Dynamic contrast-enhanced Magnetic Resonance Imaging in Paediatric Brain Tumours Systematically Reviewed

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Keil, Yeva Prysiazhniuk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543940/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2026 Read the published version in Pediatric Radiology → Version 1 posted 7 You are reading this latest preprint version Abstract Background Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an advanced imaging technique utilising dynamic contrast uptake to quantify blood-brain barrier permeability. Objective The clinical utility of DCE in paediatric brain tumours is unclear. This systematic review evaluates the efficacy of DCE in differentiating paediatric brain tumours and identifying progression. It also gathers information on the technical implementation of DCE in paediatric MRI, improving the standard of care. Materials and methods A string-based literature search was performed in PubMed and Web of Science. Original articles evaluating the utility of DCE were included. A modified QUADAS-2 instrument evaluated the risk of bias. Results Nine studies (2008–2025) were eligible (sample size 6–72 cases). Six studies investigated low-grade versus high-grade differentiation in mixed pediatric tumours (cumulative sample n = 196) with successful discrimination through K tra ns and/or k ep in three studies (60 patients). Discrimination of two distinct histologies was usually more successful. Two studies evaluated the response to different treatments. Results for survival prediction based on DCE parameters were not promising. One study attempted to predict tumour aggressiveness in optic pathway glioma with good prognostic capacity for K trans . DCE technical execution varied substantially among studies and was usually not compliant with current guidelines. Meta-analyses were impossible. Conclusion DCE may be of added value to discriminate between two different paediatric brain tumour entities, but a general discrimination potential between low- and high-grade lesions is doubtful. More studies and greater technical homogeneity are needed to investigate the technique’s prognostic potential for paediatric cohorts. brain tumour glioma paediatric dynamic contrast enhanced magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 1. Introduction Brain tumours rank as the most lethal, as well as the second most common type of cancer in paediatric patients [ 1 ]. Paediatric patients are 5 of biomarkers that avoid surgery to classify brain tumours and evaluate treatment response due to their vulnerability and the frequently surgically unfavourable tumour locations. Reliable noninvasive biomarkers may also improve cognitive outcome in this population [ 2 ]. Magnetic Resonance Imaging (MRI) is the key pillar of brain tumour therapy planning and surveillance, including in the pediatric population. Still, conventional MRI tumour protocols often fail to adequately capture the biological complexity of brain tumours, limiting their ability to accurately assess tumour vitality and malignancy. Advanced MRI techniques, on the other hand, show potential in improving the diagnostic performance in differentiating brain tumours in the paediatric population as well as the evaluation of treatment response [ 3 ]. Dynamic contrast-enhanced (DCE) MRI is an advanced quantitative MRI technique that is well-established for evaluating adult-type brain tumours [ 4 ]. DCE is a contrast agent injection-dependent sequence aiming to deliver insights into blood-brain barrier integrity by quantifying vascular permeability and perfusion-related parameters [ 5 ]. A significant correlation has been observed between DCE kinetic parameters and tumour grade in adult brain tumours [ 6 ]. However, the most common low-grade paediatric brain tumour, pilocytic astrocytoma, also typically shows blood-brain barrier disruption with pronounced contrast enhancement, in contrast to the more restrained enhancement patterns observed in adult low-grade gliomas or other paediatric low-grade tumours (Fig. 1 ). Nevertheless, systematic data on DCE measurements in paediatric gliomas remain scarce and underrepresented. From a technical perspective, the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA) suggested standardised scan parameters and processing for adults to increase DCE reproducibility without a pediatric counterpart [ 7 ]. Children differ, however, from adults in several aspects relevant to DCE measurements, including brain volume, cardiac output, cerebral blood flow, haematocrit, and contrast agent response, as well as in practical implementation factors such as cannula size and achievable injection rates [ 8 ]. A structured evaluation of DCE for paediatric brain tumour patients is therefore imperative. This systematic literature review was conducted to 1. evaluate the value of DCE in the diagnostic assessment of paediatric brain tumours and their therapy, and to 2. deliver an overview of DCE paediatric scan protocols for the radiological community. 2. Methods 2.1. Protocol This is a systematic review with a fixed search string and keywords (Table 1 ). Table 1 Search string for Web of Science and PubMed Theme Search terms Aspect 1: Dynamic Contrast Enhanced Magnetic Resonance Imaging Dynamic Contrast Enhanced Magnetic Resonance Imaging, Dynamic Contrast Enhanced MRI, Dynamic Contrast Enhanced Imaging, Dynamic Contrast Enhanced Image, Dynamic CE MRI, Dynamic CE Magnetic Resonance Imaging, Dynamic CE Imaging, Dynamic CE Image, DCE MRI, DCE Magnetic Resonance Imaging, DCE Magnetic Resonance Image, DCE Imaging, DCE Image, Dynamic Contrast Enhanced Perfusion, DCE Perfusion, DCE Perfusion MRI, DCE Perfusion Magnetic Resonance Imaging, DCE Perfusion Magnetic Resonance Image, Dynamic Contrast Enhanced Perfusion MRI, Dynamic Contrast Enhanced Perfusion Magnetic Resonance Imaging, DCE Perfusion Imaging, DCE Perfusion Image, Dynamic Contrast Enhanced T1-Weighted MRI, DCE T1-Weighted MRI, Dynamic Contrast Enhanced T1-Weighted Magnetic Resonance Imaging, DCE T1-Weighted Magnetic Resonance Imaging, Dynamic Contrast Enhanced T1-Weighted Magnetic Resonance Image, DCE T1-Weighted Magnetic Resonance Image, Dynamic Contrast Enhanced T1-Weighted Imaging, DCE T1-Weighted Imaging, Dynamic Contrast Enhanced T1-Weighted Image, DCE T1-Weighted Image, Dynamic Contrast Enhanced Perfusion T1-Weighted Magnetic Resonance Imaging, T1-Weighted Dynamic Contrast-Enhanced Brain MRI, T1-Weighted Dynamic Contrast-Enhanced Brain Magnetic Resonance Imaging, T1-Weighted Dynamic Contrast-Enhanced Brain Magnetic Resonance Image, T1-Weighted Dynamic Contrast Enhanced Magnetic Resonance Imaging, T1-Weighted Dynamic Contrast-Enhanced MRI, T1-Weighted Dynamic Contrast-Enhanced Imaging Aspect 2: Paediatric Paediatrics, Infant, Child, Paediatric, Infant, Child, Children, Children’s, Childhood Aspect 3: Brain Tumour Brain Neoplasms, Brain Stem Neoplasms, Brain Tumour, Brain Tumour, Brian Cancer, Brian Carcinoma, Brain Stem Tumour, Brain Stem Tumour, Cerebral Tumour, Cerebral Neoplasm, Cerebral Cancer, Cerebral Carcinoma, Cerebellar Neoplasms, White Matter Neoplasm, White Matter Tumour, White Matter Tumour, Grey Matter Neoplasm, Grey Matter Tumour, Grey Matter Tumour, Brain Metastasis, Diffuse Low Grade Tumour, Diffuse Low Grade Tumour, Diffuse Low Grade Neoplasm, Low Grade Tumour, Low Grade Tumour, Low Grade Neoplasm, Diffuse High Grade Tumour, Diffuse High Grade Tumour, Diffuse High Grade Neoplasm, High Grade Tumour, High Grade Tumour, High Grade Neoplasm, Subependymal Tumour, Subependymal Tumour, Subependymal Neoplasm, Glioneuronal Tumour, Glioneuronal Tumour, Glioneuronal Neoplasm, Neuronal Tumour, Neuronal Tumour, Neuronal Neoplasm, Dysembryoplastic Neuroepithelial Tumour, Dysembryoplastic Neuroepithelial Tumour, Dysembryoplastic Neuroepithelial Neoplasm, Neuroepithelial Tumour, Neuroepithelial Tumour, Neuroepithelial Neoplasm, Ependymal Tumour, Ependymal Tumour, Ependymal Neoplasm, Choroid Tumour, Choroid Tumour, Choroid Neoplasm, Choroid Plexus Tumour, Choroid Plexus Tumour, Choroid Plexus Neoplasm, Choroid Plexus Neoplasms, Choroid Plexus Carcinoma, Teratoid Tumour, Teratoid Tumour, Rhabdoid Tumour, Rhabdoid Tumour, Mesenchymal Tumour, Mesenchymal Tumour, Mesenchymal Neoplasm, Non-Meningothelial Tumour, Non-Meningothelial Tumour, Non-Meningothelial Neoplasm, Fibroblastic Tumour, Fibroblastic Tumour, Fibroblastic Neoplasm, Myofibroblastic Tumour, Myofibroblastic Tumour, Myofibroblastic Neoplasm, Primary Intracranial Sarcoma, Chondrogenic Tumour, Chondrogenic Tumour, Chondrogenic Neoplasm, Notochordal Tumour, Notochordal Tumour, Notochordal Neoplasm, Intracranial Tumour, Intracranial Tumour, Intracranial Neoplasm, Infratentorial Neoplasms, Supratentorial Neoplasms, Hypothalamic Neoplasms, Glioma, Subependymal, Gliosarcoma, Cerebral Ventricle Neoplasm, Infratentorial Neoplasm, Supratentorial Neoplasm, Hypothalamic Neoplasm, Diffuse Intrinsic Pontine Glioma, Glioma, Glioma, Diffuse Low Grade Glioma, Low Grade Glioma, Diffuse High Grade Glioma, High Grade Glioma, Diffuse Midline Glioma, Midline Glioma, Diffuse Hemispheric Glioma, Hemispheric Glioma, Diffuse Paediatric High Grade Glioma, Infant Type Hemispheric Glioma, Circumscribed Astrocytic Glioma, Astrocytic Glioma, Choroid Glioma, Astrocytoma, Astrocytoma, Diffuse Astrocytoma, Pilocytic Astrocytoma, High Grade Astrocytoma, Pleomorphic Xanthoastrocytoma, Xanthoastrocytoma, Subependymal Giant Cell Astrocytoma, Giant Cell Astrocytoma, Desmoplastic Infantile Astrocytoma, Oligodendroglioma, Oligodendroglioma, Oligodendroblastoma, Oligodendroglioma, Glioblastoma, Glioblastoma, Neoplasms, Neuroepithelial, Polymorphous Low-Grade Neuroepithelial Tumour, Polymorphous Low-Grade Neuroepithelial Neoplasm, Neuroepithelial Tumour, Neuroepithelial Neoplasm, Ganglioglioma, Desmoplastic Infantile Ganglioglioma, Ganglioglioma, Angiocentric Glioma, Astroblastoma, Neurocytoma, Neurocytoma, Liponeurocytoma, Ependymoma, Subependymoma, Ependymoma, Ependymoma, Medulloblastoma, Medulloblastoma, Neuroblastoma, Neuroblastoma, Pineocytoma, Pinealoma, Pineoblastoma, Schwannoma, Neurofibroma, Perineurioma, Paraganglioma, Meningioma, Hemangioblastoma, Medullary Hemangioblastoma, Chondrosarcoma, Mesenchymal, Mesenchymal Chondrosarcoma, Craniopharyngioma, Craniopharyngioma, Pituitary Neoplasm, Pituitary Adenoma, Pituitary Blastoma IRB approval was waived for this research. All four authors screened the articles retrieved. In stage 1, only titles and abstracts were screened. In stage 2, full-text articles were screened. An additional hand search was conducted, thoroughly examining references cited in the selected articles to identify additional literature relevant to the research question. A detailed description of the search, retrieval and selection is provided in Supplementary Material S1. QUADAS-2 risk of bias assessments were performed by all authors, with at least two raters evaluating each article [ 9 ]. Disagreements were resolved by consensus. Compliance with QIBA recommendations for DCE implementation and reporting was assessed for each selected study by an MRI physicist with four years of clinical neuroimaging experience (x.x.). 2.2. Search strategy The search was carried out on the PubMed and Web of Science databases on August 31, 2025, using the building block method, with the search question being divided into three dimensions: 1. Technique (dynamic contrast-enhanced magnetic resonance imaging), 2. Population (paediatric) and 3. Pathology (brain tumour). A set of keywords was defined for each dimension. 2.3. Eligibility assessment Publications that included both adult and paediatric patients were needed to allow for the extraction of results for paediatric patients only. All publications were evaluated for cohort overlap. The diagnosis of a paediatric-type brain tumour was ideally based upon recent pathology classification systems and tissue. Still, tumours without histopathological verification of diagnosis were not excluded, as paediatric tumour patients sometimes do not undergo tissue-based tumour verification, e.g., due to a challenging tumour location. Only English-language publications were included. Case series of five or more patients were allowed due to the low incidence of some paediatric brain tumours. The exclusion criteria comprised studies that did not involve paediatric patients and articles that did not specifically address the use of DCE as a diagnostic tool. Publications not containing original data were also excluded, as were case reports with fewer than five cases. These criteria were used during search stages 1 and 2. 2.4. Data extraction and risk of bias assessment Data extraction included study design, tumour and patient characteristics (number of patients, sex, age, tumour grade and type), and main findings measured in parameters including transfer constant from plasma into the extravascular extracellular space (K trans ) and back (k ep ), extravascular extracellular space volume per unit tissue volume (v e ), and blood plasma volume fraction (v p ). Study results based on pharmacokinetic modelling techniques, including dual-compartment modelling and deconvolution analysis to derive blood volume and flow from DCE data to provide complementary insights into perfusion, were not included. A modified QUADAS-2 instrument was applied (Supplement Material 2) [ 9 ]. Eligibility for meta-analysis was considered based on homogeneity of multiple aspects, including imaging protocol, tumour type, and treatment status. The availability of ≥ 5 studies with most QUADAS-2 categories scoring low or medium risk of bias was the liberal minimum for a meta-analysis. Otherwise, a narrative synthesis summarised the findings. 3. Results 3.1. Overview Figure 2 illustrates the retrieval. The searches rendered 118 articles. After duplicate removal, 95 articles remained for stage 1 screening. 78 articles not meeting the criteria were excluded. Seventeen articles remained for stage 2. At the end of the screening process, eight articles matched the inclusion and exclusion criteria [ 10 – 17 ]. Handsearching resulted in one additional relevant article [ 18 ]. 