Differentiation of Medulloblastoma Molecular Subtypes Using Multiparametric MRI and Texture Analysis

Differentiation of Medulloblastoma Molecular Subtypes Using Multiparametric MRI and Texture Analysis | 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 Differentiation of Medulloblastoma Molecular Subtypes Using Multiparametric MRI and Texture Analysis Bulent Aslan, Erhan Biyikli, Tahsin Aybal, Gulnur Tokuc, Suheyla Bozkurt, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7337273/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Medulloblastoma, the most common malignant pediatric brain tumor, has distinct molecular subtypes that vary in prognosis and treatment response. Accurate preoperative identification of these subtypes is crucial for optimizing therapy. Objective: The aim of our study was to differentiate medulloblastoma molecular subtypes using multiparametric MRI, including MRI-based texture analysis. Materials and methods: Fifty-eight patients with preoperative MRI and histopathological diagnoses of medulloblastoma were included. The patients were divided into SHH pathway active and group 3/group 4 subtypes. Morphological MRI findings, ADC measurements, and texture analysis features were compared between the groups. Results: Of the 58 patients, 55.2% had SHH-active tumors. Morphological features, including location out of the midline or in the cerebellar hemisphere (p<0.001), peri-tumoral edema (p=0.041), macrocysts (p=0.001), nodular involvement/lobulation (p=0.002), and heterogeneous contrast enhancement (p=0.002) were more common in SHH tumors. ADC measurements showed that the solid tumor-to-thalamus ratio was significantly lower in SHH tumors (p<0.001), with a threshold of 0.855 providing 82.1% sensitivity and 92.3% specificity. As for texture analysis parameters, kurtosis (p=0.023), SumOfSqs (p=0.022) and 01-10-50-90% percentile (p=0.011; p=0.001; p=0.006; and p=0.013 respectively) values obtained from ADC images and kurtosis (p=0.041), SumOfSqs (p=0.005), SumVarnc (p=0.014), SumEntrp (p=0.032) values obtained from T1W images were statistically significant in differentiating SHH and group 3/ group 4 medulloblastoma. Conclusion: Combining MRI morphological findings, ADC measurements, and texture analysis offers valuable diagnostic information for distinguishing medulloblastoma molecular subtypes. medulloblastoma ADC measurement texture analysis brain MRI Sonic hedgehog Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Medulloblastoma (MB) is the most common embryonal brain tumor, leading to significant morbidity and high mortality in children [1]. Initially viewed as a single disease, recent molecular research has revealed that MB is composed of at least four distinct subgroups: SHH, WNT, Group 3, and Group 4. These subgroups have unique biological, prognostic, and therapeutic characteristics [2-4]. Tumor classification is typically performed on tissue samples obtained during surgery or biopsy. Unlike high-grade gliomas, MBs exhibit a uniform genetic profile throughout the tumor, meaning even a small biopsy can accurately determine the subgroup [5]. However, obtaining these samples is an invasive procedure with inherent risks for the patient. Despite the clear benefits of subtyping, its integration into clinical practice has been slow due to the high costs and lack of access to the advanced methods required for accurate analysis [4]. While radiomics has been underutilized for MB, a number of studies have used magnetic resonance imaging (MRI) to qualitatively assess and categorize MBs based on their molecular characteristics. These studies have demonstrated that the location and contrast-enhancement patterns of tumors vary among the different MB subgroups [6-11]. For example: Group 3 and Group 4 MBs usually develop in the midline of the brain. SHH tumors are most frequently found in the cerebellar hemispheres. WNT tumors can be found in the midline, cerebellar peduncle, or cerebellopontine cisterns [6, 8, 11]. Additionally, the absence of contrast enhancement on an MRI is a strong indicator of a Group 4 tumor, while intense enhancement in non-WNT/SHH tumors is associated with a poorer prognosis [11]. MRI-based texture analysis is a quantitative technique that provides insights into a tumor's heterogeneity [12, 13]. This method uses a series of mathematical features to analyze specific regions within an image, generating objective data about tissue characteristics [14]. These features are generally categorized into three types based on their complexity: First-order features (least complex): These are single statistical measures that describe the tissue of an entire volume. Examples include mean intensity, standard deviation (SD), entropy, kurtosis, and skewness. Second-order features: These describe the relationship between two points, such as pixels or voxels, within an image. They can provide information about a tumor's three-dimensional shape, size, and the range of values within it. Common examples include entropy, compactness, sphericity, and the surface-to-volume ratio. Higher-order features (most complex): These are more intricate and define the relationships between three or more points in space [15]. The development of non-invasive imaging biomarkers for MB subgroups could give physicians a valuable non-surgical way to understand a tumor's genetic makeup. This would significantly help with clinical prognostication and enable more informed treatment decisions. This study aims to explore how multiparametric MRI, including advanced texture analysis, can be used to differentiate the molecular subtypes of medulloblastoma. MATERIALS AND METHODS This retrospective study was conducted after institutional review board approval. The requirement for informed consent was waived given the retrospective nature of the study. Study Design Patients with pathologically proven medulloblastomas who underwent posterior fossa surgery or biopsy at a tertiary referral center between January 2012 and August 2020 were retrospectively reviewed. A total of 91 patients were identified with a pathologic diagnosis of medulloblastoma. Thirty-three patients were excluded from the study due to either not having molecular subtyping, non-diagnostic MRI due to motion/technical artifacts, or lack of preoperative MRI at our center. Of the excluded patients, only two had a molecular subtype diagnosis of WNT medulloblastoma. Fifty-eight patients who had preoperative MRIs and were diagnosed with SHH/Group 3-4 molecular subtype medulloblastoma were included in our study. The patients were divided into two groups: Group 3-4 and SHH pathway-active medulloblastoma. Brain MRI findings, ADC measurements, and texture analysis characteristics of each group were compared. Brain MRI Protocol Brain MRI exams of the patients were performed in the local hospital using 1.5 T (Avanto) or 3 T (Verio) Siemens MRI machines. Our routine brain MRI protocols included T1-weighted spin-echo (TR = 450-515 ms, TE = 10-15 ms, flip angle = 90°, FOV = 200-260 mm), T2-weighted spin-echo (TR = 4200-4650 ms, TE = 90-100 ms, flip angle = 150°, FOV = 200-260 mm), T2-FLAIR (TR = 7000-8000 ms, TE = 84-100 ms, flip angle = 150°, FOV = 200-260 mm), and axial diffusion-weighted imaging (DWI) with b-values of 0 and 1000 s/mm². Calculated apparent diffusion coefficient (ADC) maps were also obtained. Slice thickness varied between 3-4 mm. Post-contrast T1-weighted images were obtained similarly to pre-contrast images, with the addition of fat suppression and sagittal plane imaging. The presence of hemorrhage/calcification in 31 patients was evaluated using the SWI sequence or with available CT of the head. Diffusion-weighted imaging was not available for four patients, and post-contrast T1-weighted imaging was unavailable for three patients. Lesion Evaluation Brain MRI images were initially evaluated blind to clinical information and postoperative histopathological diagnoses. On brain MRI, tumor localization, contrast enhancement pattern (heterogenous vs homogenous or minimal/non-enhancing), margins, T2 signal relative to cerebral gray matter, the presence of vasogenic edema, hemorrhage/calcification, microcysts (1 cm), nodularity/lobulation, and the presence of intracranial/spinal seeding or hydrocephalus were assessed. ADC values of the lesions were measured using the PACS system. Measurements were made by drawing manual ROIs (regions of interest) to cover the solid part of the lesion. Additionally, the ADC value of the left thalamus was measured similarly in each patient for a reference value. The ration of mean ADC values of the solid part of the lesion and the left thalamus were calculated. Finally, the images of 58 patients (one ADC map, one T2-weighted image, and one post-contrast T1-weighted image per patient) were analyzed for texture analysis. Texture analysis and histogram evaluations were performed using the MaZda program (MaZda 4.60, The Technical University of Lodz, Institute of Electronics, Poland). Each lesion was manually marked with an ROI to include the entire solid part of the lesion on a single slice (Figure 1). As a result, texture analysis data