003c/sub\u003e = (ADC\u003csub\u003e25Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e) /ADC\u003csub\u003e0Hz\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e = (ADC\u003csub\u003e50Hz\u003c/sub\u003e -ADC\u003csub\u003e0Hz\u003c/sub\u003e)/ ADC\u003csub\u003e0Hz\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHistopathologic validation\u003c/h2\u003e \u003cp\u003eH\u0026amp;E-stained sections were used to validate t\u003csub\u003ed\u003c/sub\u003e-dMRI parameters (Fig.\u0026nbsp;6a and 6b). The IMPULSED-derived \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e correlated strongly with the volume fraction quantified from the segmented nuclei (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003enuclei\u003c/em\u003e\u003c/sub\u003e) (r\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; n\u0026thinsp;=\u0026thinsp;43). Cellularity was closely associated with ADC values at 0 Hz, whereas td-dMRI\u0026ndash;derived cell diameter showed poor correlation with them, likely due to glioma cell pleomorphism in GBM (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;6\u003c/b\u003e Correlation between pathologic findings and the microstructural mapping results. (\u003cb\u003ea\u003c/b\u003e) Hematoxylin and eosin (H\u0026amp;E)\u0026ndash;stained slices (magnification, \u0026times;100) of a GBM (n\u0026thinsp;=\u0026thinsp;29) and a PCNSL (n\u0026thinsp;=\u0026thinsp;14) patients. Nuclei was automatically quantified with a conditional generative adversarial network. (\u003cb\u003eb\u003c/b\u003e) The graph shows correlation between \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e from IMPULSED model and pathologic examination\u0026ndash;based f\u003csub\u003enuclei\u003c/sub\u003e in 43 participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that t\u003csub\u003ed\u003c/sub\u003e-dMRI\u0026ndash;derived parameters revealed distinct group differences, with significantly lower \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, diameter, \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, and ADC values, and higher cellularity and relative ADC changes (rADC\u003csub\u003e1\u003c/sub\u003e, rADC\u003csub\u003e2\u003c/sub\u003e) in PCNSL patients. Among these, \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e showed superior diagnostic performance, underscoring t\u003csub\u003ed\u003c/sub\u003e-dMRI\u0026rsquo;s potential to differentiate PCNSL from GBM. These microstructural markers (\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, cellularity) reflected specific pathologic features, indicating t\u003csub\u003ed\u003c/sub\u003e-dMRI\u0026rsquo;s capacity to provide cellular- or even subcellular-level insights.\u003c/p\u003e \u003cp\u003eGiven the divergent therapeutic strategies, accurate differentiation between PCNSL and GBM is critical for optimizing patient outcomes\u0026mdash;timely high-dose methotrexate for PCNSL versus maximal tumor resection for GBM. Prior studies have evaluated advanced MRI techniques for this purpose. Yu et al. reported that multiparametric models incorporating relative mean ADC (rADC(mean)), maximum cerebral blood flow (CBF(max)), and non-enhancing tumor volume (nET) achieved superior diagnostic performance compared with single- or two-variable models (AUC 0.96, sensitivity 90%, specificity 96.55%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Diffusional kurtosis imaging (DKI)\u0026ndash;derived water kurtosis and diffusional metrics have been shown to correlate with nuclear-to-cytoplasmic ratio, highlighting DKI\u0026rsquo;s value in distinguishing PCNSL from high-grade gliomas (HGGs) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Multiparametric MRI\u0026ndash;based radiomics and deep learning approaches have also yielded strong diagnostic performance (AUC 0.899 and 0.977, respectively) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], although these methods cannot directly characterize microstructural features. W\u0026uuml;rtemberger et al. further demonstrated that diffusion microstructure imaging (DMI) combined with diffusion-weighted imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) achieved high predictive values in separating PCNSL from GBM, with mean diffusivity (MD) and intracellular volume fraction (ICVF) performing best (AUC 0.960) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, these approaches mainly emphasized axonal-related features rather than microstructural characteristics of tumor cells and did not account for diffusion-time effects, a unique advantage of t\u003csub\u003ed\u003c/sub\u003e-dMRI.\u003c/p\u003e \u003cp\u003eOur findings demonstrated that t\u003csub\u003ed\u003c/sub\u003e-dMRI\u0026ndash;derived microstructural indices not only effectively distinguished PCNSL from GBM but also revealed multifaceted tissue-level disparities. Among all MRI markers, \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e showed the highest diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.901), with PCNSL exhibiting significantly higher \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e than GBM. This finding was consistent with Haopeng P et al., who reported a higher N/C ratio in PCNSL compared with HGGs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], suggesting that the elevated N/C ratio may underlie the increased \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e observed in PCNSL. PCNSL also demonstrated higher cellularity than GBM. The mean cell diameter in PCNSL (13.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 \u0026micro;m) was smaller than in GBM (14.