3.2. Study characteristics and summary of the main study findings Table 2 presents the study characteristics, including study design, study population, and main results related to the research question. Technical DCE sequence details are provided in Table 3 . Table 2 Study characteristics and results Author (year) Study Design Study population Main results Arevalo-Perez et al. (2024) Retrospective 6 patients, 3 girls, 6–16 years old; 2 low-grade ependymoma, 4 anaplastic ependymoma Relative v p, max shows potential for differentiating between low-grade and high-grade ependymomas; however, the study population was too small to draw definitive conclusions. Gupta et al. (2017) Retrospective 64 patients, 8 months-18 years old; 44 high-grade tumours (incl. glioblastomas, anaplastic astrocytomas, anaplastic ependymomas, medulloblastomas, choroid plexus carcinoma, atypical teratoid rhabdoid tumours, gangliogliomas) and 20 low-grade tumours (incl. astrocytomas, ganglioglioma, subependymal giant cell astrocytoma, dysembryoplastic neuroepithelial tumours, pilocytic astrocytomas) Relative blood volume and v p were significantly different between high- and low-grade tumours. v p differentiated low-grade tumours from high-grade with sensitivity 0.75 and specificity 0.65 (cutoff 0.0135). k ep demonstrated a significant difference between posterior fossa ependymomas and medulloblastomas, whereas v e , differentiated not only posterior fossa ependymomas from medulloblastomas but also pilocytic astrocytomas from medulloblastomas. Ho et al. (2025) Retrospective 72 patients, 44 boys, 1.7–211 months old; 36 high-grade tumours (incl. medulloblastomas, atypical teratoid/rhabdoid tumours, anaplastic ependymomas, diffuse midline gliomas, CNS embryonal tumours NOS, high-grade gliomas, anaplastic astrocytomas, ganglioblastoma) and 36 low-grade tumours (incl. pilocytic astrocytomas, dysembryoplastic neuroepithelial tumours, gangliogliomas, desmoplastic infantile ganglioglioma, diffuse astrocytoma, ependymoma, low-grade astrocytoma, low-grade glial neoplasm, low-grade neuroepithelial neoplasm, optic chiasm glioma, pilomyxoid astrocytoma, pleomorphic xanthoastrocytoma) None of the DCE parameters showed a significant difference between low- and high-grade gliomas after statistical correction. Jost et al. (2008) Retrospective 27 patients with optic pathway gliomas; 14 had OPGs associated with neurofibromatosis type 1, 13 had sporadic OPGs; 11 were classified as “clinically stable”, 16 as “clinically aggressive” “Clinically aggressive” OPGs demonstrated significantly higher K PS compared with “clinically stable” OPGs. Among sporadic cases, tumours classified as “clinically aggressive” also exhibited significantly greater permeability values than their “clinically stable” counterparts. Rochetams et al. (2017) Prospective 18 patients, 9 girls, 0.47–15.92 years old; 6 patients with high-grade tumours (incl. rhabdoid tumour, medulloblastomas, high-grade gliomas) and 4 low-grade tumours (pilocytic astrocytomas, DNET, low-grade glioma) Types of concentration-time curves are presented for brain tumours. There was a significant difference in K trans between grade IV and I tumours, but there was no difference in K ep and v e . K trans and v e (but not K ep ) were significantly different in tumour when compared to non-pathological surrounding tissue. Vajapeyam et al. (2020) Prospective 53 patients, 33 girls, 2.5–12.9 years, 3 excluded from the analysis; all 50 patients had DIPGs, 43 died, 45 experienced a PFS event Higher mean K trans and mean v e were associated with shorter OS and PFS. Maximum K trans was associated with PFS. Vajapeyam et al. (2018) Retrospective 41 patients, 23 boys, 0.3-16.76 years old; 31 infratentorial and 10 supratentorial tumors; 16 low-grade (7 pilocytic astrocytomas, 5 low-grade gliomas, 1 mature teratoma, 1 atypical meningioma, 1 low-grade ganglioglioma, and 1 low-grade mixed germ cell tumor), 25 high-grade tumors (12 medulloblastomas, 4 glioblastomas, 4 anaplastic ependymomas, and 1 each of atypical teratoid/rhabdoid tumor, embryonal tumor not otherwise specified, choroid plexus carcinoma, embryonal tumor with rhabdoid features, and diffuse midline glioma) K trans , k ep , v e showed a significant difference between high- and low-grade tumours. ROC analysis has demonstrated good discriminatory performance for K trans (AROC = 0.883, CI 0.781–0.984), k ep (AROC = 0.908, CI 0.815-1.0), and ve (AROC = 0.843, CI 0.713–0.972) in distinguishing between high- and low-grade tumours. Vajapeyam et al. (2017) Retrospective 38 patients, 24 boys, 0.3-18.14 years; 18 low-grade tumors (7 pilocytic astrocytomas, 3 low-grade gliomas with piloid features, 3 low-grade gliomas, 1 low-grade ependymoma, 1 atypical meningioma WHO II, 1 hemangioblastoma grade I, 1 ganglioglioma grade I–II, 1 low-grade histiocytic sarcoma) and 20 high-grade tumors (11 medulloblastomas, 3 glioblastoma multiformes, 2 anaplastic ependymomas, 1 high-grade sarcoma, 1 choroid plexus carcinoma, 1 germinomatous germ cell tumor, and 1 high-grade glioma). K trans , k ep , v e showed a significant difference between populations of high- and low-grade tumours with high sensitivity (> 0.7) and specificity (> 0.82). Zukotynski et al. (2013) Prospective 24 patients, 13 girls; 7 supratentorial HGGs, 9 LGGs, 4 BSGs, 2 medulloblastomas, 2 ependymomas No statistically significant difference in K ps, max was observed between children with HGG/BSG and those with LGG. K ps, max was not significantly correlated with PFS. Caption: Abbreviations: AROC - area under the receiver operating curve, BSG - brainstem glioma, CNS - central nervous system, DIPG - diffuse intrinsic pontine glioma, DNET - dysembryoplastic neuroepithelial tumour, HGG - high-grade glioma, LGG - low-grade glioma, NOS - not otherwise specified, OPG - optic pathway glioma, OS - overall survival, PFS - progression-free survival, WHO - World Health Organization Vajapeyam et al. (2018) and Vajapeyam et al. (2017) were considered to contain cohort overlap. This included all the patients from the 2018 study, as well as 1 low-grade ependymoma, 1 hemangioblastoma grade I, 1 low-grade histiocytic sarcoma, and 1 high-grade sarcoma included in Vajapeyam et al. 2017. Table 3 Technical details of DCE sequence Author (year) Scanner and field strength Head coil type Contrast agent type and dose Sequence type and flip angle (FA) Number of phases/time resolution Coverage / in-plane sampling (FOV /matrix size) T1/B1 map acquisition Processing Arevalo-Perez et al. (2024) Mixed: 1.5T Optima GE, 3T Signa Premier GE 8-channel head coil Gatobutrol (Gadavist, Bayer) 0.1 mmol/kg, delivered with a power injector at 2–3 mL/s via an 18–21 gauge venous catheter 3D T1-weighted fast-spoiled (fast echo-spoiled) gradient-echo sequence; FA 25°; TR 4–5 ms; TE 1–2 ms Temporal resolution 5–6 s; 10 pre-injection phases and 30 post-injection phases FOV 24 cm, matrix 128 x 128, slice thickness 3 mm, 10–12 axial images No T1/B1 mapping reported Software: NordicIce Preprocessing: spatial and temporal smoothing AIF: individually computed from MCA Model: extended Tofts two-compartment Gupta et al. (2017) 3T Philips 15-channel head coil Gd-BOPTA (Multihance, Bracco, Italy) 0.1 mmol/kg, injected via a power injector at 1.5–2 mL/s via 22–24 gauge cannulas T1 fast gradient echo; FA 10°; TR/TE = 5.0/1.4 ms Temporal resolution ~ 3.9 s, 32 dynamics (4 pre-injection) FOV 24 cm, 128 x 128, slice thickness 6 mm, 12 axial slices Pre-contrast T1 mapping reported Software: not specified Preprocessing: not specified AIF: automated extraction Model: leaky tracer kinetic model Ho et al. (2025) 3T Magnetom Skyra Siemens Not specified Gd-BOPTA, two doses 0.1 mmol/kg each, injected via power injector at 5 mL/s when 18–20 G IV access was possible; 24 G used in smaller children T1-weighted gradient echo; FA 10°; TR/TE = 1.54/3.91 ms 100 time points over 4.5 min, ~ 2.7 s per phase; unclear how many phases before/after injection FOV not reported, matrix 154 x 192, slice thickness 5 mm, 20 axial slices No T1/B1 mapping reported Software: IDL package “Qimage” Preprocessing: FSL for motion correction and registration AIF: Manual MCA ROI Model: extended Tofts model Jost et al. (2008) Mixed: 1.5T Sonata Siemens and 3T Trio Siemens Not specified Contrast agent not specified, 0.1 mmol/kg, injection method not specified T1-weighted 3D FLASH, FA not specified, TR/TE = 30/6 ms Dynamic series not stated, dynamic duration 6 min FOV ~ 128x128 mm 2 , matrix 128 x 128, in-plane voxel 1 mm 2 , slice thickness 3, 16 axial slices VFA T1 mapping (FAs 10°, 15°, 25°) Software: customised MATLAB Preprocessing: coregistration with Intelli-link AIF: blood ROI selection not specified Model: Patlak Rochetams et al. (2017) 1.5T Magnetom Aero Siemens 20-channel head coil Gadoteric acid (Dotarem, Guerbet), 0.2 mL/kg, injected with power injector at 1mL/s via peripheral 22G IV 3D T1-weighted gradient echo; FA 12°; TR/TE = 4.46/1.72 ms 56 time points, temporal resolution ~ 3.1 s, dynamic duration 2:53 min FOV 230 × 186 mm², matrix 166 x 256, slice thickness 3 mm No T1/B1 mapping reported Software: from vendor (Syngo MR Tissue 4D, Siemens) Preprocessing: not specified AIF: automatic extraction Model: extended Tofts two-compartment Vajapeyam et al. (2020) 3T (unspecified) Not specified Gadobutrol, two half-doses (0.05 mmol/kg each), injection not specified 3D T1-weighted fast gradient echo; FA 15°; TR 4 s, TE minimum 50 timepoints, ~ 7 s/phase, contrast injection after 20 s FOV 24 cm, matrix not specified, slice thickness 5 mm VFA T1 mapping (FAs 15°, 10°, 5°, 2°) Software: DynaCAD (Invivo) with OmniLook Preprocessing: not specified AIF: not specified Model: extended Tofts two-compartment Vajapeyam et al. (2018) 3T Siemens Not specified Gadobutrol, 0.1 mL/kg at injection rate 2mL/s, the rest not specified 3D T1-weighted fast gradient echo; FA 15°; T4 4 s, TE minimum 50 timepoints, ~ 7 s/phase, contrast injection after 20 s FOV 24 cm, matrix not specified, slice thickness 5 mm VFA T1 mapping (FAs 15°, 10°, 5°, 2°) Software: VersaVue (Invivo) with OmniLook Preprocessing: not specified AIF: not specified Model: extended Tofts two-compartment Vajapeyam et al. (2017) 3T Siemens Not specified Gadobutrol, 0.1 mL/kg at injection rate 2mL/s, the rest not specified 3D T1-weighted fast gradient echo; FA 15°; T4 4 s, TE minimum 50 timepoints, ~ 7 s/phase, contrast injection after 20 s FOV 24 cm, matrix not specified, slice thickness 5 mm VFA T1 mapping (FAs 15°, 10°, 5°, 2°) Software: VersaVue (Invivo) with OmniLook Preprocessing: not specified AIF: not specified Model: extended Tofts two-compartment Zukotynski et al. (2013) 1.5T (not specified) Not specified Not specified 3D T1-weighted spoiled gradient echo; FA 30°; TR/TE minimum (not specified) 40 dynamics, temporal resolution not specified FOV 24 cm, matrix ≥ 128 x 128, 16 axial slices No T1/B1 mapping reported Software: in-house IDL software Preprocessing: not specified AIF: not specified Model: not specified Caption: Abbreviations: AIF - arterial input function; FA - flip angle; FOV - field of view; IV - intravenous; ROI - region of interest; T - Tesla; TR - repetition time; TE - echo time; VFA - variable flip angle Studies ranged in cohort size from 6 to 72 patients. Only one study applied the most recent World Health Organization Classification of Tumours of the Central Nervous System (WHO) of 2021 [ 16 , 19 ]. Three studies investigated histologically confirmed tumours only, while two included some tumours that were histologically confirmed, and four did not mention whether the tumours were histologically evaluated. Four studies investigated untreated tumours, while two dealt with therapy evaluation, and three contained treated and untreated lesions. The criteria for a meta-analysis were not met. Considerable heterogeneity was observed across cohorts in tumour types, treatment status, and evaluation systems used to diagnose and categorise pathologies. Subsequently, a narrative synthesis was conducted to summarise the key findings of the included studies. 3.3. Risk of bias and QIBA compliance Most studies showed a low risk of bias (Fig. 3 ), with patient selection description scoring the lowest (Fig. 3 ). QIBA compliance, based on the technical parameters presented in Table 3 , was limited to low (Fig. 3 ), with no study being fully compliant with the recommendations on implementation and reporting. 3.4. Noninvasive tumour differentiation Four studies investigated DCE parameters, including K trans , k ep , v e , v p in newly diagnosed, treatment-naive patients to differentiate between low and high-grade paediatric primary brain tumours [ 10 – 12 , 15 , 16 ]. The cumulative number of cases is 186. Only Ho et al. applied the most recent WHO CNS 5 classification in their analysis of a mixed primary brain tumour cohort examining the capability of DCE to differentiate low- from high-grade lesions [ 16 ]. They present the largest cohort of 36 high-grade (WHO 3 and 4) and 36 low-grade primary brain tumours. No extended Tofts model DCE parameter significantly differed between both groups after Bonferroni correction. Gupta et al., in their mixed primary pediatric brain tumour study of 64 patients, did not find any significant Bonferroni-corrected differences in extended Tofts model kinetic parameters across different tumour grades, corroborating results by Ho et al. [ 15 ]. They did, however, report significantly different (p = 0.036) K trans values between different posterior fossa tumours, namely between pilocytic astrocytomas (0.48, 0.17–1.25) and medulloblastomas (0.01, 0.0-0.74). They also observed that ependymomas (8.25 (3.98–17.91) and pilocytic astrocytomas (8.68 (5–79.05) had higher k ep than medulloblastomas (2.89 (0.0-10.77); P = 0.012). Moreover, Gupta et al. reported that medulloblastomas had significantly lower v e compared to pilocytic astrocytomas and ependymomas (P = 0.003 and 0.012, respectively). Vajapeyam et al. (2018) present a comparable mixed histology setup of 41 cases, aiming to differentiate between WHO I and II (LGG by the 2016 WHO CNS classification) and grade III/IV (n = 25) using extended Tofts model parameters [ 11 ]. They conclude that significant differences were observed between groups for K trans , k ep (both higher in HGG), and v e (lower in HGG). Statistical error correction was not stated. They had previously published a part of their cohort (with a great overlap) and concluded that all three parameters had high specificity (range, 82–100%), while the best sensitivity was achieved for v e (combined sensitivity 76% vs. 71% for K trans and k ep ) [ 10 ]. In summary, two mixed-lesion studies did not identify any differentiation potential for DCE (cumulative cases, n = 136). In contrast, two studies based on two overlapping datasets, totalling approximately 50 cases, identified diagnostic potential. In a homogeneous cohort of 50 diffuse intrinsic pontine glioma patients (histological confirmation unclear) treated with the same study protocol (veliparib plus radiotherapy followed by temozolomide), Vajapeyam et al. later examined the power of extended Tofts model DCE parameters measured in treatment-naive tumours to predict survival [ 12 ]. A higher K trans at baseline was associated with shorter overall and progression-free survival, although this did not reach statistical significance thresholds. 