were extracted for three images of each lesion (one ADC map, one T2-weighted image, and one post-contrast T1-weighted image). Statistical Analysis SPSS version 22 software (IBM Corp., Armonk, NY, USA) was used for data analysis. The data were presented as mean, standard deviation, median, minimum, and maximum values. The Kolmogorov-Smirnov test was used to test for normal distribution of the data. Parametric tests were used for normally distributed data, and non-parametric tests were used for non-normally distributed data. Mann-Whitney U test, t-test, and ROC analysis were used in the analyses. A p-value of less than 0.05 was considered statistically significant. RESULTS The mean age of the 58 patients included in the study was 9.62±8.25 years (range 0.08–35.75 years). Of the patients, 58.6% were male, and 55.2% had SHH pathway-active medulloblastoma. Remainder of the patients had group 3/4 medulloblastoma. Table 1 presents the descriptive data on ADC measurements including the ADC solid/thalamus ratio (ratio of the ADC value of the tumor’s solid part to normal left thalamus) and texture features. A statistically significant difference was found in ADC measurements by molecular type. In SHH pathway-active medulloblastoma, ADC solid/thalamus, Mean, Perc.01%, Perc.10%, Perc.90%, and SumOfSqs ADC values were significantly lower than those in Group 3-4 medulloblastoma, whereas Kurtosis and Perc.50% ADC values were significantly higher (table1). In ROC analysis, the area under the curve was highest for the ADC solid/thalamus ratio, 0.891, indicating this parameter is most useful for differentiating the two molecular subtypes (figure 2 and 3). The optimal cut-off value for this diagnostic test was 0.855, with a sensitivity of 82.1%, and specificity of 92.3%. Additionally, the positive likelihood ratio was found to be 10.6, and the negative likelihood ratio was 0.19, indicating that the ADC solid/thalamus ratio is a very good diagnostic test for differentiating SHH from Group 3-4 molecular types. For T1-WI derived texture features; kurtosis, SumOfSqs, SumVarnc, and SumEntrp values were significantly different between SHH pathway-active medulloblastoma and Group 3-4 medulloblastoma. Lower Kurtosis T1 values favored SHH, while higher values in other measurements favored SHH (table 2). SumOfSqs had the largest area under the curve on ROC analysis with an optimal cut-off value of 107.06, yielding a sensitivity of 62.1% and specificity of 84.6% (figure 4). No statistically significant differences were found for T2-WI derived texture features between SHH pathway-active medulloblastoma and Group 3-4 medulloblastoma. Nodularity and/or the presence of more than three lobulations were observed more commonly in the SHH pathway-active molecular subtype (p=0.002). Tumor localization also showed statistically significant differences by molecular subtype, with localization outside the midline or in the cerebellar hemisphere being more common in the SHH subtype (p<0.001). Additionally, contrast enhancement patterns varied significantly by molecular subtype, with heterogeneous contrast enhancement being more common in the SHH subtype (p=0.002). However, no statistically significant differences were found in the presence of hemorrhage and/or calcification, intracranial and/or spinal seeding by molecular subtype. T2 signal intensity also demonstrated statistically significant differences, with a hypointense appearance relative to gray matter being more common in the SHH molecular subtype. While no statistically significant differences were noted in the presence of microcysts (1cm) were significantly more common in the SHH subtype (p=0.001). Similarly, edema surrounding the tumor was significantly more common in the SHH subtype (p=0.041). There were no statistically significant differences in tumor margins (poorly or well-defined) or the presence of hydrocephalus by molecular subtype (figure 5). DISCUSSION Medulloblastoma constitutes approximately 20-25% of all pediatric brain tumors and is the most common malignant tumor of the central nervous system in children [16]. Originating from the posterior fossa and classified as a primitive embryonal tumor, it is histologically considered a WHO grade 4 tumor. Over the past decade, new biological insights into the heterogeneity of medulloblastomas have led to the identification of various molecular subgroups with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical outcomes. In the WHO 2016 classification of central nervous system tumors, medulloblastomas were divided into four different molecular subgroups (Wingless/WNT, Sonic Hedgehog/SHH, Group 3, and Group 4) [17]. With the rapid evolution of genomic technology, several methods are available for distinguishing molecular subtypes of medulloblastomas. These include the NanoString technique, which allows for expression profiling of a selected set of markers at the RNA level, real-time reverse transcriptase polymerase chain reaction (RT-PCR) to detect different expressions of selected protein-coding genes and microRNAs, immunohistochemical detection of selected marker expression, and DNA methylation arrays [18-22]. Each method has its own advantages and disadvantages. Conventional MRI findings, MR spectroscopy, perfusion, and diffusion MRI have been used in many studies to predict these molecular subgroups preoperatively. In our study, we aimed to differentiate molecular subgroups using MRI texture analysis in addition to conventional and diffusion MRI findings. Traditionally, imaging has been used in oncology for diagnosis (characterization of the lesion) and staging (assessment of disease extent). In relation to brain tumors, the neuro-radiology community has primarily focused on correlating imaging features with histomorphology and grading. However, as is now widely believed, images are more than just representations—they reflect the dynamics of underlying disease biology, including processes such as gene expression, proliferation, metabolism, and angiogenesis [23, 24]. Radiogenomics, or imaging genomics, is an exciting and emerging field of research aimed at defining relationships between non-invasive imaging features (radiophenotypes) and genomic data/molecular markers (molecular phenotypes). In recent years, it has become increasingly possible to extract meaningful information from routine imaging beyond diagnostic and staging roles. Radiogenomics is a multi-step process that includes image acquisition, image segmentation, feature selection, feature extraction, characterization, and final correlation with molecular markers. At its simplest, semantic imaging features such as size/volume, edges/margins, density or density characteristics, contrast enhancement, edema, necrosis, hemorrhage, and calcification can be visually evaluated and quantified individually or in combination for correlation with molecular markers. The introduction of computer-based algorithms has not only improved the assessment of semantic features but also enabled the extraction of agnostic features (histograms, textures, wavelets, and fractal dimensions) that often surpass human capacity. This type of automated high-throughput processing significantly reduces time and nearly eliminates the inter-observer variability associated with human interpretation [25]. Texture analysis is a post-processing technique that reveals imaging information undetectable to the human eye. By analyzing the gray levels of image pixels, quantitative texture characteristics can be detected and analyzed for diagnostic or prognostic purposes. In recent years, texture analysis has been investigated in various tumors such as hepatocellular carcinoma, glioblastoma, and nasopharyngeal carcinoma [26-28]. Given that texture analysis is ideal for detecting hidden information in MRI, we hypothesized in our study that MRI texture analysis could differentiate molecular subtypes of medulloblastoma. When comparing the relationship between the histological and molecular subtypes of the patients included in our study, we found that classical histology was dominant in Group 3-4 medulloblastomas (73.1%) and the desmoplastic/nodular histological subtype was dominant in SHH-activated medulloblastomas (53.1%). Both findings were statistically significant (p<0.001). These results are consistent with the literature, where Kool et al. also reported a strong correlation between the SHH molecular subtype and desmoplastic/nodular histology [3]. Additionally, in our study, the histological subtype showing extensive nodularity (MBEN) was found to be associated only with the active SHH molecular pathway in four patients. All four patients were under one year old. Although this histological subtype has been reported as specific to infants in many studies, it has also been detected in adult patients [29]. SHH pathway-activated medulloblastomas are known to develop from granule neuron precursor cells in the external granular layer of the cerebellum and are often associated with a lateralized hemispheric location [30-33]. In our study, 62.5% of SHH-activated medulloblastomas were located outside the midline or in the cerebellar