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Response criteria for paediatric high-grade glioma vary historically and across different cooperative groups. The Response Assessment in Neuro-Oncology working group developed response criteria for adult high-grade glioma, but these were not created to meet the unique challenges in children with the disease. The Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. We established a subcommittee to develop response assessment criteria for paediatric high-grade glioma. Current practice and literature were reviewed to identify major challenges in assessing the response of paediatric high-grade gliomas to various treatments. For areas in which scientific investigation was scarce, consensus was reached through an iterative process. RAPNO response assessment recommendations include the use of MRI of the brain and the spine, assessment of clinical status, and the use of corticosteroids or antiangiogenics. Imaging standards for brain and spine are defined. Compared with the recommendations for the management of adult high-grade glioma, for paediatrics there is inclusion of diffusion-weighted imaging and a higher reliance on T2-weighted fluid-attenuated inversion recovery. Consensus recommendations and response definitions have been established and, similar to other RAPNO recommendations, prospective validation in clinical trials is warranted.

PurposeHigh-grade astrocytoma with piloid features (HGAP) is a recently described brain tumor entity defined by a specific DNA methylation profile. HGAP has been proposed to be integrated in the upcoming World Health Organization classification of central nervous system tumors expected in 2021. In this series, we present the first single-center experience with this new entity.MethodsDuring 2017 and 2020, six HGAP were identified. Clinical course, surgical procedure, histopathology, genome-wide DNA methylation analysis, imaging, and adjuvant therapy were collected.ResultsTumors were localized in the brain stem (n = 1), cerebellar peduncle (n = 1), diencephalon (n = 1), mesencephalon (n = 1), cerebrum (n = 1) and the thoracic spinal cord (n = 2). The lesions typically presented as T1w hypo- to isointense and T2w hyperintense with inhomogeneous contrast enhancement on MRI. All patients underwent initial surgical intervention. Three patients received adjuvant radiochemotherapy, and one patient adjuvant radiotherapy alone. Four patients died of disease, with an overall survival of 1.8, 9.1, 14.8 and 18.1 months. One patient was alive at the time of last follow-up, 14.6 months after surgery, and one patient was lost to follow-up. Apart from one tumor, the lesions did not present with high grade histology, however patients showed poor clinical outcomes.ConclusionsHere, we provide detailed clinical, neuroradiological, histological, and molecular pathological information which might aid in clinical decision making until larger case series are published. With the exception of one case, the tumors did not present with high-grade histology but patients still showed short intervals between diagnosis and tumor progression or death even after extensive multimodal therapy.

High-grade pediatric central nervous system glial tumors are comprised primarily of anaplastic astrocytomas (AA, WHO grade III) and glioblastomas (GBM, WHO grade IV). High-grade gliomas are most commonly diagnosed in the primary setting in children, but as in adults, they can also arise as a result of transformation of a low-grade malignancy, though with limited frequency in the pediatric population. The molecular genetics of high-grade gliomas in the pediatric population are distinct from their adult counterparts. In contrast to the adult population, high-grade gliomas in children are relatively infrequent, representing less than 20% of cases.

T cells expressing anti-CD19 chimeric antigen receptors (CARs) demonstrate impressive efficacy in the treatment of systemic B cell malignancies, including B cell lymphoma. However, their effect on primary central nervous system lymphoma (PCNSL) is unknown. Additionally, the detailed cellular dynamics of CAR T cells during their antitumor reaction remain unclear, including their intratumoral infiltration depth, mobility, and persistence. Studying these processes in detail requires repeated intravital imaging of precisely defined tumor regions during weeks of tumor growth and regression. Here, we have combined a model of PCNSL with in vivo intracerebral 2-photon microscopy. Thereby, we were able to visualize intracranial PCNSL growth and therapeutic effects of CAR T cells longitudinally in the same animal over several weeks. Intravenous (i.v.) injection resulted in poor tumor infiltration of anti-CD19 CAR T cells and could not sufficiently control tumor growth. After intracerebral injection, however, anti-CD19 CAR T cells invaded deeply into the solid tumor, reduced tumor growth, and induced regression of PCNSL, which was associated with long-term survival. Intracerebral anti-CD19 CAR T cells entered the circulation and infiltrated distant, nondraining lymph nodes more efficiently than mock CAR T cells. After complete regression of tumors, anti-CD19 CAR T cells remained detectable intracranially and intravascularly for up to 159 d. Collectively, these results demonstrate the great potential of anti-CD19 CAR T cells for the treatment of PCNSL.