3.5. Studies with unknown treatment status at the time of scanning Three studies did not specify patient treatment status and included both retrospective and prospective cohorts [ 13 , 14 , 17 ]. Rochetams et al. [ 13 ] present 10 pediatric patients with mixed gliomas (4 LGG WHO I/II, 6 HGG) of unknown therapy status, with K trans ratios being significantly different between grade I and grade IV brain tumours ( P = 0.027), while other differentiations were not possible. Grade IV paediatric brain tumours were distinctly defined by a K trans ratio above 0.63 [ 13 ]. K trans -based differentiation between pilocytic astrocytoma and medulloblastoma was impossible. Jost et al. [ 14 ] present a study of optic pathway LGG patients with unknown therapy status, where they attempted to predict whether a patient would remain clinically stable (n = 11) or develop aggressive disease (n = 16). However, the relation between MRI and clinical judgment is unclear [ 14 ]. Nonetheless, they found significantly higher ( P = 0.05) mean K trans surface area product (KPS) in clinically aggressive optic pathway gliomas (2.24 ml/min per 100 cm³) compared to clinically stable tumours (1.38 ml/min per 100 cm³). All clinically stable tumours had a value of < 2.0 ml/min per 100 cm³. To further support the observed correlation between vascular permeability and aggressiveness, a significantly higher mean permeability value (2.77 ml/min per 100 cm 3 ) was observed in sporadic tumours classified as clinically aggressive compared to those classified as clinically stable (< 2.0 ml/min per 100 cm 3 ) ( P < 0.05). Arevalo-Perez et al. present a 2024 study to differentiate low-grade cerebral ependymoma from anaplastic ones [ 17 ]. However, only four cases were anaplastic, and two were low-grade. The v pmax was, however, following trends in the adults with higher values in anaplastic lesions (mean 17.44%) than in low-grade lesions (mean 9.65%). Study quality is impaired by unknown treatment status. 3.6. Treatment Evaluation Studies Two studies evaluated tumour parameters during treatment using the above-mentioned DCE parameters to monitor changes in vascular permeability and perfusion characteristics over time [ 12 , 18 ]. Zukotynski et al. [ 18 ] examined 24 children with mixed primary brain tumour histologies under bevacizumab and irinotecan treatment before, during, and after treatment, with variable data completeness, and only analysed K trans (referred to as Kps in the paper). K trans did not differ between LGG and HGG at baseline (n = 21; P = 0.56). K trans was not associated with progression-free survival, but with one exception, lowered during therapy. The 2020 Vajapeyam et al. publication [ 12 ] also contained follow-up scans showing that when analysed as continuous time-dependent variables, associations of mean v e with progression-free survival (P = 0.03) and overall survival (P = 0.03), as well as maximum K trans with progression-free survival (P = 0.03) were near significant. Greater kinetic parameter increases with time were associated with worse outcomes. Kinetic parameters showed no difference between pseudoprogression and true early progression groups. 4. Discussion 4.1. Critical summary This systematic review on DCE in paediatric brain tumour patients illustrates that only a very few studies have focused on the topic, which is further complicated by substantial heterogeneity in cases and technical execution. 4.2. Clinical findings A total of nine smaller-scale studies, partially with cohort overlap, unclear or outdated tumour histology, and differing research questions, as well as equivocal scanning parameters, underscore the substantial knowledge gap for DCE applications in paediatric neuro-oncological imaging. Regarding the differentiation of low-grade and high-grade brain tumours, data relies on four to five studies, with a majority of samples indicating that differentiation is not possible using DCE kinetic parameters. However, the subgroup analyses of these studies suggest that this may be due to the histological heterogeneity of these cohorts, and that a differentiation between two types of tumours may be possible, as indicated by the results of Gupta et al. for pilocytic astrocytoma versus medulloblastoma [ 15 ]. Rochetams et al., however, could not find a difference for these two tumour entities, which may be due to their very small sample size of five cases for both groups in total [ 13 ]. The equivocal results of the published tumour malignancy prediction studies underscore another issue: Is it clinically useful to attempt a broad, mixed brain tumour differentiation across all types of entities? Should future studies not focus on differentiating two entities (like medulloblastoma and pilocytic astrocytoma), as this is difficult enough? Furthermore, we could not identify a single study that focuses on the molecular differentiation of tumour entities, which, however, is key to modern tumour diagnostics. Approaches, therefore, need to change substantially in the future. Examining the published nine studies, it is notable that only a minority focused on histologically and clinically comparable cases, such as the optic pathway glioma study by Jost et al., which should be the standard when the sample size is relatively small. In this context, clinical reproducibility is an important factor in advanced MR imaging. While not fitting the inclusion criteria of this publication, the authors want to draw the attention to the publication by Miyazaki et al., who showed in eight pediatric glioma cases that only K trans had a variation coefficient below 20% and that the arterial input function (AIF) may show sharper and earlier first pass peaks than in adults having an impact on dynamic scan planning [ 20 ]. On the other hand, Carceller et al.’s cohort of five HGG patients showed the lowest variation coefficient for v e (2.9%), while also K trans and k ep remained below 20% [ 21 ]. This means that future studies of DCE use in paediatric brain tumour patients must, on the one hand, critically evaluate which parameters to take into account, and, on the other, that AIF selection in the smaller vessels of paediatric brains remains an unsolved but relevant issue. Both are pivotal aspects, particularly in the currently underexplored field of follow-up studies in the paediatric cohort, which leads to the topic of technical implementation in paediatric MRI. 4.3. Technical implementation of DCE in pediatric populations The reviewed studies illustrate a high heterogeneity in DCE implementation across clinical research settings. Notably, there was a substantial underreporting of key acquisition parameters, particularly with respect to head coil specifications, contrast agent injection parameters, temporal sampling, and post-processing. Scanner field strengths ranged from 1.5 T to 3 T with variable head coils (8–20 when reported). Contrast administration protocols differed in both type and dosing – most commonly gadobutrol or Gd-BOPTA at 0.1 mmol/kg - although half-dose and repeat-dose strategies were also reported in combination with dynamic susceptibility contrast perfusion MRI, and injection rates varied from 1 to 5 mL/s. Only a subset of studies incorporated pre-contrast T1 mapping, and B1 mapping was rarely performed. AIF selection varied substantially, from manual middle cerebral artery regions of interest selection to automated extractions, followed by parameter quantification with the extended Tofts two-compartment, Patlak, and leaky tracer kinetic models. Despite the persistent application of DCE in pediatric neuro-oncology, there are currently no guidelines that specifically address its implementation in children. Existing recommendations, such as QIBA [ 7 ], have been developed for adult populations only, and they do not incorporate practical pediatric considerations such as smaller intravenous catheter gauge size [ 22 ], the risk of extravasation at higher injection pressures [ 23 ], or the need for adjusted dosing strategies [ 24 ]. In practice, pediatric patients frequently require the use of 18–21 G catheters, which restrict achievable injection rates. Consequently, lower injection rates are often employed to reduce the risk of extravasation, but slower infusion flattens the AIF peak and compromises the fidelity of tracer kinetic modelling [ 25 ]. Reduced AIF sharpness directly limits the accuracy of quantitative parameters such as K trans , which are sensitive to bolus profile [ 26 ]. This gap between existing technical standards and the realities of pediatric practice highlights the need for tailored guidelines that strike a balance between patient safety and the demands of robust pharmacokinetic analysis. A consistent theme across published studies is the systematic failure to both implement and report acquisitions and analyses in line with QIBA recommendations. Key acquisition parameters, including head coil type, contrast injection details, time resolution, and coverage, were frequently underreported, while essential calibration measures such as T1 and B1 mapping were often omitted. Similarly, descriptions of preprocessing pipelines, AIF definition, and model selection were highly variable or often insufficiently documented, making them difficult to reproduce. The lack of adherence across studies further limits clinical translation and the establishment of robust quantitative biomarkers for pediatric neuro-oncology. Community efforts, such as the Open-Source Initiative for Perfusion Imaging (OSIPI), provide repositories of openly shared software tools and reference data processing pipelines to promote reproducible quantitative DCE imaging [ 27 ]. Greater alignment with QIBA recommendations and broader adoption of reproducible workflows will be essential for advancing multicenter validation of DCE in children. 4.4. Limitations of current studies While DCE demonstrates potentially useful correlations between pediatric brain tumour grade and parameters, including K trans , k ep , v e , and v p , there are several limitations in the present review that should be considered when interpreting the results. One important discrepancy in this review is that several included studies varied in patient cohort characteristics, statistical methodologies, and imaging protocols, which limits direct comparability and reduces the generalisability of the findings across clinical contexts. Another limitation concerns the substantial variability and discrepancy in tumour type evaluation and grade classification systems. Only four of the nine studies reported compliance with any WHO CNS classification, and of these, only two specified the edition of the WHO CNS classification used. Additionally, several studies did not include histological verification. This may impact the accuracy of the overall conclusions drawn by this review regarding the correlation of DCE parameters with tumour grade and type. For some tumour discriminations, e.g, regarding medulloblastoma vs. pilocytic astrocytoma, the impact of the transition from WHO CNS 2016 to 2021 may be small, and comparisons between such studies can still be made [ 28 ], while paediatric glioma classifications underwent substantial adaptations in the 2021 WHO classification [ 29 ]. Another crucial limitation identified by this review is the small sample sizes of the studies. This limits the statistical power of the findings and makes it challenging to draw firm conclusions regarding the prognostic value of DCE parameters. Furthermore, measurement discrepancies may have been introduced by the inconsistent methodologies across studies. Variations in acquisition parameters, including temporal resolution, field strength, contrast agent types, and processing pipelines, are a significant limitation to comparability. Variation in arterial input function (AIF) selection is another relevant issue that may have affected clinical outcomes of the presented studies [ 30 ]. The inability to directly compare among studies is exacerbated by the use of different processing models (e.g., Tofts, Patlak, or Kermode models), as each model highlights different aspects of tumour physiology and may not accurately reflect all tumour types. This may pose challenges in producing consistently reliable and accurate results across tumour subtypes. Lastly, while DCE shows promise in providing insights into tumour vascularity, it possesses some inherent technical limitations. For example, DCE is highly sensitive to motion artefacts, which are particularly evident in paediatric patients who may have trouble staying still during their procedure. Additionally, the use of contrast agents carries a small risk of side effects [ 8 ], although these are generally rare. 4.5. Future Directions for DCE in Paediatric Neuro-Oncology Future studies implementing DCE in paediatric neuro-oncology should aim to align more closely with the QIBA recommendations while adapting protocols to the practical constraints of paediatric cohorts. This includes the use of standardised acquisition schemes, a rigorous documentation of contrast injection parameters, and transparent reporting of data processing. Importantly, recommendations specifically tailored to the paediatric population are still lacking, and developing such consensus guidelines will be crucial to strike a balance between technical rigour and the clinical demands of imaging children. In addition, since paediatric tumours are rare and published sample sizes are limited, the creation of inter-institutional databases that compile DCE acquisition and pharmacokinetic parameters would be invaluable, enabling more robust analyses and facilitating clinically translatable biomarkers. Beyond technical harmonisation, future DCE studies should address biologically and clinically more meaningful questions, moving away from broad comparisons among heterogeneous tumour entities and instead focusing on molecular markers, limited entities, and clinically relevant outcomes to strengthen their translational value. This is particularly true for post-treatment studies. These steps would foster greater reproducibility, comparability, and clinical impact of DCE in the pediatric neuro-oncology domain, particularly for the benefit of young, highly vulnerable patients. 4.6 Conclusion DCE MRI may deliver valuable non-invasive imaging biomarkers in paediatric patients with brain tumours, as indicated by several studies. However, the low published sample sizes and technical and case heterogeneity do not currently allow for firm conclusions. Dedicated paediatric DCE MRI protocols, as well as clinically focused studies, are needed, indicating that patients currently do not benefit from clinically used DCE due to still too low levels of evidence, which hampers reliable kinetic parameter interpretation. Abbreviations AIF arterial input function DCE dynamic contrast-enhanced k ep rate constant from extravascular extracellular space back into blood plasma K trans transfer constant from blood plasma into extravascular extracellular space PS permeability × surface area product per unit mass of tissue v e extravascular extracellular volume fraction v p blood-plasma volume fraction Declarations Author Contribution All authors reviewed the articles used for the review.All authors participated in the risk of bias analysis.All authors participated in the writing and review process.All authors worked on image material. V.C.K. was the primary supervisor to this work.Y.P. was the technical and statistical supervisor for this work. Acknowledgement We would like to acknowledge Jakub Otáhal, Martin Kynčl, and David Kala for their contributions to project management, patient recruitment, and guidance on contrast data acquisition at University Hospital Motol. Data Availability IRB approval was waived for this research. All four authors screened the articles retrieved. In stage 1, only titles and abstracts were screened. In stage 2, full-text articles were screened. An additional hand search was conducted, thoroughly examining references cited in the selected articles to identify additional literature relevant to the research question. A detailed description of the search, retrieval and selection is provided in Supplementary Material S1. QUADAS-2 risk of bias assessments were performed by all authors, with at least two raters evaluating each article [[9]](https:/paperpile.com/c/ZCQTw0/rezc) . Disagreements were resolved by consensus. Compliance with QIBA recommendations for DCE implementation and reporting was assessed for each selected study by an MRI physicist with four years of clinical neuroimaging experience (x.x.). References Hossain MJ, Xiao W, Tayeb M, et al. Epidemiology and prognostic factors of pediatric brain tumor survival in the US: Evidence from four decades of population data. Cancer Epidemiol 2021; 72: 101942 Oyefiade A, Paltin I, De Luca CR, et al. Cognitive risk in survivors of pediatric brain tumors. J Clin Oncol 2021; 39: 1718–1726 Goo HW, Ra Y-S. Advanced MRI for pediatric brain tumors with emphasis on clinical benefits. Korean J Radiol 2017; 18: 194–207 Hirschler L, Sollmann N, Schmitz-Abecassis B, et al. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; doi:10.1002/jmri.28662 Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999; 10: 223–232 Keil VC, Gielen GH, Pintea B, et al. DCE-MRI in Glioma, Infiltration Zone and Healthy Brain to Assess Angiogenesis: A Biopsy Study. Clin Neuroradiol 2021; 31: 1049–1058 Shukla-Dave A, Obuchowski NA, Chenevert TL, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019; 49: i – i Ouyang M, Bao L. Gadolinium contrast agent deposition in children. J Magn Reson Imaging 2025; 61: 70–82 Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155: 529–536 Vajapeyam S, Stamoulis C, Ricci K, et al. Automated processing of dynamic contrast-enhanced MRI: Correlation of advanced pharmacokinetic metrics with tumor grade in pediatric brain tumors. AJNR Am J Neuroradiol 2017; 38: 170–175 Vajapeyam S, Brown D, Johnston PR, et al. Multiparametric analysis of permeability and ADC histogram metrics for classification of pediatric brain tumors by tumor grade. AJNR Am J Neuroradiol 2018; 39: 552–557 Vajapeyam S, Brown D, Billups C, et al. Advanced ADC histogram, perfusion, and permeability metrics show an association with survival and pseudoprogression in newly diagnosed diffuse intrinsic pontine glioma: A report from the Pediatric Brain Tumor Consortium. AJNR Am J Neuroradiol 2020; 41: 718–724 Rochetams B-B, Marechal B, Cottier J-P, et al. T1-weighted dynamic contrast-enhanced brain magnetic resonance imaging: A preliminary study with low infusion rate in pediatric patients. Neuroradiol J 2017; 30: 429–436 Jost SC, Ackerman JW, Garbow JR, et al. Diffusion-weighted and dynamic contrast-enhanced imaging as markers of clinical behavior in children with optic pathway glioma. Pediatr Radiol 2008; 38: 1293–1299 Gupta PK, Saini J, Sahoo P, et al. Role of dynamic contrast-enhanced perfusion magnetic resonance imaging in grading of pediatric brain tumors on 3T. Pediatr Neurosurg 2017; 52: 298–305 Ho CY, Supakul N, Anthony G, et al. Perfusion showdown: Comparison of multiple MRI perfusion techniques in the grading of pediatric brain tumors. AJNR Am J Neuroradiol 2025; 46: 1464–1470 Arevalo-Perez J, Yllera-Contreras E, Peck KK, et al. Differentiating low-grade from high-grade intracranial ependymomas: Comparison of dynamic contrast-enhanced MRI and diffusion-weighted imaging. AJNR Am J Neuroradiol 2024; 45: 927–933 Zukotynski KA, Fahey FH, Vajapeyam S, et al. Exploratory evaluation of MR permeability with 18 F-FDG PET mapping in pediatric brain tumors: A report from the pediatric brain tumor consortium. J Nucl Med 2013; 54: 1237–1243 Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 2021; 23: 1231–1251 Miyazaki K, Jerome NP, Collins DJ, et al. Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study. Eur Radiol 2015; 25: 2641–2650 Carceller F, Jerome NP, Miyazaki K, et al. Feasibility and applicability of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in routine assessments of children with high-grade gliomas. Pediatr Blood Cancer 2017; 64: 279–283 Bennett J, Cheung M. Intravenous access in children. Paediatr Child Health (Oxford) 2020; 30: 224–229 Amaral JG, Traubici J, BenDavid G, et al. Safety of power injector use in children as measured by incidence of extravasation. AJR Am J Roentgenol 2006; 187: 580–583 Yu OJ, Kim PH, Yoon HM, et al. Safety of gadolinium-based contrast agents in children: A systematic review and meta-analysis. Radiology 2025; 316: e241224 Wang Y, Huang W, Panicek DM, et al. Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data. Magn Reson Med 2008; 59: 1183–1189 Klawer EME, van Houdt PJ, Pos FJ, et al. Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer. NMR Biomed 2018; 31: e3946 van Houdt PJ, Ragunathan S, Berks M, et al. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91: 1774–1786 Cotter JA, Hawkins C. Medulloblastoma: WHO 2021 and beyond. Pediatr Dev Pathol 2022; 25: 23–33 Gonçalves FG, Viaene AN, Vossough A. Advanced magnetic resonance imaging in pediatric glioblastomas. Front Neurol 2021; 12: 733323 Keil VC, Mädler B, Gieseke J, et al. Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI. Magn Reson Imaging 2017; 40: 83–90 Additional Declarations No competing interests reported. Supplementary Files Supplement1.docx Supplement2.docx Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2026 Read the published version in Pediatric Radiology → Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 02 Oct, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 05 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":1296754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003edepicting K\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003etrans\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u003cstrong\u003e measurements in different pediatric brain tumours.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7543940/v1/2df551d9b0478e7776f2c78e.png"},{"id":91937903,"identity":"b07aa6ca-7201-48c7-9535-879b3656edba","added_by":"auto","created_at":"2025-09-23 03:03:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132096,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study selection process.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7543940/v1/b182ea268640c9213c2452b3.png"},{"id":91937909,"identity":"61e51710-8f4f-4afa-a5ca-e3873986e859","added_by":"auto","created_at":"2025-09-23 03:03:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRisk of bias analysis of fitting paediatric DCE studies and protocol compliance with QIBA recommendations.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7543940/v1/6db55028f0b9fbe101b8258b.png"},{"id":106343932,"identity":"f2dd103e-c636-45f5-b2f5-f93882272620","added_by":"auto","created_at":"2026-04-07 16:10:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2939476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7543940/v1/88a3f91c-1ec3-4383-92e0-6c32cf591a1f.pdf"},{"id":91939423,"identity":"e3a8cea4-a5c7-450d-83dc-400cf64f1ca1","added_by":"auto","created_at":"2025-09-23 03:11:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11771,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7543940/v1/767f8fee77758f6ce526315a.docx"},{"id":91939424,"identity":"2b4e54fd-faf9-4f66-9863-49b1e6121d8d","added_by":"auto","created_at":"2025-09-23 03:11:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22375,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7543940/v1/91444b78a137e769e1711c52.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic contrast-enhanced Magnetic Resonance Imaging in Paediatric Brain Tumours Systematically Reviewed","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBrain tumours rank as the most lethal, as well as the second most common type of cancer in paediatric patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Paediatric patients are 5 of biomarkers that avoid surgery to classify brain tumours and evaluate treatment response due to their vulnerability and the frequently surgically unfavourable tumour locations. Reliable noninvasive biomarkers may also improve cognitive outcome in this population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMagnetic Resonance Imaging (MRI) is the key pillar of brain tumour therapy planning and surveillance, including in the pediatric population. Still, conventional MRI tumour protocols often fail to adequately capture the biological complexity of brain tumours, limiting their ability to accurately assess tumour vitality and malignancy. Advanced MRI techniques, on the other hand, show potential in improving the diagnostic performance in differentiating brain tumours in the paediatric population as well as the evaluation of treatment response [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDynamic contrast-enhanced (DCE) MRI is an advanced quantitative MRI technique that is well-established for evaluating adult-type brain tumours [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. DCE is a contrast agent injection-dependent sequence aiming to deliver insights into blood-brain barrier integrity by quantifying vascular permeability and perfusion-related parameters [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A significant correlation has been observed between DCE kinetic parameters and tumour grade in adult brain tumours [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the most common low-grade paediatric brain tumour, pilocytic astrocytoma, also typically shows blood-brain barrier disruption with pronounced contrast enhancement, in contrast to the more restrained enhancement patterns observed in adult low-grade gliomas or other paediatric low-grade tumours (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nevertheless, systematic data on DCE measurements in paediatric gliomas remain scarce and underrepresented.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom a technical perspective, the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA) suggested standardised scan parameters and processing for adults to increase DCE reproducibility without a pediatric counterpart [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Children differ, however, from adults in several aspects relevant to DCE measurements, including brain volume, cardiac output, cerebral blood flow, haematocrit, and contrast agent response, as well as in practical implementation factors such as cannula size and achievable injection rates [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA structured evaluation of DCE for paediatric brain tumour patients is therefore imperative. This systematic literature review was conducted to 1. evaluate the value of DCE in the diagnostic assessment of paediatric brain tumours and their therapy, and to 2. deliver an overview of DCE paediatric scan protocols for the radiological community.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Protocol\u003c/h2\u003e\u003cp\u003eThis is a systematic review with a fixed search string and keywords (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSearch string for Web of Science and PubMed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSearch terms\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAspect 1: Dynamic Contrast Enhanced Magnetic Resonance Imaging\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDynamic Contrast Enhanced Magnetic Resonance Imaging, Dynamic Contrast Enhanced MRI, Dynamic Contrast Enhanced Imaging, Dynamic Contrast Enhanced Image, Dynamic CE MRI, Dynamic CE Magnetic Resonance Imaging, Dynamic CE Imaging, Dynamic CE Image, DCE MRI, DCE Magnetic Resonance Imaging, DCE Magnetic Resonance Image, DCE Imaging, DCE Image, Dynamic Contrast Enhanced Perfusion, DCE Perfusion, DCE Perfusion MRI, DCE Perfusion Magnetic Resonance Imaging, DCE Perfusion Magnetic Resonance Image, Dynamic Contrast Enhanced Perfusion MRI, Dynamic Contrast Enhanced Perfusion Magnetic Resonance Imaging, DCE Perfusion Imaging, DCE Perfusion Image, Dynamic Contrast Enhanced T1-Weighted MRI, DCE T1-Weighted MRI, Dynamic Contrast Enhanced T1-Weighted Magnetic Resonance Imaging, DCE T1-Weighted Magnetic Resonance Imaging, Dynamic Contrast Enhanced T1-Weighted Magnetic Resonance Image, DCE T1-Weighted Magnetic Resonance Image, Dynamic Contrast Enhanced T1-Weighted Imaging, DCE T1-Weighted Imaging, Dynamic Contrast Enhanced T1-Weighted Image, DCE T1-Weighted Image, Dynamic Contrast Enhanced Perfusion T1-Weighted Magnetic Resonance Imaging, T1-Weighted Dynamic Contrast-Enhanced Brain MRI, T1-Weighted Dynamic Contrast-Enhanced Brain Magnetic Resonance Imaging, T1-Weighted Dynamic Contrast-Enhanced Brain Magnetic Resonance Image, T1-Weighted Dynamic Contrast Enhanced Magnetic Resonance Imaging, T1-Weighted Dynamic Contrast-Enhanced MRI, T1-Weighted Dynamic Contrast-Enhanced Imaging\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAspect 2: Paediatric\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaediatrics, Infant, Child, Paediatric, Infant, Child, Children, Children’s, Childhood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAspect 3: Brain Tumour\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrain Neoplasms, Brain Stem Neoplasms, Brain Tumour, Brain Tumour, Brian Cancer, Brian Carcinoma, Brain Stem Tumour, Brain Stem Tumour, Cerebral Tumour, Cerebral Neoplasm, Cerebral Cancer, Cerebral Carcinoma, Cerebellar Neoplasms, White Matter Neoplasm, White Matter Tumour, White Matter Tumour, Grey Matter Neoplasm, Grey Matter Tumour, Grey Matter Tumour, Brain Metastasis, Diffuse Low Grade Tumour, Diffuse Low Grade Tumour, Diffuse Low Grade Neoplasm, Low Grade Tumour, Low Grade Tumour, Low Grade Neoplasm, Diffuse High Grade Tumour, Diffuse High Grade Tumour, Diffuse High Grade Neoplasm, High Grade Tumour, High Grade Tumour, High Grade Neoplasm, Subependymal Tumour, Subependymal Tumour, Subependymal Neoplasm, Glioneuronal Tumour, Glioneuronal Tumour, Glioneuronal Neoplasm, Neuronal Tumour, Neuronal Tumour, Neuronal Neoplasm, Dysembryoplastic Neuroepithelial Tumour, Dysembryoplastic Neuroepithelial Tumour, Dysembryoplastic Neuroepithelial Neoplasm, Neuroepithelial Tumour, Neuroepithelial Tumour, Neuroepithelial Neoplasm, Ependymal Tumour, Ependymal Tumour, Ependymal Neoplasm, Choroid Tumour, Choroid Tumour, Choroid Neoplasm, Choroid Plexus Tumour, Choroid Plexus Tumour, Choroid Plexus Neoplasm, Choroid Plexus Neoplasms, Choroid Plexus Carcinoma, Teratoid Tumour, Teratoid Tumour, Rhabdoid Tumour, Rhabdoid Tumour, Mesenchymal Tumour, Mesenchymal Tumour, Mesenchymal Neoplasm, Non-Meningothelial Tumour, Non-Meningothelial Tumour, Non-Meningothelial Neoplasm, Fibroblastic Tumour, Fibroblastic Tumour, Fibroblastic Neoplasm, Myofibroblastic Tumour, Myofibroblastic Tumour, Myofibroblastic