hemisphere, and this finding was statistically significant compared to Group 3-4 tumors (p<0.001). SHH pathway tumors are more frequently associated with peritumoral edema compared to other molecular subgroups. Keil et al. reported a median edema volume of 5.1 cm³ in SHH subgroup tumors and 1.2 cm³ in Group 4 medulloblastomas [32]. Zhao et al. also reported a high frequency of peritumoral edema (81%) in SHH pathway tumors in their study [7]. In another study, peritumoral edema was detected in 91% of SHH pathway medulloblastomas, while it was observed in only 26-41% of the other three subgroups [34]. In our study, peritumoral edema was observed in 53.1% of SHH pathway-activated tumors, and this was significantly higher compared to Group 3-4 tumors (p=0.041). The degree and pattern of contrast enhancement in SHH pathway-activated tumors have been variably reported in different studies. Dasgupta et al. found contrast enhancement in 94% of SHH subgroup medulloblastomas, with more than 50% showing a heterogeneous enhancement pattern [34]. In our study, the contrast enhancement pattern was categorized into three groups: solid (>90%), heterogeneous (10-90%), and none/minimal (<10%). Heterogeneous contrast enhancement was detected in 58.6% of SHH-activated tumors, which was statistically significant (p=0.011). SHH subgroup medulloblastomas are frequently associated with the presence of microcysts (1 cm) [12]. While not classic for the SHH pathway, intratumoral macrocysts have been more commonly found in infantile SHH subgroup medulloblastomas [34]. In our study, macrocysts were found in only two of the Group 3-4 medulloblastomas, but they were present in 50% of the 32 SHH-activated medulloblastomas, which was statistically significant (p=0.001). Some SHH-activated medulloblastomas can involve nodular areas in the cerebellar cortex outside the primary tumor. Today, these are thought to be related to multi-centric disease involvement rather than metastasis and are highly specific for the SHH subgroup [30, 33]. In our study, nodular involvement and more than three lobulations in the tumor contour were more frequently observed in SHH-activated medulloblastomas (p=0.002). In a recent study by Reddy et al., significant differences were found between molecular subgroups of medulloblastomas in the absolute ADC values of solid contrast-enhancing tumor parts and the ratios of these values to cerebellar white matter or the thalamus. The median values of the tumor-to-thalamus ratio were reported as 0.791 for SHH pathway-activated tumors, 0.754 for WNT subgroup tumors, and 0.922 for Group 3-4 medulloblastomas [35]. In our study, the median values of the tumor-to-thalamus ratio were 0.75 for SHH pathway-activated medulloblastomas and 0.95 for Group 3-4 medulloblastomas (p<0.001). The lower ADC solid/thalamus ratio favors SHH, and the optimal cut-off value for this diagnostic test was calculated as 0.855. For this cut-off value, the sensitivity was 82.1% and the specificity was 92.3%. The ADC solid/thalamus ratio was found to be an excellent diagnostic test for distinguishing between SHH and Group 3-4 molecular subtypes. When we examined the histology of SHH pathway-activated tumors in our study group, 53.1% were desmoplastic/nodular and 12.5% showed advanced nodular differentiation. The relatively low ADC values observed in SHH pathway-activated tumors could be partially explained by the dense reticulin fibers in these two histological subtypes, which restrict extracellular water movement [36-38]. It has been noted that texture analysis, a mathematical method for the quantitative analysis of variations in imaging models, shows promising diagnostic potential for various brain tumors such as glioma, meningioma, metastatic brain tumors, primary central nervous system lymphoma, and medulloblastoma [39-43]. Studies in the literature mainly focus on the use of texture analysis in differentiating brain tumors with different histology. One study demonstrated that three-dimensional texture analysis on MRI could be used to distinguish glioblastoma from primary central nervous system lymphoma [42]. Another study showed the value of various texture analysis parameters in differentiating metastatic brain tumors from high-grade gliomas [41]. In our study, ROC analysis evaluating the diagnostic value of MRI ADC measurements in distinguishing between SHH pathway-activated medulloblastoma and Group 3-4 medulloblastomas showed that the areas under the curve (AUC) for Kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and SumOfSqs ADC values were significant. These measured values were found to be weak-to-moderate in diagnostic decision-making. High Kurtosis ADC values were interpreted in favor of SHH, while low values in other measurements also supported SHH. Additionally, in the ROC curve analysis evaluating the diagnostic value of T1 measurements, the AUCs for Kurtosis, SumOfSqs, SumVarnc, and SumEntrp were found to be significant. The measured values in the T1 sequence were also found to be weak-to-moderate in diagnostic decision-making. For the SumOfSqs measurement, which had the largest AUC, the optimal cut-off point was 107.06, with a sensitivity of 62.1% and a specificity of 84.6%. Texture analysis was seen to provide valuable information in distinguishing molecular subtypes. It is possible that combinations with other MRI parameters could increase the diagnostic value, but further multi-center studies with larger patient groups are needed to support these findings. There are some limitations to our study. Firstly, we did not have any patients with the WNT molecular subtype. Additionally, Group 3 and 4 tumors were considered as a single group. Another limitation is the retrospective nature of our study and the small sample size. As with many studies involving quantitative measurements, the potential for incorrect measurements due to incorrect placement of the measurement circle (ROI) or the measurement of surrounding tissues due to partial volume effect are also limitations. CONCLUSION The use of morphological features such as the location of the tumor, the presence of macrocysts, nodularity/lobulation, ADC measurements, texture analysis, and histogram parameters in distinguishing the molecular subgroups of medulloblastoma reveals promising new findings. These results need to be supported by new prospective multi-center studies with larger patient populations. Declarations Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 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AJNR Am J Neuroradiol. 2014;39:1009–15. Tables Table-1: Comparison of MRI ADC measurements according to molecular subtypes Molecular Subtype Group 3-4 Medulloblastoma SHH-activated Medulloblastoma Mean S.D. Median Mean S.D. Median p ADC solid/thalamus 0,96 0,17 0,95 0,74 0,11 0,75 <0.001 Mean-ADC 843,75 149,56 838,15 752,56 96,54 763,22 0.010 Variance-ADC 18737,43 14068,76 18968,91 17133,04 15203,18 11863,79 0.457 Skewness-ADC 1,10 0,63 1,05 1,42 0,69 1,38 0.082 Kurtosis-ADC 2,70 2,49 2,18 4,47 3,45 4,19 0.023 Perc.01%-ADC 557,69 98,18 561,50 496,21 72,74 496,50 0.011 Perc.10%-ADC 635,89 110,18 619,50 558,29 59,99 555,00 0,001 Perc.50%-ADC 765,92 150,46 733,00 1819,68 6191,54 657,00 0,006 Perc.90%-ADC 1009,50 173,67 981,00 894,18 131,88 845,50 0,013 Perc.99%-ADC 1281,15 404,21 1140,50 1162,64 411,65 1066,50 0,069 AngScMom-ADC 0,00 0,00 0,00 0,00 0,00 0,00 0,171 Contrast-ADC 146,36 35,29 149,80 132,91 29,27 136,44 0,132 Correlat-ADC 0,24 0,16 0,20 0,26 0,14 0,25 0.324 SumOfSqs-ADC 96,94 11,43 99,45 90,13 10,52 93,02 0,022 InvDfMom-ADC 0,13 0,03 0,12 0,14 0,03 0,13 0,226 SumAverg-ADC 64,35 1,34 64,25 63,79 1,12 64,17 0,166 SumVarnc-ADC 241,41 49,08 230,18 227,59 37,57 228,24 0,665 SumEntrp-ADC 1,68 0,11 1,70 1,70 0,08 1,72 0,500 Entropy-ADC 2,51 0,25 2,59 2,60 0,21 2,68 0,123 DifVarnc-ADC 61,51 14,56 61,73 57,43 10,97 57,47 0.248 DifEntrp-ADC 1,32 0,08 1,35 1,32 0,06 1,32 0.568 Table 2: Comparison of MRI T1-weighted sequence measurements according to molecular subtypes Molecular Subtype Group 3-4 Medulloblastoma SHH-activated Medulloblastoma Mean S.D. Median Mean S.D. Median p Mean-T1 430,87 251,18 354,90 1626,41 6254,52 380,85 0,522 Variance-T1 5104,53 4923,56 3470,40 10205,11 14241,63 4989,36 0,134 Skewness-T1 0,30 0,92 0,08 0,00 0,94 -0,12 0,125 Kurtosis-T1 1,49 2,53 0,68 1,55 6,93 -0,12 0,041 Perc.01%-T1 299,62 183,72 239,50 287,86 192,27 229,00 0,800 Perc.10%-T1 360,77 220,75 272,00 365,38 226,35 310,00 0,794 Perc.50%-T1 427,62 255,93 359,50 465,10 279,00 381,00 0,631 Perc.90%-T1 506,50 281,73 438,00 549,35 314,10 440,00 0,853 Perc.99%-T1 579,65 303,64 544,50 606,07 333,91 497,00 0,926 AngScMom-T1 0,00 0,00 0,00 0,00 0,00 0,00 0,428 Contrast-T1 85,55 44,02 80,32 69,62 30,56 70,29 0.122 Correlat-T1 0,57 0,22 0,57 0,67 0,15 0,67 0.055 SumOfSqs-T1 100,55 7,86 102,05 105,30 8,50 107,78 0,005 InvDfMom-T1 0,20 0,09 0,18 0,22 0,08 0,19 0,238 SumAverg-T1 65,09 1,18 65,26 65,34 0,84 65,32 0,601 SumVarnc-T1 316,65 55,43 320,03 351,56 46,02 355,06 0,014 SumEntrp-T1 1,80 0,08 1,82 1,83 0,04 1,85 0,032 Entropy-T1 2,76 0,22 2,81 2,82 0,13 2,85 0,281 DifVarnc-T1 36,49 16,50 34,57 31,01 12,54 