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 \u0026micro;m), aligning with reported ranges of 10\u0026ndash;20 \u0026micro;m for PCNSL and 10\u0026ndash;33 \u0026micro;m for GBM [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e which reflects extracellular space architecture was lower in PCNSL than in GBM. This is attributable to the uniform high cellular density and compact extracellular space of PCNSL, in contrast with the heterogeneous moderate cellularity and medium-sized extracellular space of GBM, influenced by extracellular matrix components, hemorrhage, and necrosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These structural distinctions explain the lower \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e in PCNSL, as increased cellular density and reduced extracellular space restrict free molecular diffusion.\u003c/p\u003e \u003cp\u003eADC values reflect water diffusional restriction and can indicate tumor cellularity, necrosis, and cystic degeneration [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Several studies have reported that ADC values within enhancing regions of PCNSL are generally lower than those of GBM, due to higher tumor cell density and N/C ratio [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our results corroborated these findings: ADC\u003csub\u003e0Hz\u003c/sub\u003e and ADC\u003csub\u003e25Hz\u003c/sub\u003e were useful for distinguishing PCNSL from GBM. Notably, ADC values increased as diffusion time decreased in both tumor types, highlighting the time-dependent nature of diffusion. However, ADC\u003csub\u003e50Hz\u003c/sub\u003e lacked discriminatory value, suggesting that shorter diffusion times may reduce sensitivity to microstructural differences. This emphasizes the importance of selecting optimized effective diffusion times in clinical DWI. Kamimura et al. recently demonstrated that t\u003csub\u003ed\u003c/sub\u003e-dMRI parameters may aid in differentiating PCNSL from GBM, with mean relative ADC changes achieving the highest discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.920) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, their method relied on ADC values derived from OGSE (Δ\u003csub\u003eeff\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.1 ms) and conventional PGSE (Δ\u003csub\u003eeff\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;44.5 ms), which are not directly linked to tumor microstructural features. In contrast, our study applied OGSE at 25 Hz and 50 Hz with multiple b values, integrating IMPULSED-modeled parameters (diameter, cellularity, \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e) to characterize tumor microstructure. These multidimensional metrics capture PCNSL\u0026ndash;GBM differences beyond what ADC alone can provide.\u003c/p\u003e \u003cp\u003eThe edema surrounding PCNSL arises from tumor compression of adjacent brain tissue, venous obstruction, and increased capillary permeability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast, GBM exhibits infiltrative growth with tumor cell dispersion in peritumoral zones [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Based on these differences, we explored whether microstructural parameters could differentiate peritumoral regions in PCNSL and GBM. However, no significant differences were observed in any peritumoral t\u003csub\u003ed\u003c/sub\u003e-dMRI metrics. Previous studies show conflicting results regarding ADC in peritumoral regions: Ko et al. and Cindil et al. reported significantly higher ADC in PCNSL than GBM [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], whereas Wang et al. found no significant difference in peritumoral edema [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Taken together, our results suggest that t\u003csub\u003ed\u003c/sub\u003e-dMRI\u0026ndash;based microstructural markers in peritumoral regions may have limited diagnostic value for differentiating these tumors.\u003c/p\u003e \u003cp\u003eHistological validation confirmed strong concordance between t\u003csub\u003ed\u003c/sub\u003e-dMRI parameters and tissue architecture, highlighting its potential to enhance the accuracy of noninvasive diagnostics. Notably, correlation analysis revealed a nonlinear relationship between MRI-derived cell diameter and PGSE-based ADC, suggesting that these metrics provide complementary insights into tumor microstructure.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, given the small sample size (n\u0026thinsp;=\u0026thinsp;19 for PCNSL, n\u0026thinsp;=\u0026thinsp;32 for GBM), evaluating combined diagnostic models may increase the risk of overfitting, so we limited our analysis to individual parameters. Second, ROIs were manually defined; automated tumor segmentation could reduce inter-operator variability and improve accuracy. Third, histopathologic slides were obtained from limited tumor areas, whereas t\u003csub\u003ed\u003c/sub\u003e-dMRI parameters were derived from the entire tumor, limiting precise spatial correlation. Finally, we did not integrate t\u003csub\u003ed\u003c/sub\u003e-dMRI microstructural parameters with other MRI techniques, which might further enhance diagnostic performance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTime-dependent diffusion MRI\u0026ndash;derived microstructural parameters, particularly \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, demonstrated strong diagnostic performance in differentiating PCNSL from GBM. These findings underscore the clinical relevance of t\u003csub\u003ed\u003c/sub\u003e-dMRI as a novel tool to refine preoperative differential diagnosis of histopathologically distinct brain tumors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApparent diffusion coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArterial spin labeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion MRI\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusional kurtosis imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion microstructure imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion tensor imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDWI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlioblastoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-grade glioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraclass correlation coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMPULSED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImaging microstructural parameters using limited spectrally edited diffusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean diffusivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eN/C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNuclear-to-cytoplasmic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNODDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeurite orientation dispersion and density imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOGSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOscillating gradient spin echo\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCNSL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary central nervous system lymphoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePGSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulsed gradient spin echo\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT\u003csub\u003ed\u003c/sub\u003e-dMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTime-dependent diffusion MRI\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study adhered to the declaration of Helsinki and received approval from the Institutional Review Board of our institution (Wuhan Union Hospital, NO. UHCT22438). Written informed consent was obtained from all patients in this study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study has received funding by the National Natural Science Foundation of China (82202230, 82371945).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJW and XG: Conceptualization, Supervision, Writing-review \u0026amp; editing; JW and JW: Funding acquisition; XLZ and XTG: Data Curation, Data analysis, Writing-original draft preparation; JQC and QQ: Visualization, Validation; NZ, PS and XG: Writing \u0026ndash; review \u0026amp; editing, Methodology, Software; JW and JL: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Visualization, Formal analysis. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNo.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to data privacy and intellectual property issues but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiller KD, Ostrom QT, Kruchko C, et al. Brain and other central nervous system tumor statistics. 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AJNR Am J Neuroradiol. 2011;32:507\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3174/ajnr.A2333\u003c/span\u003e\u003cspan address=\"10.3174/ajnr.A2333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Time-dependent diffusion MRI, primary central nervous system lymphoma, glioblastoma, microstructure","lastPublishedDoi":"10.21203/rs.3.rs-8770485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8770485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are two different types of malignant brain tumors, precise preoperative differentiation is crucial for guiding optimal treatments. This study aimed to evaluate the diagnostic utility of time-dependent diffusion MRI (t\u003csub\u003ed\u003c/sub\u003e-dMRI)-derived microstructural parameters in differentiating PCNSL from GBM and to correlate these parameters with histopathologic findings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 32 GBM and 19 PCNSL patients who underwent 3.0-T MRI with oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences. Microstructural parameters [intracellular volume fraction (\u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e), cell diameter, cellularity, extracellular diffusivity (\u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e)] were compared between groups. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of these indices. Histopathologic validation was performed by correlating t\u003csub\u003ed\u003c/sub\u003e-dMRI parameters with hematoxylin-eosin (H\u0026amp;E)\u0026ndash;stained sections.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn enhancing tumor regions, PCNSL showed significantly lower cell diameter and \u003cem\u003eD\u003c/em\u003e\u003csub\u003eex\u003c/sub\u003e, but higher \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e and cellularity than GBM (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e demonstrated the highest diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.901; sensitivity\u0026thinsp;=\u0026thinsp;0.737; specificity\u0026thinsp;=\u0026thinsp;0.906). No significant differences were observed in peritumoral regions. \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e correlated strongly with histopathologic nuclear volume fraction (r\u0026thinsp;=\u0026thinsp;0.76; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eT\u003csub\u003ed\u003c/sub\u003e-dMRI-derived microstructural parameters, particularly \u003cem\u003eV\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e, effectively differentiated PCNSL from GBM, providing a novel approach to improve preoperative diagnosis.\u003c/p\u003e","manuscriptTitle":"Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:40:29","doi":"10.21203/rs.3.rs-8770485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T17:01:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T21:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155597004868341358189553788570373939631","date":"2026-02-16T14:07:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-16T14:03:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T13:09:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T11:35:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Imaging","date":"2026-02-03T03:32:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d722841-1d63-48b4-bb69-5ef598de6793","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T05:10:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:40:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8770485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8770485","identity":"rs-8770485","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}