Neoplasm, Primary Intracranial Sarcoma, Chondrogenic Tumour, Chondrogenic Tumour, Chondrogenic Neoplasm, Notochordal Tumour, Notochordal Tumour, Notochordal Neoplasm, Intracranial Tumour, Intracranial Tumour, Intracranial Neoplasm, Infratentorial Neoplasms, Supratentorial Neoplasms, Hypothalamic Neoplasms, Glioma, Subependymal, Gliosarcoma, Cerebral Ventricle Neoplasm, Infratentorial Neoplasm, Supratentorial Neoplasm, Hypothalamic Neoplasm, Diffuse Intrinsic Pontine Glioma, Glioma, Glioma, Diffuse Low Grade Glioma, Low Grade Glioma, Diffuse High Grade Glioma, High Grade Glioma, Diffuse Midline Glioma, Midline Glioma, Diffuse Hemispheric Glioma, Hemispheric Glioma, Diffuse Paediatric High Grade Glioma, Infant Type Hemispheric Glioma, Circumscribed Astrocytic Glioma, Astrocytic Glioma, Choroid Glioma, Astrocytoma, Astrocytoma, Diffuse Astrocytoma, Pilocytic Astrocytoma, High Grade Astrocytoma, Pleomorphic Xanthoastrocytoma, Xanthoastrocytoma, Subependymal Giant Cell Astrocytoma, Giant Cell Astrocytoma, Desmoplastic Infantile Astrocytoma, Oligodendroglioma, Oligodendroglioma, Oligodendroblastoma, Oligodendroglioma, Glioblastoma, Glioblastoma, Neoplasms, Neuroepithelial, Polymorphous Low-Grade Neuroepithelial Tumour, Polymorphous Low-Grade Neuroepithelial Neoplasm, Neuroepithelial Tumour, Neuroepithelial Neoplasm, Ganglioglioma, Desmoplastic Infantile Ganglioglioma, Ganglioglioma, Angiocentric Glioma, Astroblastoma, Neurocytoma, Neurocytoma, Liponeurocytoma, Ependymoma, Subependymoma, Ependymoma, Ependymoma, Medulloblastoma, Medulloblastoma, Neuroblastoma, Neuroblastoma, Pineocytoma, Pinealoma, Pineoblastoma, Schwannoma, Neurofibroma, Perineurioma, Paraganglioma, Meningioma, Hemangioblastoma, Medullary Hemangioblastoma, Chondrosarcoma, Mesenchymal, Mesenchymal Chondrosarcoma, Craniopharyngioma, Craniopharyngioma, Pituitary Neoplasm, Pituitary Adenoma, Pituitary Blastoma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIRB approval was waived for this research. All four authors screened the articles retrieved. In stage 1, only titles and abstracts were screened. In stage 2, full-text articles were screened. An additional hand search was conducted, thoroughly examining references cited in the selected articles to identify additional literature relevant to the research question. A detailed description of the search, retrieval and selection is provided in Supplementary Material S1. QUADAS-2 risk of bias assessments were performed by all authors, with at least two raters evaluating each article [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Disagreements were resolved by consensus. Compliance with QIBA recommendations for DCE implementation and reporting was assessed for each selected study by an MRI physicist with four years of clinical neuroimaging experience (x.x.).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Search strategy\u003c/h2\u003e\u003cp\u003eThe search was carried out on the PubMed and Web of Science databases on August 31, 2025, using the building block method, with the search question being divided into three dimensions: 1. Technique (dynamic contrast-enhanced magnetic resonance imaging), 2. Population (paediatric) and 3. Pathology (brain tumour). A set of keywords was defined for each dimension.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Eligibility assessment\u003c/h2\u003e\u003cp\u003ePublications that included both adult and paediatric patients were needed to allow for the extraction of results for paediatric patients only. All publications were evaluated for cohort overlap. The diagnosis of a paediatric-type brain tumour was ideally based upon recent pathology classification systems and tissue. Still, tumours without histopathological verification of diagnosis were not excluded, as paediatric tumour patients sometimes do not undergo tissue-based tumour verification, e.g., due to a challenging tumour location. Only English-language publications were included. Case series of five or more patients were allowed due to the low incidence of some paediatric brain tumours.\u003c/p\u003e\u003cp\u003eThe exclusion criteria comprised studies that did not involve paediatric patients and articles that did not specifically address the use of DCE as a diagnostic tool. Publications not containing original data were also excluded, as were case reports with fewer than five cases. These criteria were used during search stages 1 and 2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data extraction and risk of bias assessment\u003c/h2\u003e\u003cp\u003eData extraction included study design, tumour and patient characteristics (number of patients, sex, age, tumour grade and type), and main findings measured in parameters including transfer constant from plasma into the extravascular extracellular space (K\u003csup\u003etrans\u003c/sup\u003e) and back (k\u003csub\u003eep\u003c/sub\u003e), extravascular extracellular space volume per unit tissue volume (v\u003csub\u003ee\u003c/sub\u003e), and blood plasma volume fraction (v\u003csub\u003ep\u003c/sub\u003e). Study results based on pharmacokinetic modelling techniques, including dual-compartment modelling and deconvolution analysis to derive blood volume and flow from DCE data to provide complementary insights into perfusion, were not included. A modified QUADAS-2 instrument was applied (Supplement Material 2) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Eligibility for meta-analysis was considered based on homogeneity of multiple aspects, including imaging protocol, tumour type, and treatment status. The availability of ≥ 5 studies with most QUADAS-2 categories scoring low or medium risk of bias was the liberal minimum for a meta-analysis. Otherwise, a narrative synthesis summarised the findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1. Overview\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the retrieval. The searches rendered 118 articles. After duplicate removal, 95 articles remained for stage 1 screening. 78 articles not meeting the criteria were excluded. Seventeen articles remained for stage 2. At the end of the screening process, eight articles matched the inclusion and exclusion criteria [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Handsearching resulted in one additional relevant article [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.2. Study characteristics and summary of the main study findings\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the study characteristics, including study design, study population, and main results related to the research question. Technical DCE sequence details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy characteristics and results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAuthor (year)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStudy Design\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eMain results\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArevalo-Perez et al. (2024)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 patients, 3 girls, 6–16 years old; 2 low-grade ependymoma, 4 anaplastic ependymoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative v\u003csub\u003ep, max\u003c/sub\u003e shows potential for differentiating between low-grade and high-grade ependymomas; however, the study population was too small to draw definitive conclusions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGupta et al. (2017)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 patients, 8 months-18 years old; 44 high-grade tumours (incl. glioblastomas, anaplastic astrocytomas, anaplastic ependymomas, medulloblastomas, choroid plexus carcinoma, atypical teratoid rhabdoid tumours, gangliogliomas) and 20 low-grade tumours (incl. astrocytomas, ganglioglioma, subependymal giant cell astrocytoma, dysembryoplastic neuroepithelial tumours, pilocytic astrocytomas)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative blood volume and v\u003csub\u003ep\u003c/sub\u003e were significantly different between high- and low-grade tumours. v\u003csub\u003ep\u003c/sub\u003e differentiated low-grade tumours from high-grade with sensitivity 0.75 and specificity 0.65 (cutoff 0.0135). k\u003csub\u003eep\u003c/sub\u003e demonstrated a significant difference between posterior fossa ependymomas and medulloblastomas, whereas v\u003csub\u003ee\u003c/sub\u003e, differentiated not only posterior fossa ependymomas from medulloblastomas but also pilocytic astrocytomas from medulloblastomas.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHo et al. (2025)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 patients, 44 boys, 1.7–211 months old; 36 high-grade tumours (incl. medulloblastomas, atypical teratoid/rhabdoid tumours, anaplastic ependymomas, diffuse midline gliomas, CNS embryonal tumours NOS, high-grade gliomas, anaplastic astrocytomas, ganglioblastoma) and 36 low-grade tumours (incl. pilocytic astrocytomas, dysembryoplastic neuroepithelial tumours, gangliogliomas, desmoplastic infantile ganglioglioma, diffuse astrocytoma, ependymoma, low-grade astrocytoma, low-grade glial neoplasm, low-grade neuroepithelial neoplasm, optic chiasm glioma, pilomyxoid astrocytoma, pleomorphic xanthoastrocytoma)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNone of the DCE parameters showed a significant difference between low- and high-grade gliomas after statistical correction.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJost et al. (2008)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 patients with optic pathway gliomas; 14 had OPGs associated with neurofibromatosis type 1, 13 had sporadic OPGs; 11 were classified as “clinically stable”, 16 as “clinically aggressive”\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e“Clinically aggressive” OPGs demonstrated significantly higher K\u003csup\u003ePS\u003c/sup\u003e compared with “clinically stable” OPGs. Among sporadic cases, tumours classified as “clinically aggressive” also exhibited significantly greater permeability values than their “clinically stable” counterparts.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRochetams et al. (2017)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 patients, 9 girls, 0.47–15.92 years old; 6 patients with high-grade tumours (incl. rhabdoid tumour, medulloblastomas, high-grade gliomas) and 4 low-grade tumours (pilocytic astrocytomas, DNET, low-grade glioma)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTypes of concentration-time curves are presented for brain tumours. There was a significant difference in K\u003csup\u003etrans\u003c/sup\u003e between grade IV and I tumours, but there was no difference in K\u003csub\u003eep\u003c/sub\u003e and v\u003csub\u003ee\u003c/sub\u003e. K\u003csup\u003etrans\u003c/sup\u003e and v\u003csub\u003ee\u003c/sub\u003e (but not K\u003csub\u003eep\u003c/sub\u003e) were significantly different in tumour when compared to non-pathological surrounding tissue.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVajapeyam et al. (2020)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 patients, 33 girls, 2.5–12.9 years, 3 excluded from the analysis; all 50 patients had DIPGs, 43 died, 45 experienced a PFS event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigher mean K\u003csup\u003etrans\u003c/sup\u003e and mean v\u003csub\u003ee\u003c/sub\u003e were associated with shorter OS and PFS. Maximum K\u003csup\u003etrans\u003c/sup\u003e was associated with PFS.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVajapeyam et al. (2018)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 patients, 23 boys, 0.3-16.76 years old; 31 infratentorial and 10 supratentorial tumors; 16 low-grade (7 pilocytic astrocytomas, 5 low-grade gliomas, 1 mature teratoma, 1 atypical meningioma, 1 low-grade ganglioglioma, and 1 low-grade mixed germ cell tumor), 25 high-grade tumors (12 medulloblastomas, 4 glioblastomas, 4 anaplastic ependymomas, and 1 each of atypical teratoid/rhabdoid tumor, embryonal tumor not otherwise specified, choroid plexus carcinoma, embryonal tumor with rhabdoid features, and diffuse midline glioma)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eK\u003csup\u003etrans\u003c/sup\u003e, k\u003csub\u003eep\u003c/sub\u003e, v\u003csub\u003ee\u003c/sub\u003e showed a significant difference between high- and low-grade tumours. ROC analysis has demonstrated good discriminatory performance for K\u003csup\u003etrans\u003c/sup\u003e (AROC = 0.883, CI 0.781–0.984), k\u003csub\u003eep\u003c/sub\u003e (AROC = 0.908, CI 0.815-1.0), and ve (AROC = 0.843, CI 0.713–0.972) in distinguishing between high- and low-grade tumours.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVajapeyam et al. (2017)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 patients, 24 boys, 0.3-18.14 years; 18 low-grade tumors (7 pilocytic astrocytomas, 3 low-grade gliomas with piloid features, 3 low-grade gliomas, 1 low-grade ependymoma, 1 atypical meningioma WHO II, 1 hemangioblastoma grade I, 1 ganglioglioma grade I–II, 1 low-grade histiocytic sarcoma) and 20 high-grade tumors (11 medulloblastomas, 3 glioblastoma multiformes, 2 anaplastic ependymomas, 1 high-grade sarcoma, 1 choroid plexus carcinoma, 1 germinomatous germ cell tumor, and 1 high-grade glioma).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eK\u003csup\u003etrans\u003c/sup\u003e, k\u003csub\u003eep\u003c/sub\u003e, v\u003csub\u003ee\u003c/sub\u003e showed a significant difference between populations of high- and low-grade tumours with high sensitivity (\u0026gt; 0.7) and specificity (\u0026gt; 0.82).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eZukotynski et al. (2013)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 patients, 13 girls; 7 supratentorial HGGs, 9 LGGs, 4 BSGs, 2 medulloblastomas, 2 ependymomas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo statistically significant difference in K\u003csub\u003eps, max\u003c/sub\u003e was observed between children with HGG/BSG and those with LGG. K\u003csub\u003eps, max\u003c/sub\u003e was not significantly correlated with PFS.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCaption: Abbreviations: AROC - area under the receiver operating curve, BSG - brainstem glioma, CNS - central nervous system, DIPG - diffuse intrinsic pontine glioma, DNET - dysembryoplastic neuroepithelial tumour, HGG - high-grade glioma, LGG - low-grade glioma, NOS - not otherwise specified, OPG - optic pathway glioma, OS - overall survival, PFS - progression-free survival, WHO - World Health Organization\u003c/em\u003e\u003c/p\u003e\u003cp\u003eVajapeyam et al. (2018) and Vajapeyam et al. (2017) were considered to contain cohort overlap. This included all the patients from the 2018 study, as well as 1 low-grade ependymoma, 1 hemangioblastoma grade I, 1 low-grade histiocytic sarcoma, and 1 high-grade sarcoma included in Vajapeyam et al. 2017.