31,66 0.169 DifEntrp-T1 1,20 0,15 1,21 1,16 0,14 1,17 0.218 Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Suheyla","middleName":"","lastName":"Bozkurt","suffix":""},{"id":499464045,"identity":"c1b7fdf7-8587-424b-87b9-a6095f5dc657","order_by":5,"name":"Nuri Cagatay Cimsit","email":"","orcid":"","institution":"Marmara University","correspondingAuthor":false,"prefix":"","firstName":"Nuri","middleName":"Cagatay","lastName":"Cimsit","suffix":""}],"badges":[],"createdAt":"2025-08-10 07:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7337273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7337273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90464707,"identity":"f09a2680-0e15-4a28-a511-e822f51f4b18","added_by":"auto","created_at":"2025-09-03 05:11:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":293297,"visible":true,"origin":"","legend":"\u003cp\u003eROI segmentation of medulloblastomas for texture analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7337273/v1/e352d42c6a3f8cf339545d97.png"},{"id":90464708,"identity":"bef63aa9-fd5d-4425-9ae1-c29c3b55d1d8","added_by":"auto","created_at":"2025-09-03 05:11:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":298668,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis for ADC measurements\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7337273/v1/c9058416ddc7ceb7a777a1e4.png"},{"id":90466787,"identity":"ffc98ede-e93c-434f-b543-80babfb49bf7","added_by":"auto","created_at":"2025-09-03 05:37:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31787,"visible":true,"origin":"","legend":"\u003cp\u003eADC solid tumor/thalamus ratio values according to molecular subtype (box-plot graph)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7337273/v1/6ad1a77e3369b797b4e8a1cf.png"},{"id":90464705,"identity":"7e16297f-0130-40dd-9816-e9eb46710225","added_by":"auto","created_at":"2025-09-03 05:11:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42863,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis for T1-weighted images\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7337273/v1/3440f91637f338d210e1e11a.png"},{"id":90464709,"identity":"62cf0222-ce50-4ef7-a62a-b35d75d4f4e4","added_by":"auto","created_at":"2025-09-03 05:11:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":501967,"visible":true,"origin":"","legend":"\u003cp\u003eStatistically significant qualitative parameters distinguishing SHH-activated MB from Group 3/4 MB. These parameters, listed from left to right, include off-midline/cerebellar vs midline/4th ventricular location, presence or absence of peritumoral edema, macrocysts or nodularity/lobulation, and heterogeneous vs solid or none-minimal enhancement\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7337273/v1/03cbcd0fa2890e7c654b114b.png"},{"id":95510058,"identity":"21fd5f72-7c4f-4771-9fee-1649266aa42c","added_by":"auto","created_at":"2025-11-10 07:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1728312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7337273/v1/4494f13a-78db-491c-adea-c0b70f120535.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differentiation of Medulloblastoma Molecular Subtypes Using Multiparametric MRI and Texture Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMedulloblastoma (MB) is the most common embryonal brain tumor, leading to significant morbidity and high mortality in children [1]. Initially viewed as a single disease, recent molecular research has revealed that MB is composed of at least four distinct subgroups: SHH, WNT, Group 3, and Group 4. These subgroups have unique biological, prognostic, and therapeutic characteristics [2-4].\u003c/p\u003e\n\u003cp\u003eTumor classification is typically performed on tissue samples obtained during surgery or biopsy. Unlike high-grade gliomas, MBs exhibit a uniform genetic profile throughout the tumor, meaning even a small biopsy can accurately determine the subgroup [5]. However, obtaining these samples is an invasive procedure with inherent risks for the patient. Despite the clear benefits of subtyping, its integration into clinical practice has been slow due to the high costs and lack of access to the advanced methods required for accurate analysis [4].\u003c/p\u003e\n\u003cp\u003eWhile radiomics has been underutilized for MB, a number of studies have used magnetic resonance imaging (MRI) to qualitatively assess and categorize MBs based on their molecular characteristics. These studies have demonstrated that the location and contrast-enhancement patterns of tumors vary among the different MB subgroups [6-11]. For example:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eGroup 3 and Group 4 MBs usually develop in the midline of the brain.\u003c/li\u003e\n \u003cli\u003eSHH tumors are most frequently found in the cerebellar hemispheres.\u003c/li\u003e\n \u003cli\u003eWNT tumors can be found in the midline, cerebellar peduncle, or cerebellopontine cisterns [6, 8, 11].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditionally, the absence of contrast enhancement on an MRI is a strong indicator of a Group 4 tumor, while intense enhancement in non-WNT/SHH tumors is associated with a poorer prognosis [11].\u003c/p\u003e\n\u003cp\u003eMRI-based texture analysis is a quantitative technique that provides insights into a tumor's heterogeneity [12, 13]. This method uses a series of mathematical features to analyze specific regions within an image, generating objective data about tissue characteristics [14].\u003c/p\u003e\n\u003cp\u003eThese features are generally categorized into three types based on their complexity:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eFirst-order features (least complex): These are single statistical measures that describe the tissue of an entire volume. Examples include mean intensity, standard deviation (SD), entropy, kurtosis, and skewness.\u003c/li\u003e\n \u003cli\u003eSecond-order features: These describe the relationship between two points, such as pixels or voxels, within an image. They can provide information about a tumor's three-dimensional shape, size, and the range of values within it. Common examples include entropy, compactness, sphericity, and the surface-to-volume ratio.\u003c/li\u003e\n \u003cli\u003eHigher-order features (most complex): These are more intricate and define the relationships between three or more points in space [15].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe development of non-invasive imaging biomarkers for MB subgroups could give physicians a valuable non-surgical way to understand a tumor's genetic makeup. This would significantly help with clinical prognostication and enable more informed treatment decisions. This study aims to explore how multiparametric MRI, including advanced texture analysis, can be used to differentiate the molecular subtypes of medulloblastoma.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis retrospective study was conducted after institutional review board approval. The requirement for informed consent was waived given the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with pathologically proven medulloblastomas who underwent posterior fossa surgery or biopsy at a tertiary referral center between January 2012 and August 2020 were retrospectively reviewed. A total of 91 patients were identified with a pathologic diagnosis of medulloblastoma. Thirty-three patients were excluded from the study due to either not having molecular subtyping, non-diagnostic MRI due to motion/technical artifacts, or lack of preoperative MRI at our center. Of the excluded patients, only two had a molecular subtype diagnosis of WNT medulloblastoma. Fifty-eight patients who had preoperative MRIs and were diagnosed with SHH/Group 3-4 molecular subtype medulloblastoma were included in our study. The patients were divided into two groups: Group 3-4 and SHH pathway-active medulloblastoma. Brain MRI findings, ADC measurements, and texture analysis characteristics of each group were compared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain MRI Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain MRI exams of the patients were performed in the local hospital using 1.5 T (Avanto) or 3 T (Verio) Siemens MRI machines. Our routine brain MRI protocols included T1-weighted spin-echo (TR = 450-515 ms, TE = 10-15 ms, flip angle = 90°, FOV = 200-260 mm), T2-weighted spin-echo (TR = 4200-4650 ms, TE = 90-100 ms, flip angle = 150°, FOV = 200-260 mm), T2-FLAIR (TR = 7000-8000 ms, TE = 84-100 ms, flip angle = 150°, FOV = 200-260 mm), and axial diffusion-weighted imaging (DWI) with b-values of 0 and 1000 s/mm². Calculated apparent diffusion coefficient (ADC) maps were also obtained. Slice thickness varied between 3-4 mm. Post-contrast T1-weighted images were obtained similarly to pre-contrast images, with the addition of fat suppression and sagittal plane imaging. The presence of hemorrhage/calcification in 31 patients was evaluated using the SWI sequence or with available CT of the head. Diffusion-weighted imaging was not available for four patients, and post-contrast T1-weighted imaging was unavailable for three patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLesion Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain MRI images were initially evaluated blind to clinical information and postoperative histopathological diagnoses. On brain MRI, tumor localization, contrast enhancement pattern (heterogenous vs homogenous or minimal/non-enhancing), margins, T2 signal relative to cerebral gray matter, the presence of vasogenic edema, hemorrhage/calcification, microcysts (\u0026lt;1 cm)/macrocysts (\u0026gt;1 cm), nodularity/lobulation, and the presence of intracranial/spinal seeding or hydrocephalus were assessed.