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTechnical details of DCE sequence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAuthor (year)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScanner and field strength\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eHead coil type\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eContrast agent type and dose\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSequence type and flip angle (FA)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNumber of phases/time\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eresolution\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCoverage / in-plane sampling (FOV /matrix size)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eT1/B1 map acquisition\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eProcessing\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArevalo-Perez et al. (2024)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed: 1.5T Optima GE, 3T Signa Premier GE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8-channel head coil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGatobutrol (Gadavist, Bayer) 0.1 mmol/kg, delivered with a power injector at 2–3 mL/s via an 18–21 gauge venous catheter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3D T1-weighted fast-spoiled (fast echo-spoiled) gradient-echo sequence; FA 25°; TR 4–5 ms; TE 1–2 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTemporal resolution 5–6 s; 10 pre-injection phases and 30 post-injection phases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 24 cm, matrix 128 x 128, slice thickness 3 mm, 10–12 axial images\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo T1/B1 mapping reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: NordicIce\u003c/p\u003e\u003cp\u003ePreprocessing: spatial and temporal smoothing\u003c/p\u003e\u003cp\u003eAIF: individually computed from MCA\u003c/p\u003e\u003cp\u003eModel: extended Tofts two-compartment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGupta et al. (2017)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3T Philips\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15-channel head coil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGd-BOPTA (Multihance, Bracco, Italy) 0.1 mmol/kg, injected via a power injector at 1.5–2 mL/s via 22–24 gauge cannulas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT1 fast gradient echo; FA 10°; TR/TE = 5.0/1.4 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTemporal resolution ~ 3.9 s, 32 dynamics (4 pre-injection)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 24 cm, 128 x 128, slice thickness 6 mm, 12 axial slices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePre-contrast T1 mapping reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: not specified\u003c/p\u003e\u003cp\u003ePreprocessing: not specified\u003c/p\u003e\u003cp\u003eAIF: automated extraction\u003c/p\u003e\u003cp\u003eModel: leaky tracer kinetic model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHo et al. (2025)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3T Magnetom Skyra Siemens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGd-BOPTA, two doses 0.1 mmol/kg each, injected via power injector at 5 mL/s when 18–20 G IV access was possible; 24 G used in smaller children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT1-weighted gradient echo; FA 10°; TR/TE = 1.54/3.91 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100 time points over 4.5 min, ~ 2.7 s per phase; unclear how many phases before/after injection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV not reported, matrix 154 x 192, slice thickness 5 mm, 20 axial slices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo T1/B1 mapping reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: IDL package “Qimage”\u003c/p\u003e\u003cp\u003ePreprocessing: FSL for motion correction and registration\u003c/p\u003e\u003cp\u003eAIF: Manual MCA ROI\u003c/p\u003e\u003cp\u003eModel: extended Tofts model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJost et al. (2008)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed: 1.5T Sonata Siemens and 3T Trio Siemens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContrast agent not specified, 0.1 mmol/kg, injection method not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT1-weighted 3D FLASH, FA not specified, TR/TE = 30/6 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDynamic series not stated, dynamic duration 6 min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV ~ 128x128 mm\u003csup\u003e2\u003c/sup\u003e, matrix 128 x 128, in-plane voxel 1 mm\u003csup\u003e2\u003c/sup\u003e, slice thickness 3, 16 axial slices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVFA T1 mapping (FAs 10°, 15°, 25°)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: customised MATLAB\u003c/p\u003e\u003cp\u003ePreprocessing: coregistration with Intelli-link\u003c/p\u003e\u003cp\u003eAIF: blood ROI selection not specified\u003c/p\u003e\u003cp\u003eModel: Patlak\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRochetams et al. (2017)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5T Magnetom Aero Siemens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20-channel head coil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGadoteric acid (Dotarem, Guerbet), 0.2 mL/kg, injected with power injector at 1mL/s via peripheral 22G IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3D T1-weighted gradient echo; FA 12°; TR/TE = 4.46/1.72 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56 time points, temporal resolution ~ 3.1 s, dynamic duration 2:53 min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 230 × 186 mm², matrix 166 x 256, slice thickness 3 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo T1/B1 mapping reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: from vendor (Syngo MR Tissue 4D, Siemens)\u003c/p\u003e\u003cp\u003ePreprocessing: not specified\u003c/p\u003e\u003cp\u003eAIF: automatic extraction\u003c/p\u003e\u003cp\u003eModel: extended Tofts two-compartment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVajapeyam et al. (2020)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3T (unspecified)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGadobutrol, two half-doses (0.05 mmol/kg each), injection not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3D T1-weighted fast gradient echo; FA 15°; TR 4 s, TE minimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50 timepoints, ~ 7 s/phase, contrast injection after 20 s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 24 cm, matrix not specified, slice thickness 5 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVFA T1 mapping (FAs 15°, 10°, 5°, 2°)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: DynaCAD (Invivo) with OmniLook\u003c/p\u003e\u003cp\u003ePreprocessing: not specified\u003c/p\u003e\u003cp\u003eAIF: not specified\u003c/p\u003e\u003cp\u003eModel: extended Tofts two-compartment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVajapeyam et al. (2018)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3T Siemens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGadobutrol, 0.1 mL/kg at injection rate 2mL/s, the rest not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3D T1-weighted fast gradient echo; FA 15°; T4 4 s, TE minimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50 timepoints, ~ 7 s/phase, contrast injection after 20 s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 24 cm, matrix not specified, slice thickness 5 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVFA T1 mapping (FAs 15°, 10°, 5°, 2°)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: VersaVue (Invivo) with OmniLook\u003c/p\u003e\u003cp\u003ePreprocessing: not specified\u003c/p\u003e\u003cp\u003eAIF: not specified\u003c/p\u003e\u003cp\u003eModel: extended Tofts two-compartment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVajapeyam et al. (2017)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3T Siemens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGadobutrol, 0.1 mL/kg at injection rate 2mL/s, the rest not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3D T1-weighted fast gradient echo; FA 15°; T4 4 s, TE minimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50 timepoints, ~ 7 s/phase, contrast injection after 20 s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 24 cm, matrix not specified, slice thickness 5 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVFA T1 mapping (FAs 15°, 10°, 5°, 2°)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: VersaVue (Invivo) with OmniLook\u003c/p\u003e\u003cp\u003ePreprocessing: not specified\u003c/p\u003e\u003cp\u003eAIF: not specified\u003c/p\u003e\u003cp\u003eModel: extended Tofts two-compartment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eZukotynski et al. (2013)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5T (not specified)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3D T1-weighted spoiled gradient echo; FA 30°; TR/TE minimum (not specified)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40 dynamics, temporal resolution not specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFOV 24 cm, matrix ≥ 128 x 128, 16 axial slices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo T1/B1 mapping reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSoftware: in-house IDL software\u003c/p\u003e\u003cp\u003ePreprocessing: not specified\u003c/p\u003e\u003cp\u003eAIF: not specified\u003c/p\u003e\u003cp\u003eModel: not specified\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCaption: Abbreviations: AIF - arterial input function; FA - flip angle; FOV - field of view; IV - intravenous; ROI - region of interest; T - Tesla; TR - repetition time; TE - echo time; VFA - variable flip angle\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eStudies ranged in cohort size from 6 to 72 patients. Only one study applied the most recent World Health Organization Classification of Tumours of the Central Nervous System (WHO) of 2021 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Three studies investigated histologically confirmed tumours only, while two included some tumours that were histologically confirmed, and four did not mention whether the tumours were histologically evaluated. Four studies investigated untreated tumours, while two dealt with therapy evaluation, and three contained treated and untreated lesions.\u003c/p\u003e\u003cp\u003eThe criteria for a meta-analysis were not met. Considerable heterogeneity was observed across cohorts in tumour types, treatment status, and evaluation systems used to diagnose and categorise pathologies. Subsequently, a narrative synthesis was conducted to summarise the key findings of the included studies.\u003c/p\u003e\u003ch2\u003e3.3. Risk of bias and QIBA compliance\u003c/h2\u003e\u003cp\u003eMost studies showed a low risk of bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with patient selection description scoring the lowest (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). QIBA compliance, based on the technical parameters presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, was limited to low (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with no study being fully compliant with the recommendations on implementation and reporting.\u003c/p\u003e\u003ch2\u003e3.4. Noninvasive tumour differentiation\u003c/h2\u003e\u003cp\u003eFour studies investigated DCE parameters, including K\u003csup\u003etrans\u003c/sup\u003e, k\u003csub\u003eep\u003c/sub\u003e, v\u003csub\u003ee\u003c/sub\u003e, v\u003csub\u003ep\u003c/sub\u003e in newly diagnosed, treatment-naive patients to differentiate between low and high-grade paediatric primary brain tumours [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The cumulative number of cases is 186.\u003c/p\u003e\u003cp\u003eOnly Ho et al. applied the most recent WHO CNS 5 classification in their analysis of a mixed primary brain tumour cohort examining the capability of DCE to differentiate low- from high-grade lesions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. They present the largest cohort of 36 high-grade (WHO 3 and 4) and 36 low-grade primary brain tumours. No extended Tofts model DCE parameter significantly differed between both groups after Bonferroni correction.\u003c/p\u003e\u003cp\u003eGupta et al., in their mixed primary pediatric brain tumour study of 64 patients, did not find any significant Bonferroni-corrected differences in extended Tofts model kinetic parameters across different tumour grades, corroborating results by Ho et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They did, however, report significantly different (p = 0.036) K\u003csup\u003etrans\u003c/sup\u003e values between different posterior fossa tumours, namely between pilocytic astrocytomas (0.48, 0.17–1.25) and medulloblastomas (0.01, 0.0-0.74). They also observed that ependymomas (8.25 (3.98–17.91) and pilocytic astrocytomas (8.68 (5–79.05) had higher k\u003csub\u003eep\u003c/sub\u003e than medulloblastomas (2.89 (0.0-10.77); P = 0.012). Moreover, Gupta et al. reported that medulloblastomas had significantly lower v\u003csub\u003ee\u003c/sub\u003e compared to pilocytic astrocytomas and ependymomas (P = 0.003 and 0.012, respectively).\u003c/p\u003e\u003cp\u003eVajapeyam et al. (2018) present a comparable mixed histology setup of 41 cases, aiming to differentiate between WHO I and II (LGG by the 2016 WHO CNS classification) and grade III/IV (n = 25) using extended Tofts model parameters [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They conclude that significant differences were observed between groups for K\u003csup\u003etrans\u003c/sup\u003e, k\u003csub\u003eep\u003c/sub\u003e (both higher in HGG), and v\u003csub\u003ee\u003c/sub\u003e (lower in HGG). Statistical error correction was not stated. They had previously published a part of their cohort (with a great overlap) and concluded that all three parameters had high specificity (range, 82–100%), while the best sensitivity was achieved for v\u003csub\u003ee\u003c/sub\u003e (combined sensitivity 76% vs. 71% for K\u003csup\u003etrans\u003c/sup\u003e and k\u003csub\u003eep\u003c/sub\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, two mixed-lesion studies did not identify any differentiation potential for DCE (cumulative cases, n = 136). In contrast, two studies based on two overlapping datasets, totalling approximately 50 cases, identified diagnostic potential.\u003c/p\u003e\u003cp\u003eIn a homogeneous cohort of 50 diffuse intrinsic pontine glioma patients (histological confirmation unclear) treated with the same study protocol (veliparib plus radiotherapy followed by temozolomide), Vajapeyam et al. later examined the power of extended Tofts model DCE parameters measured in treatment-naive tumours to predict survival [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A higher K\u003csup\u003etrans\u003c/sup\u003e at baseline was associated with shorter overall and progression-free survival, although this did not reach statistical significance thresholds.