\u003c/p\u003e\n\u003cp\u003eADC values of the lesions were measured using the PACS system. Measurements were made by drawing manual ROIs (regions of interest) to cover the solid part of the lesion. Additionally, the ADC value of the left thalamus was measured similarly in each patient for a reference value. The ration of mean ADC values of the solid part of the lesion and the left thalamus were calculated. Finally, the images of 58 patients (one ADC map, one T2-weighted image, and one post-contrast T1-weighted image per patient) were analyzed for texture analysis.\u003c/p\u003e\n\u003cp\u003eTexture analysis and histogram evaluations were performed using the MaZda program (MaZda 4.60, The Technical University of Lodz, Institute of Electronics, Poland). Each lesion was manually marked with an ROI to include the entire solid part of the lesion on a single slice (Figure 1). As a result, texture analysis data were extracted for three images of each lesion (one ADC map, one T2-weighted image, and one post-contrast T1-weighted image).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS version 22 software (IBM Corp., Armonk, NY, USA) was used for data analysis. The data were presented as mean, standard deviation, median, minimum, and maximum values. The Kolmogorov-Smirnov test was used to test for normal distribution of the data. Parametric tests were used for normally distributed data, and non-parametric tests were used for non-normally distributed data. Mann-Whitney U test, t-test, and ROC analysis were used in the analyses. A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe mean age of the 58 patients included in the study was 9.62\u0026plusmn;8.25 years (range 0.08\u0026ndash;35.75 years). Of the patients, 58.6% were male, and 55.2% had SHH pathway-active medulloblastoma. Remainder of the patients had group 3/4 medulloblastoma.\u003c/p\u003e\n\u003cp\u003eTable 1 presents the descriptive data on ADC measurements including the ADC solid/thalamus ratio (ratio of the ADC value of the tumor\u0026rsquo;s solid part to normal left thalamus) and texture features. A statistically significant difference was found in ADC measurements by molecular type. In SHH pathway-active medulloblastoma, ADC solid/thalamus, Mean, Perc.01%, Perc.10%, Perc.90%, and SumOfSqs ADC values were significantly lower than those in Group 3-4 medulloblastoma, whereas Kurtosis and Perc.50% ADC values were significantly higher (table1). In ROC analysis, the area under the curve was highest for the ADC solid/thalamus ratio, 0.891, indicating this parameter is most useful for differentiating the two molecular subtypes (figure 2 and 3). The optimal cut-off value for this diagnostic test was 0.855, with a sensitivity of 82.1%, and specificity of 92.3%. Additionally, the positive likelihood ratio was found to be 10.6, and the negative likelihood ratio was 0.19, indicating that the ADC solid/thalamus ratio is a very good diagnostic test for differentiating SHH from Group 3-4 molecular types.\u003c/p\u003e\n\u003cp\u003eFor T1-WI derived texture features; kurtosis, SumOfSqs, SumVarnc, and SumEntrp values were significantly different between SHH pathway-active medulloblastoma and Group 3-4 medulloblastoma. Lower Kurtosis T1 values favored SHH, while higher values in other measurements favored SHH (table 2). SumOfSqs had the largest area under the curve on ROC analysis with an optimal cut-off value of 107.06, yielding a sensitivity of 62.1% and specificity of 84.6% (figure 4).\u003c/p\u003e\n\u003cp\u003eNo statistically significant differences were found for T2-WI derived texture features between SHH pathway-active medulloblastoma and Group 3-4 medulloblastoma.\u003c/p\u003e\n\u003cp\u003eNodularity and/or the presence of more than three lobulations were observed more commonly in the SHH pathway-active molecular subtype (p=0.002). Tumor localization also showed statistically significant differences by molecular subtype, with localization outside the midline or in the cerebellar hemisphere being more common in the SHH subtype (p\u0026lt;0.001). Additionally, contrast enhancement patterns varied significantly by molecular subtype, with heterogeneous contrast enhancement being more common in the SHH subtype (p=0.002). However, no statistically significant differences were found in the presence of hemorrhage and/or calcification, intracranial and/or spinal seeding by molecular subtype. T2 signal intensity also demonstrated statistically significant differences, with a hypointense appearance relative to gray matter being more common in the SHH molecular subtype. While no statistically significant differences were noted in the presence of microcysts (\u0026lt;1cm), macrocysts (\u0026gt;1cm) were significantly more common in the SHH subtype (p=0.001). Similarly, edema surrounding the tumor was significantly more common in the SHH subtype (p=0.041). There were no statistically significant differences in tumor margins (poorly or well-defined) or the presence of hydrocephalus by molecular subtype (figure 5).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMedulloblastoma constitutes approximately 20-25% of all pediatric brain tumors and is the most common malignant tumor of the central nervous system in children [16]. Originating from the posterior fossa and classified as a primitive embryonal tumor, it is histologically considered a WHO grade 4 tumor. Over the past decade, new biological insights into the heterogeneity of medulloblastomas have led to the identification of various molecular subgroups with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical outcomes. In the WHO 2016 classification of central nervous system tumors, medulloblastomas were divided into four different molecular subgroups (Wingless/WNT, Sonic Hedgehog/SHH, Group 3, and Group 4) [17]. With the rapid evolution of genomic technology, several methods are available for distinguishing molecular subtypes of medulloblastomas. These include the NanoString technique, which allows for expression profiling of a selected set of markers at the RNA level, real-time reverse transcriptase polymerase chain reaction (RT-PCR) to detect different expressions of selected protein-coding genes and microRNAs, immunohistochemical detection of selected marker expression, and DNA methylation arrays [18-22]. Each method has its own advantages and disadvantages. Conventional MRI findings, MR spectroscopy, perfusion, and diffusion MRI have been used in many studies to predict these molecular subgroups preoperatively. In our study, we aimed to differentiate molecular subgroups using MRI texture analysis in addition to conventional and diffusion MRI findings.\u003c/p\u003e\n\u003cp\u003eTraditionally, imaging has been used in oncology for diagnosis (characterization of the lesion) and staging (assessment of disease extent). In relation to brain tumors, the neuro-radiology community has primarily focused on correlating imaging features with histomorphology and grading. However, as is now widely believed, images are more than just representations\u0026mdash;they reflect the dynamics of underlying disease biology, including processes such as gene expression, proliferation, metabolism, and angiogenesis [23, 24]. Radiogenomics, or imaging genomics, is an exciting and emerging field of research aimed at defining relationships between non-invasive imaging features (radiophenotypes) and genomic data/molecular markers (molecular phenotypes). In recent years, it has become increasingly possible to extract meaningful information from routine imaging beyond diagnostic and staging roles. Radiogenomics is a multi-step process that includes image acquisition, image segmentation, feature selection, feature extraction, characterization, and final correlation with molecular markers. At its simplest, semantic imaging features such as size/volume, edges/margins, density or density characteristics, contrast enhancement, edema, necrosis, hemorrhage, and calcification can be visually evaluated and quantified individually or in combination for correlation with molecular markers. The introduction of computer-based algorithms has not only improved the assessment of semantic features but also enabled the extraction of agnostic features (histograms, textures, wavelets, and fractal dimensions) that often surpass human capacity. This type of automated high-throughput processing significantly reduces time and nearly eliminates the inter-observer variability associated with human interpretation [25].\u003c/p\u003e\n\u003cp\u003eTexture analysis is a post-processing technique that reveals imaging information undetectable to the human eye. By analyzing the gray levels of image pixels, quantitative texture characteristics can be detected and analyzed for diagnostic or prognostic purposes. In recent years, texture analysis has been investigated in various tumors such as hepatocellular carcinoma, glioblastoma, and nasopharyngeal carcinoma [26-28]. Given that texture analysis is ideal for detecting hidden information in MRI, we hypothesized in our study that MRI texture analysis could differentiate molecular subtypes of medulloblastoma.\u003c/p\u003e\n\u003cp\u003eWhen comparing the relationship between the histological and molecular subtypes of the patients included in our study, we found that classical histology was dominant in Group 3-4 medulloblastomas (73.1%) and the desmoplastic/nodular histological subtype was dominant in SHH-activated medulloblastomas (53.1%). Both findings were statistically significant (p\u0026lt;0.001). These results are consistent with the literature, where Kool et al. also reported a strong correlation between the SHH molecular subtype and desmoplastic/nodular histology [3]. Additionally, in our study, the histological subtype showing extensive nodularity (MBEN) was found to be associated only with the active SHH molecular pathway in four patients. All four patients were under one year old. Although this histological subtype has been reported as specific to infants in many studies, it has also been detected in adult patients [29].\u003c/p\u003e\n\u003cp\u003eSHH pathway-activated medulloblastomas are known to develop from granule neuron precursor cells in the external granular layer of the cerebellum and are often associated with a lateralized hemispheric location [30-33]. In our study, 62.5% of SHH-activated medulloblastomas were located outside the midline or in the cerebellar hemisphere, and this finding was statistically significant compared to Group 3-4 tumors (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eSHH pathway tumors are more frequently associated with peritumoral edema compared to other molecular subgroups. Keil et al. reported a median edema volume of 5.1 cm\u0026sup3; in SHH subgroup tumors and 1.2 cm\u0026sup3; in Group 4 medulloblastomas [32]. Zhao et al. also reported a high frequency of peritumoral edema (81%) in SHH pathway tumors in their study [7]. In another study, peritumoral edema was detected in 91% of SHH pathway medulloblastomas, while it was observed in only 26-41% of the other three subgroups [34]. In our study, peritumoral edema was observed in 53.1% of SHH pathway-activated tumors, and this was significantly higher compared to Group 3-4 tumors (p=0.041).\u003c/p\u003e\n\u003cp\u003eThe degree and pattern of contrast enhancement in SHH pathway-activated tumors have been variably reported in different studies. Dasgupta et al. found contrast enhancement in 94% of SHH subgroup medulloblastomas, with more than 50% showing a heterogeneous enhancement pattern [34]. In our study, the contrast enhancement pattern was categorized into three groups: solid (\u0026gt;90%), heterogeneous (10-90%), and none/minimal (\u0026lt;10%). Heterogeneous contrast enhancement was detected in 58.6% of SHH-activated tumors, which was statistically significant (p=0.011).\u003c/p\u003e\n\u003cp\u003eSHH subgroup medulloblastomas are frequently associated with the presence of microcysts (\u0026lt;1 cm) and macrocysts (\u0026gt;1 cm) [12]. While not classic for the SHH pathway, intratumoral macrocysts have been more commonly found in infantile SHH subgroup medulloblastomas [34]. In our study, macrocysts were found in only two of the Group 3-4 medulloblastomas, but they were present in 50% of the 32 SHH-activated medulloblastomas, which was statistically significant (p=0.001).\u003c/p\u003e\n\u003cp\u003eSome SHH-activated medulloblastomas can involve nodular areas in the cerebellar cortex outside the primary tumor. Today, these are thought to be related to multi-centric disease involvement rather than metastasis and are highly specific for the SHH subgroup [30, 33]. In our study, nodular involvement and more than three lobulations in the tumor contour were more frequently observed in SHH-activated medulloblastomas (p=0.002).\u003c/p\u003e\n\u003cp\u003eIn a recent study by Reddy et al., significant differences were found between molecular subgroups of medulloblastomas in the absolute ADC values of solid contrast-enhancing tumor parts and the ratios of these values to cerebellar white matter or the thalamus. The median values of the tumor-to-thalamus ratio were reported as 0.791 for SHH pathway-activated tumors, 0.754 for WNT subgroup tumors, and 0.922 for Group 3-4 medulloblastomas [35]. In our study, the median values of the tumor-to-thalamus ratio were 0.75 for SHH pathway-activated medulloblastomas and 0.95 for Group 3-4 medulloblastomas (p\u0026lt;0.001). The lower ADC solid/thalamus ratio favors SHH, and the optimal cut-off value for this diagnostic test was calculated as 0.855. For this cut-off value, the sensitivity was 82.1% and the specificity was 92.3%. The ADC solid/thalamus ratio was found to be an excellent diagnostic test for distinguishing between SHH and Group 3-4 molecular subtypes. When we examined the histology of SHH pathway-activated tumors in our study group, 53.1% were desmoplastic/nodular and 12.5% showed advanced nodular differentiation. The relatively low ADC values observed in SHH pathway-activated tumors could be partially explained by the dense reticulin fibers in these two histological subtypes, which restrict extracellular water movement [36-38].\u003c/p\u003e\n\u003cp\u003eIt has been noted that texture analysis, a mathematical method for the quantitative analysis of variations in imaging models, shows promising diagnostic potential for various brain tumors such as glioma, meningioma, metastatic brain tumors, primary central nervous system lymphoma, and medulloblastoma [39-43]. Studies in the literature mainly focus on the use of texture analysis in differentiating brain tumors with different histology. One study demonstrated that three-dimensional texture analysis on MRI could be used to distinguish glioblastoma from primary central nervous system lymphoma [42]. Another study showed the value of various texture analysis parameters in differentiating metastatic brain tumors from high-grade gliomas [41].\u003c/p\u003e\n\u003cp\u003eIn our study, ROC analysis evaluating the diagnostic value of MRI ADC measurements in distinguishing between SHH pathway-activated medulloblastoma and Group 3-4 medulloblastomas showed that the areas under the curve (AUC) for Kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and SumOfSqs ADC values were significant. These measured values were found to be weak-to-moderate in diagnostic decision-making. High Kurtosis ADC values were interpreted in favor of SHH, while low values in other measurements also supported SHH. Additionally, in the ROC curve analysis evaluating the diagnostic value of T1 measurements, the AUCs for Kurtosis, SumOfSqs, SumVarnc, and SumEntrp were found to be significant. The measured values in the T1 sequence were also found to be weak-to-moderate in diagnostic decision-making. For the SumOfSqs measurement, which had the largest AUC, the optimal cut-off point was 107.06, with a sensitivity of 62.1% and a specificity of 84.6%. Texture analysis was seen to provide valuable information in distinguishing molecular subtypes. It is possible that combinations with other MRI parameters could increase the diagnostic value, but further multi-center studies with larger patient groups are needed to support these findings.\u003c/p\u003e\n\u003cp\u003eThere are some limitations to our study. Firstly, we did not have any patients with the WNT molecular subtype. Additionally, Group 3 and 4 tumors were considered as a single group. Another limitation is the retrospective nature of our study and the small sample size. As with many studies involving quantitative measurements, the potential for incorrect measurements due to incorrect placement of the measurement circle (ROI) or the measurement of surrounding tissues due to partial volume effect are also limitations.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe use of morphological features such as the location of the tumor, the presence of macrocysts, nodularity/lobulation, ADC measurements, texture analysis, and histogram parameters in distinguishing the molecular subgroups of medulloblastoma reveals promising new findings. These results need to be supported by new prospective multi-center studies with larger patient populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eB.A. collected the data B.A , E.B. and N.C.C reviewed the imagesT.A. performed the statistical analysisB.A., E.B., and T.A. wrote the main manuscript B.A and T.A . prepared figuresAll authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePollack IF, Jakacki RI. Childhood brain tumors: Epidemiology, current management and future directions. Nat Rev Neurol. 2011;7:495\u0026ndash;506.\u003c/li\u003e\n\u003cli\u003eTaylor MD, Northcott PA, Korshunov A, et al. Molecular subgroups of medulloblastoma: The current consensus. 