\u003c/p\u003e\u003ch2\u003e3.5. Studies with unknown treatment status at the time of scanning\u003c/h2\u003e\u003cp\u003eThree studies did not specify patient treatment status and included both retrospective and prospective cohorts [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRochetams et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] present 10 pediatric patients with mixed gliomas (4 LGG WHO I/II, 6 HGG) of unknown therapy status, with K\u003csup\u003etrans\u003c/sup\u003e ratios being significantly different between grade I and grade IV brain tumours (\u003cem\u003eP\u003c/em\u003e = 0.027), while other differentiations were not possible. Grade IV paediatric brain tumours were distinctly defined by a K\u003csup\u003etrans\u003c/sup\u003e ratio above 0.63 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. K\u003csup\u003etrans\u003c/sup\u003e-based differentiation between pilocytic astrocytoma and medulloblastoma was impossible.\u003c/p\u003e\u003cp\u003eJost et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] present a study of optic pathway LGG patients with unknown therapy status, where they attempted to predict whether a patient would remain clinically stable (n = 11) or develop aggressive disease (n = 16). However, the relation between MRI and clinical judgment is unclear [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nonetheless, they found significantly higher (\u003cem\u003eP\u003c/em\u003e = 0.05) mean K\u003csup\u003etrans\u003c/sup\u003e surface area product (KPS) in clinically aggressive optic pathway gliomas (2.24 ml/min per 100 cm³) compared to clinically stable tumours (1.38 ml/min per 100 cm³). All clinically stable tumours had a value of \u0026lt; 2.0 ml/min per 100 cm³. To further support the observed correlation between vascular permeability and aggressiveness, a significantly higher mean permeability value (2.77 ml/min per 100 cm\u003csup\u003e3\u003c/sup\u003e) was observed in sporadic tumours classified as clinically aggressive compared to those classified as clinically stable (\u0026lt; 2.0 ml/min per 100 cm\u003csup\u003e3\u003c/sup\u003e) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\u003cp\u003eArevalo-Perez et al. present a 2024 study to differentiate low-grade cerebral ependymoma from anaplastic ones [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, only four cases were anaplastic, and two were low-grade. The v\u003csub\u003epmax\u003c/sub\u003e was, however, following trends in the adults with higher values in anaplastic lesions (mean 17.44%) than in low-grade lesions (mean 9.65%). Study quality is impaired by unknown treatment status.\u003c/p\u003e\u003ch2\u003e3.6. Treatment Evaluation Studies\u003c/h2\u003e\u003cp\u003eTwo studies evaluated tumour parameters during treatment using the above-mentioned DCE parameters to monitor changes in vascular permeability and perfusion characteristics over time [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eZukotynski et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] examined 24 children with mixed primary brain tumour histologies under bevacizumab and irinotecan treatment before, during, and after treatment, with variable data completeness, and only analysed K\u003csup\u003etrans\u003c/sup\u003e (referred to as Kps in the paper). K\u003csup\u003etrans\u003c/sup\u003e did not differ between LGG and HGG at baseline (n = 21; P = 0.56). K\u003csup\u003etrans\u003c/sup\u003e was not associated with progression-free survival, but with one exception, lowered during therapy.\u003c/p\u003e\u003cp\u003eThe 2020 Vajapeyam et al. publication [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] also contained follow-up scans showing that when analysed as continuous\u003c/p\u003e\u003cp\u003etime-dependent variables, associations of mean v\u003csub\u003ee\u003c/sub\u003e with progression-free survival (P = 0.03) and overall survival (P = 0.03), as well as maximum K\u003csup\u003etrans\u003c/sup\u003e with progression-free survival (P = 0.03) were near significant. Greater kinetic parameter increases with time were associated with worse outcomes. Kinetic parameters showed no difference between pseudoprogression and true early progression groups.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Critical summary\u003c/h2\u003e\u003cp\u003eThis systematic review on DCE in paediatric brain tumour patients illustrates that only a very few studies have focused on the topic, which is further complicated by substantial heterogeneity in cases and technical execution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Clinical findings\u003c/h2\u003e\u003cp\u003eA total of nine smaller-scale studies, partially with cohort overlap, unclear or outdated tumour histology, and differing research questions, as well as equivocal scanning parameters, underscore the substantial knowledge gap for DCE applications in paediatric neuro-oncological imaging.\u003c/p\u003e\u003cp\u003eRegarding the differentiation of low-grade and high-grade brain tumours, data relies on four to five studies, with a majority of samples indicating that differentiation is not possible using DCE kinetic parameters. However, the subgroup analyses of these studies suggest that this may be due to the histological heterogeneity of these cohorts, and that a differentiation between two types of tumours may be possible, as indicated by the results of Gupta et al. for pilocytic astrocytoma versus medulloblastoma [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Rochetams et al., however, could not find a difference for these two tumour entities, which may be due to their very small sample size of five cases for both groups in total [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The equivocal results of the published tumour malignancy prediction studies underscore another issue: Is it clinically useful to attempt a broad, mixed brain tumour differentiation across all types of entities? Should future studies not focus on differentiating two entities (like medulloblastoma and pilocytic astrocytoma), as this is difficult enough? Furthermore, we could not identify a single study that focuses on the molecular differentiation of tumour entities, which, however, is key to modern tumour diagnostics. Approaches, therefore, need to change substantially in the future.\u003c/p\u003e\u003cp\u003eExamining the published nine studies, it is notable that only a minority focused on histologically and clinically comparable cases, such as the optic pathway glioma study by Jost et al., which should be the standard when the sample size is relatively small. In this context, clinical reproducibility is an important factor in advanced MR imaging. While not fitting the inclusion criteria of this publication, the authors want to draw the attention to the publication by Miyazaki et al., who showed in eight pediatric glioma cases that only K\u003csup\u003etrans\u003c/sup\u003e had a variation coefficient below 20% and that the arterial input function (AIF) may show sharper and earlier first pass peaks than in adults having an impact on dynamic scan planning [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. On the other hand, Carceller et al.\u0026rsquo;s cohort of five HGG patients showed the lowest variation coefficient for v\u003csub\u003ee\u003c/sub\u003e (2.9%), while also K\u003csup\u003etrans\u003c/sup\u003e and k\u003csub\u003eep\u003c/sub\u003e remained below 20% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This means that future studies of DCE use in paediatric brain tumour patients must, on the one hand, critically evaluate which parameters to take into account, and, on the other, that AIF selection in the smaller vessels of paediatric brains remains an unsolved but relevant issue. Both are pivotal aspects, particularly in the currently underexplored field of follow-up studies in the paediatric cohort, which leads to the topic of technical implementation in paediatric MRI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Technical implementation of DCE in pediatric populations\u003c/h2\u003e\u003cp\u003eThe reviewed studies illustrate a high heterogeneity in DCE implementation across clinical research settings. Notably, there was a substantial underreporting of key acquisition parameters, particularly with respect to head coil specifications, contrast agent injection parameters, temporal sampling, and post-processing. Scanner field strengths ranged from 1.5 T to 3 T with variable head coils (8\u0026ndash;20 when reported). Contrast administration protocols differed in both type and dosing \u0026ndash; most commonly gadobutrol or Gd-BOPTA at 0.1 mmol/kg - although half-dose and repeat-dose strategies were also reported in combination with dynamic susceptibility contrast perfusion MRI, and injection rates varied from 1 to 5 mL/s. Only a subset of studies incorporated pre-contrast T1 mapping, and B1 mapping was rarely performed. AIF selection varied substantially, from manual middle cerebral artery regions of interest selection to automated extractions, followed by parameter quantification with the extended Tofts two-compartment, Patlak, and leaky tracer kinetic models.\u003c/p\u003e\u003cp\u003eDespite the persistent application of DCE in pediatric neuro-oncology, there are currently no guidelines that specifically address its implementation in children. Existing recommendations, such as QIBA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], have been developed for adult populations only, and they do not incorporate practical pediatric considerations such as smaller intravenous catheter gauge size [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the risk of extravasation at higher injection pressures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], or the need for adjusted dosing strategies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In practice, pediatric patients frequently require the use of 18\u0026ndash;21 G catheters, which restrict achievable injection rates. Consequently, lower injection rates are often employed to reduce the risk of extravasation, but slower infusion flattens the AIF peak and compromises the fidelity of tracer kinetic modelling [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Reduced AIF sharpness directly limits the accuracy of quantitative parameters such as K\u003csup\u003etrans\u003c/sup\u003e, which are sensitive to bolus profile [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This gap between existing technical standards and the realities of pediatric practice highlights the need for tailored guidelines that strike a balance between patient safety and the demands of robust pharmacokinetic analysis.\u003c/p\u003e\u003cp\u003eA consistent theme across published studies is the systematic failure to both implement and report acquisitions and analyses in line with QIBA recommendations. Key acquisition parameters, including head coil type, contrast injection details, time resolution, and coverage, were frequently underreported, while essential calibration measures such as T1 and B1 mapping were often omitted. Similarly, descriptions of preprocessing pipelines, AIF definition, and model selection were highly variable or often insufficiently documented, making them difficult to reproduce. The lack of adherence across studies further limits clinical translation and the establishment of robust quantitative biomarkers for pediatric neuro-oncology. Community efforts, such as the Open-Source Initiative for Perfusion Imaging (OSIPI), provide repositories of openly shared software tools and reference data processing pipelines to promote reproducible quantitative DCE imaging [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Greater alignment with QIBA recommendations and broader adoption of reproducible workflows will be essential for advancing multicenter validation of DCE in children.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Limitations of current studies\u003c/h2\u003e\u003cp\u003eWhile DCE demonstrates potentially useful correlations between pediatric brain tumour grade and parameters, including K\u003csup\u003etrans\u003c/sup\u003e, k\u003csub\u003eep\u003c/sub\u003e, v\u003csub\u003ee\u003c/sub\u003e, and v\u003csub\u003ep\u003c/sub\u003e, there are several limitations in the present review that should be considered when interpreting the results.\u003c/p\u003e\u003cp\u003eOne important discrepancy in this review is that several included studies varied in patient cohort characteristics, statistical methodologies, and imaging protocols, which limits direct comparability and reduces the generalisability of the findings across clinical contexts.\u003c/p\u003e\u003cp\u003eAnother limitation concerns the substantial variability and discrepancy in tumour type evaluation and grade classification systems. Only four of the nine studies reported compliance with any WHO CNS classification, and of these, only two specified the edition of the WHO CNS classification used. Additionally, several studies did not include histological verification. This may impact the accuracy of the overall conclusions drawn by this review regarding the correlation of DCE parameters with tumour grade and type. For some tumour discriminations, e.g, regarding medulloblastoma vs. pilocytic astrocytoma, the impact of the transition from WHO CNS 2016 to 2021 may be small, and comparisons between such studies can still be made [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], while paediatric glioma classifications underwent substantial adaptations in the 2021 WHO classification [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother crucial limitation identified by this review is the small sample sizes of the studies. This limits the statistical power of the findings and makes it challenging to draw firm conclusions regarding the prognostic value of DCE parameters.\u003c/p\u003e\u003cp\u003eFurthermore, measurement discrepancies may have been introduced by the inconsistent methodologies across studies. Variations in acquisition parameters, including temporal resolution, field strength, contrast agent types, and processing pipelines, are a significant limitation to comparability. Variation in arterial input function (AIF) selection is another relevant issue that may have affected clinical outcomes of the presented studies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe inability to directly compare among studies is exacerbated by the use of different processing models (e.g., Tofts, Patlak, or Kermode models), as each model highlights different aspects of tumour physiology and may not accurately reflect all tumour types. This may pose challenges in producing consistently reliable and accurate results across tumour subtypes.\u003c/p\u003e\u003cp\u003eLastly, while DCE shows promise in providing insights into tumour vascularity, it possesses some inherent technical limitations. For example, DCE is highly sensitive to motion artefacts, which are particularly evident in paediatric patients who may have trouble staying still during their procedure. Additionally, the use of contrast agents carries a small risk of side effects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], although these are generally rare.