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The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016;131:803\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eEllison DW, Kocak M, Dalton J, et al. Definition of disease-risk stratification groups in childhood medulloblastoma using combined clinical, pathologic, and molecular variables. J Clin Oncol. 2011;29:1400\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eKool M, Koster J, Bunt J, et al. Integrated genomics identifies five medulloblastoma subtypes with distinct genetic profiles, pathway signatures and clinicopathological features. PLoS One. 2008;3:e3088.\u003c/li\u003e\n\u003cli\u003eKaur K, Kakkar A, Kumar A, et al. Integrating Molecular Subclassification of Medulloblastomas into Routine Clinical Practice: A Simplified Approach. Brain Pathol. 2016;26:334\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eKunder R, Jalali R, Sridhar E, et al. Real-time PCR assay based on the differential expression of microRNAs and protein-coding genes for molecular classification of formalin-fixed paraffin embedded medulloblastomas. Neuro Oncol. 2013;15:1644\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eEllison DW, Kocak M, Dalton J, et al. Definition of disease-risk stratification groups in childhood medulloblastoma using combined clinical, pathologic, and molecular variables. J Clin Oncol. 2011;29:1400\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSchwalbe EC, Williamson D, Lindsey JC, et al. DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol. 2013;125:359\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eGillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278:563\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eKickingereder P, Andronesi OC. Radiomics, Metabolic, and Molecular MRI for Brain Tumors. Semin Neurol. 2018;38:32\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003ePrasanna P, Patel J, Partovi S, et al. Radiomic features from the peritumoral brain parenchyma on treatment-na\u0026iuml;ve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol. 2017;27:4188\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eZhang B, Tian J, Dong D, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23:4259\u0026ndash;69.\u003c/li\u003e\n\u003cli\u003eChen S, Feng S, Wei J, et al. Pretreatment prediction of immunoscore in hepatocellular cancer: A radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol. 2019;29:4177\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eGessi M, Goschzik T, D\u0026ouml;rner E, et al. Medulloblastoma with extensive nodularity: A tumour exclusively of infancy? Neuropathol Appl Neurobiol. 2017;43:267\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eZapotocky M, Mata-Mbemba D, Sumerauer D, et al. Differential patterns of metastatic dissemination across medulloblastoma subgroups. J Neurosurg Pediatr. 2018;21:145\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eWefers AK, Warmuth-Metz M, P\u0026ouml;schl J, et al. Subgroup-specific localization of human medulloblastoma based on pre-operative MRI. Acta Neuropathol. 2014;127:931\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eKeil VC, Warmuth-Metz M, Reh C, et al. Imaging biomarkers for adult medulloblastomas: Genetic entities may be identified by their MR imaging radiophenotype. AJNR Am J Neuroradiol. 2017;38:1892\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMata-Mbemba D, Zapotocky M, Laughlin S, et al. MRI characteristics of primary tumors and metastatic lesions in molecular subgroups of pediatric medulloblastoma: A single-center study. AJNR Am J Neuroradiol. 2018;39:949\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eDasgupta A, Gupta T, Pungavkar S, et al. Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: Results from a radiogenomics study of 111 patients. Neuro Oncol. 2019;21:115\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eReddy N, Ellison DW, Soares BP, et al. Pediatric Posterior Fossa Medulloblastoma: The Role of Diffusion Imaging in Identifying Molecular Groups. J Neuroimaging. 2020;30:503\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003ePillai S, Singhal A, Byrne AT, et al. Diffusion-weighted imaging and pathological correlation in pediatric medulloblastomas-They are not always restricted! Childs Nerv Syst. 2011;27:1407\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003ePoretti A, Meoded A, Huisman TAGM. Neuroimaging of pediatric posterior fossa tumors including review of the literature. J Magn Reson Imaging. 2012;35:32\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eLiu HQ, Yin X, Li Y, et al. MRI features in children with desmoplastic medulloblastoma. J Clin Neurosci. 2012;19:281\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eLu Y, Liu L, Luan S, et al. The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: An attempt using decision tree and decision forest. Eur Radiol. 2019;29:1318\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eSuh HB, Choi YS, Bae S, et al. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach. Eur Radiol. 2018;28:3832\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eMouthuy N, Cosnard G, Abarca-Quinones J, et al. Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases. J Neuroradiol. 2012;39:301\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eXiao DD, Yan PF, Wang YX, et al. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis. Clin Neurol Neurosurg. 2018;173:84\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eGutierrez DR, Awwad A, Meijer L, et al. Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors. AJNR Am J Neuroradiol. 2014;39:1009\u0026ndash;15.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable-1: Comparison of MRI ADC measurements according to molecular subtypes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"702\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 494px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular Subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 3-4 Medulloblastoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSHH-activated Medulloblastoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eADC solid/thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMean-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e843,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e149,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e838,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e752,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e96,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e763,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eVariance-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e18737,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14068,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e18968,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17133,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e15203,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11863,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSkewness-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eKurtosis-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePerc.01%-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e557,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e98,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e561,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e496,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e72,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e496,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePerc.10%-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e635,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e110,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e619,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e558,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e59,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e555,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePerc.50%-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e765,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e150,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e733,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1819,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6191,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e657,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePerc.90%-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1009,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e173,67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e981,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e894,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e131,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e845,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePerc.99%-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1281,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e404,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1140,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1162,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e411,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1066,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAngScMom-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eContrast-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e146,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e35,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e149,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e132,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e136,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCorrelat-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSumOfSqs-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e96,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e99,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e90,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e93,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eInvDfMom-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSumAverg-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e64,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e64,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e63,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e64,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSumVarnc-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e241,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e49,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e230,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e227,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e228,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSumEntrp-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eEntropy-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDifVarnc-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e61,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e61,73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e57,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10,97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e57,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDifEntrp-ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Comparison of MRI T1-weighted sequence measurements according to molecular subtypes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 465px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular Subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 225px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 3-4 Medulloblastoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSHH-activated Medulloblastoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eMean-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e430,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e251,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e354,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1626,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6254,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e380,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eVariance-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e5104,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4923,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3470,40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10205,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14241,63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e4989,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSkewness-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eKurtosis-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6,93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePerc.01%-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e299,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e183,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e239,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e287,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e192,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e229,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePerc.10%-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e360,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e220,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e272,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e365,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e226,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e310,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePerc.50%-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e427,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e255,93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e359,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e465,10\u003c/p\u003e\n \u003c/td\u003e\n 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valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e497,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eAngScMom-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n 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style=\"width: 73px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCorrelat-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n 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\u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eInvDfMom-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n 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valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e316,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e55,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e320,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e351,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e46,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e355,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSumEntrp-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eEntropy-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0,281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDifVarnc-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e36,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e16,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e34,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e31,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e31,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDifEntrp-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"medulloblastoma, ADC measurement, texture analysis, brain MRI, Sonic hedgehog","lastPublishedDoi":"10.21203/rs.3.rs-7337273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7337273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMedulloblastoma, the most common malignant pediatric brain tumor, has distinct molecular subtypes that vary in prognosis and treatment response. Accurate preoperative identification of these subtypes is crucial for optimizing therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The aim of our study was to differentiate medulloblastoma molecular subtypes using multiparametric MRI, including MRI-based texture analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods:\u003c/strong\u003e Fifty-eight patients with preoperative MRI and histopathological diagnoses of medulloblastoma were included. The patients were divided into SHH pathway active and group 3/group 4 subtypes. Morphological MRI findings, ADC measurements, and texture analysis features were compared between the groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Of the 58 patients, 55.2% had SHH-active tumors. Morphological features, including location out of the midline or in the cerebellar hemisphere (p\u0026lt;0.001), peri-tumoral edema (p=0.041), macrocysts (p=0.001), nodular involvement/lobulation (p=0.002), and heterogeneous contrast enhancement (p=0.002) were more common in SHH tumors. ADC measurements showed that the solid tumor-to-thalamus ratio was significantly lower in SHH tumors (p\u0026lt;0.001), with a threshold of 0.855 providing 82.1% sensitivity and 92.3% specificity. As for texture analysis parameters, kurtosis (p=0.023), SumOfSqs (p=0.022) and 01-10-50-90% percentile (p=0.011; p=0.001; p=0.006; and p=0.013 respectively) values obtained from ADC images and kurtosis (p=0.041), SumOfSqs (p=0.005), SumVarnc (p=0.014), SumEntrp (p=0.032) values obtained from T1W images were statistically significant in differentiating SHH and group 3/ group 4 medulloblastoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Combining MRI morphological findings, ADC measurements, and texture analysis offers valuable diagnostic information for distinguishing medulloblastoma molecular subtypes.\u003c/p\u003e","manuscriptTitle":"Differentiation of Medulloblastoma Molecular Subtypes Using Multiparametric MRI and Texture Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 05:11:17","doi":"10.21203/rs.3.rs-7337273/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6401102-65db-4ddf-be0a-81b00669ce95","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T07:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 05:11:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7337273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7337273","identity":"rs-7337273","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}