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Future Directions for DCE in Paediatric Neuro-Oncology\u003c/h2\u003e\u003cp\u003eFuture studies implementing DCE in paediatric neuro-oncology should aim to align more closely with the QIBA recommendations while adapting protocols to the practical constraints of paediatric cohorts. This includes the use of standardised acquisition schemes, a rigorous documentation of contrast injection parameters, and transparent reporting of data processing. Importantly, recommendations specifically tailored to the paediatric population are still lacking, and developing such consensus guidelines will be crucial to strike a balance between technical rigour and the clinical demands of imaging children. In addition, since paediatric tumours are rare and published sample sizes are limited, the creation of inter-institutional databases that compile DCE acquisition and pharmacokinetic parameters would be invaluable, enabling more robust analyses and facilitating clinically translatable biomarkers.\u003c/p\u003e\u003cp\u003eBeyond technical harmonisation, future DCE studies should address biologically and clinically more meaningful questions, moving away from broad comparisons among heterogeneous tumour entities and instead focusing on molecular markers, limited entities, and clinically relevant outcomes to strengthen their translational value. This is particularly true for post-treatment studies.\u003c/p\u003e\u003cp\u003eThese steps would foster greater reproducibility, comparability, and clinical impact of DCE in the pediatric neuro-oncology domain, particularly for the benefit of young, highly vulnerable patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Conclusion\u003c/h2\u003e\u003cp\u003eDCE MRI may deliver valuable non-invasive imaging biomarkers in paediatric patients with brain tumours, as indicated by several studies. However, the low published sample sizes and technical and case heterogeneity do not currently allow for firm conclusions. Dedicated paediatric DCE MRI protocols, as well as clinically focused studies, are needed, indicating that patients currently do not benefit from clinically used DCE due to still too low levels of evidence, which hampers reliable kinetic parameter interpretation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earterial input function\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edynamic contrast-enhanced\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ek\u003csub\u003eep\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erate constant from extravascular extracellular space back into blood plasma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eK\u003csup\u003etrans\u003c/sup\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etransfer constant from blood plasma into extravascular extracellular space\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epermeability \u0026times; surface area product per unit mass of tissue\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ev\u003csub\u003ee\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eextravascular extracellular volume fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ev\u003csub\u003ep\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eblood-plasma volume fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors reviewed the articles used for the review.All authors participated in the risk of bias analysis.All authors participated in the writing and review process.All authors worked on image material. V.C.K. was the primary supervisor to this work.Y.P. was the technical and statistical supervisor for this work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge Jakub Ot\u0026aacute;hal, Martin Kynčl, and David Kala for their contributions to project management, patient recruitment, and guidance on contrast data acquisition at University Hospital Motol.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eIRB approval was waived for this research. All four authors screened the articles retrieved. In stage 1, only titles and abstracts were screened. In stage 2, full-text articles were screened. An additional hand search was conducted, thoroughly examining references cited in the selected articles to identify additional literature relevant to the research question. A detailed description of the search, retrieval and selection is provided in Supplementary Material S1. QUADAS-2 risk of bias assessments were performed by all authors, with at least two raters evaluating each article [[9]](https:/paperpile.com/c/ZCQTw0/rezc) . Disagreements were resolved by consensus. Compliance with QIBA recommendations for DCE implementation and reporting was assessed for each selected study by an MRI physicist with four years of clinical neuroimaging experience (x.x.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHossain MJ, Xiao W, Tayeb M, et al. Epidemiology and prognostic factors of pediatric brain tumor survival in the US: Evidence from four decades of population data. Cancer Epidemiol 2021; 72: 101942\u003c/li\u003e\n\u003cli\u003eOyefiade A, Paltin I, De Luca CR, et al. Cognitive risk in survivors of pediatric brain tumors. J Clin Oncol 2021; 39: 1718\u0026ndash;1726\u003c/li\u003e\n\u003cli\u003eGoo HW, Ra Y-S. Advanced MRI for pediatric brain tumors with emphasis on clinical benefits. Korean J Radiol 2017; 18: 194\u0026ndash;207\u003c/li\u003e\n\u003cli\u003eHirschler L, Sollmann N, Schmitz-Abecassis B, et al. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; doi:10.1002/jmri.28662\u003c/li\u003e\n\u003cli\u003eTofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999; 10: 223\u0026ndash;232\u003c/li\u003e\n\u003cli\u003eKeil VC, Gielen GH, Pintea B, et al. DCE-MRI in Glioma, Infiltration Zone and Healthy Brain to Assess Angiogenesis: A Biopsy Study. Clin Neuroradiol 2021; 31: 1049\u0026ndash;1058\u003c/li\u003e\n\u003cli\u003eShukla-Dave A, Obuchowski NA, Chenevert TL, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019; 49: i \u0026ndash; i\u003c/li\u003e\n\u003cli\u003eOuyang M, Bao L. Gadolinium contrast agent deposition in children. J Magn Reson Imaging 2025; 61: 70\u0026ndash;82\u003c/li\u003e\n\u003cli\u003eWhiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155: 529\u0026ndash;536\u003c/li\u003e\n\u003cli\u003eVajapeyam S, Stamoulis C, Ricci K, et al. Automated processing of dynamic contrast-enhanced MRI: Correlation of advanced pharmacokinetic metrics with tumor grade in pediatric brain tumors. AJNR Am J Neuroradiol 2017; 38: 170\u0026ndash;175\u003c/li\u003e\n\u003cli\u003eVajapeyam S, Brown D, Johnston PR, et al. Multiparametric analysis of permeability and ADC histogram metrics for classification of pediatric brain tumors by tumor grade. AJNR Am J Neuroradiol 2018; 39: 552\u0026ndash;557\u003c/li\u003e\n\u003cli\u003eVajapeyam S, Brown D, Billups C, et al. Advanced ADC histogram, perfusion, and permeability metrics show an association with survival and pseudoprogression in newly diagnosed diffuse intrinsic pontine glioma: A report from the Pediatric Brain Tumor Consortium. AJNR Am J Neuroradiol 2020; 41: 718\u0026ndash;724\u003c/li\u003e\n\u003cli\u003eRochetams B-B, Marechal B, Cottier J-P, et al. T1-weighted dynamic contrast-enhanced brain magnetic resonance imaging: A preliminary study with low infusion rate in pediatric patients. Neuroradiol J 2017; 30: 429\u0026ndash;436\u003c/li\u003e\n\u003cli\u003eJost SC, Ackerman JW, Garbow JR, et al. Diffusion-weighted and dynamic contrast-enhanced imaging as markers of clinical behavior in children with optic pathway glioma. Pediatr Radiol 2008; 38: 1293\u0026ndash;1299\u003c/li\u003e\n\u003cli\u003eGupta PK, Saini J, Sahoo P, et al. Role of dynamic contrast-enhanced perfusion magnetic resonance imaging in grading of pediatric brain tumors on 3T. Pediatr Neurosurg 2017; 52: 298\u0026ndash;305\u003c/li\u003e\n\u003cli\u003eHo CY, Supakul N, Anthony G, et al. Perfusion showdown: Comparison of multiple MRI perfusion techniques in the grading of pediatric brain tumors. AJNR Am J Neuroradiol 2025; 46: 1464\u0026ndash;1470\u003c/li\u003e\n\u003cli\u003eArevalo-Perez J, Yllera-Contreras E, Peck KK, et al. Differentiating low-grade from high-grade intracranial ependymomas: Comparison of dynamic contrast-enhanced MRI and diffusion-weighted imaging. AJNR Am J Neuroradiol 2024; 45: 927\u0026ndash;933\u003c/li\u003e\n\u003cli\u003eZukotynski KA, Fahey FH, Vajapeyam S, et al. Exploratory evaluation of MR permeability with \u003csup\u003e18\u003c/sup\u003eF-FDG PET mapping in pediatric brain tumors: A report from the pediatric brain tumor consortium. J Nucl Med 2013; 54: 1237\u0026ndash;1243\u003c/li\u003e\n\u003cli\u003eLouis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 2021; 23: 1231\u0026ndash;1251\u003c/li\u003e\n\u003cli\u003eMiyazaki K, Jerome NP, Collins DJ, et al. Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study. Eur Radiol 2015; 25: 2641\u0026ndash;2650\u003c/li\u003e\n\u003cli\u003eCarceller F, Jerome NP, Miyazaki K, et al. Feasibility and applicability of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in routine assessments of children with high-grade gliomas. Pediatr Blood Cancer 2017; 64: 279\u0026ndash;283\u003c/li\u003e\n\u003cli\u003eBennett J, Cheung M. Intravenous access in children. Paediatr Child Health (Oxford) 2020; 30: 224\u0026ndash;229\u003c/li\u003e\n\u003cli\u003eAmaral JG, Traubici J, BenDavid G, et al. Safety of power injector use in children as measured by incidence of extravasation. AJR Am J Roentgenol 2006; 187: 580\u0026ndash;583\u003c/li\u003e\n\u003cli\u003eYu OJ, Kim PH, Yoon HM, et al. Safety of gadolinium-based contrast agents in children: A systematic review and meta-analysis. Radiology 2025; 316: e241224\u003c/li\u003e\n\u003cli\u003eWang Y, Huang W, Panicek DM, et al. Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data. Magn Reson Med 2008; 59: 1183\u0026ndash;1189\u003c/li\u003e\n\u003cli\u003eKlawer EME, van Houdt PJ, Pos FJ, et al. Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer. NMR Biomed 2018; 31: e3946\u003c/li\u003e\n\u003cli\u003evan Houdt PJ, Ragunathan S, Berks M, et al. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91: 1774\u0026ndash;1786\u003c/li\u003e\n\u003cli\u003eCotter JA, Hawkins C. Medulloblastoma: WHO 2021 and beyond. Pediatr Dev Pathol 2022; 25: 23\u0026ndash;33\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves FG, Viaene AN, Vossough A. Advanced magnetic resonance imaging in pediatric glioblastomas. Front Neurol 2021; 12: 733323\u003c/li\u003e\n\u003cli\u003eKeil VC, M\u0026auml;dler B, Gieseke J, et al. Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI. Magn Reson Imaging 2017; 40: 83\u0026ndash;90\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prad","sideBox":"Learn more about [Pediatric Radiology](http://link.springer.com/journal/247)","snPcode":"247","submissionUrl":"https://submission.nature.com/new-submission/247/3","title":"Pediatric Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"brain tumour, glioma, paediatric, dynamic contrast enhanced, magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-7543940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7543940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an advanced imaging technique utilising dynamic contrast uptake to quantify blood-brain barrier permeability.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThe clinical utility of DCE in paediatric brain tumours is unclear. This systematic review evaluates the efficacy of DCE in differentiating paediatric brain tumours and identifying progression. It also gathers information on the technical implementation of DCE in paediatric MRI, improving the standard of care.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e\u003cp\u003eA string-based literature search was performed in PubMed and Web of Science. Original articles evaluating the utility of DCE were included. A modified QUADAS-2 instrument evaluated the risk of bias.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eNine studies (2008\u0026ndash;2025) were eligible (sample size 6\u0026ndash;72 cases). Six studies investigated low-grade versus high-grade differentiation in mixed pediatric tumours (cumulative sample n\u0026thinsp;=\u0026thinsp;196) with successful discrimination through K\u003csup\u003etra ns\u003c/sup\u003e and/or k\u003csub\u003eep\u003c/sub\u003e in three studies (60 patients). Discrimination of two distinct histologies was usually more successful. Two studies evaluated the response to different treatments. Results for survival prediction based on DCE parameters were not promising. One study attempted to predict tumour aggressiveness in optic pathway glioma with good prognostic capacity for K\u003csup\u003etrans\u003c/sup\u003e. DCE technical execution varied substantially among studies and was usually not compliant with current guidelines. Meta-analyses were impossible.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDCE may be of added value to discriminate between two different paediatric brain tumour entities, but a general discrimination potential between low- and high-grade lesions is doubtful. More studies and greater technical homogeneity are needed to investigate the technique\u0026rsquo;s prognostic potential for paediatric cohorts.\u003c/p\u003e","manuscriptTitle":"Dynamic contrast-enhanced Magnetic Resonance Imaging in Paediatric Brain Tumours Systematically Reviewed","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 03:03:41","doi":"10.21203/rs.3.rs-7543940/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-10T16:57:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T05:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250805051803656270924589078808083753359","date":"2025-09-18T13:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T06:17:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T00:19:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T00:19:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Radiology","date":"2025-09-05T11:44:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prad","sideBox":"Learn more about [Pediatric Radiology](http://link.springer.com/journal/247)","snPcode":"247","submissionUrl":"https://submission.nature.com/new-submission/247/3","title":"Pediatric Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"79878985-192a-49d8-ac6d-2218f505aae6","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:06:23+00:00","versionOfRecord":{"articleIdentity":"rs-7543940","link":"https://doi.org/10.1007/s00247-026-06600-7","journal":{"identity":"pediatric-radiology","isVorOnly":false,"title":"Pediatric Radiology"},"publishedOn":"2026-04-01 15:58:39","publishedOnDateReadable":"April 1st, 2026"},"versionCreatedAt":"2025-09-23 03:03:41","video":"","vorDoi":"10.1007/s00247-026-06600-7","vorDoiUrl":"https://doi.org/10.1007/s00247-026-06600-7","workflowStages":[]},"version":"v1","identity":"rs-7543940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7543940","identity":"rs-7543940","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}