ObjectivesTo evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from non-necrotic atypical glioblastoma (GBM).MethodsSeventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes. These features were subjected to recursive feature elimination and random forest (RF) analysis with nested cross-validation. The diagnostic abilities of a radiomics machine-learning classifier, apparent diffusion coefficient (ADC), and three readers, who independently classified the tumours based on conventional MR sequences, were evaluated using receiver operating characteristic (ROC) analysis. Areas under the ROC curves (AUC) of the radiomics classifier, ADC value, and the radiologists were compared.ResultsThe mean AUC of the radiomics classifier was 0.921 (95 % CI 0.825–0.990). The AUCs of the three readers and ADC were 0.707 (95 % CI 0.622–0.793), 0.759 (95 %CI 0.656–0.861), 0.695 (95 % CI 0.590–0.800) and 0.684 (95 % CI0.560–0.809), respectively. The AUC of the radiomics-based classifier was significantly higher than those of the three readers and ADC (p< 0.001 for all).ConclusionsLarge-scale radiomics with a machine-learning algorithm can be useful for differentiating PCNSL from atypical GBM, and yields a better diagnostic performance than human radiologists and ADC values.Key Points• Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM.• This approach yields a higher diagnostic accuracy than visual analysis by radiologists.• Radiomics can strengthen radiologists’ diagnostic decisions whenever conventional MRI sequences are available.

To investigate safety and efficacy of high-dose chemotherapy followed by autologous stem cell transplantation (HCT-ASCT) in relapsed/refractory (r/r) primary central nervous system lymphoma (PCNSL), we conducted a single-arm multicentre study for immunocompetent patients (<66 years) with PCNSL failing high-dose methotrexate)-based chemotherapy. Induction consisted of two courses of rituximab (375 mg/m 2 ), high-dose cytarabine (2 × 3 g/m 2 ) and thiotepa (40 mg/m 2 ) with collection of stem cells in between. Conditioning for HCT-ASCT consisted of rituximab 375 mg/m 2 , carmustine 400 mg/m 2 and thiotepa (4 × 5 mg/kg). Patients commenced HCT-ASCT irrespective of response after induction. Patients not achieving complete remission (CR) after HCT-ASCT received whole-brain radiotherapy. Primary end point was CR after HCT-ASCT. We enrolled 39 patients; median age and Karnofsky performance score are 57 years and 90%, respectively. About 28 patients had relapsed and 8 refractory disease. About 22 patients responded to induction and 32 patients commenced HCT-ASCT. About 22 patients (56.4%) achieved CR after HCT-ASCT. Respective 2-year progression-free survival (PFS) and overall survival (OS) rates were 46.0% (median PFS 12.4 months) and 56.4%; median OS not reached. We recorded four treatment-related deaths. Thiotepa-based HCT-ASCT is an effective treatment option in eligible patients with r/r PCNSL. Comparative studies are needed to further scrutinise the role of HCT-ASCT in the salvage setting.

Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/−10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.

Backgroud Establishing diagnostic and prognostic biomarkers of primary central nervous system lymphoma (PCNSL) is a challenge. This study evaluated the value of dynamic interleukin (IL)-10 cerebrospinal fluid (CSF) concentrations for prognosis and relapse prediction in PCNSL. Methods Consecutive 40 patients newly diagnosed with PCNSL between April 2015 and April 2019 were recruited, and serial CSF specimens were collected by lumbar punctures (LP) or by Ommaya reservoir at diagnosis, treatment, and follow-up phase. Results We confirmed that an elevated IL-10 cutoff value of 8.2 pg/mL for the diagnosis value of PCNSL showed a sensitivity of 85%. A persistent detectable CSF IL-10 level at the end of treatment was associated with poor progression-free survival (PFS) (836 vs. 481 days, p = 0.049). Within a median follow-up of 13.6 (2–55) months, 24 patients relapsed. IL-10 relapse was defined as a positive conversion in patients with undetectable IL-10 or an increased concentration compared to the last test in patients with sustained IL-10. IL-10 relapse was detected a median of 67 days (28–402 days) earlier than disease relapse in 10/16 patients. Conclusion This study highlights a new perspective that CSF IL-10 relapse could be a surrogate marker for disease relapse and detected earlier than conventional magnetic resonance imaging (MRI) scan. Further evaluation of IL-10 monitoring in PCNSL follow-up is warranted.

Objective Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. Materials and methods A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set ( n  = 153, medical center 1) and an external test set ( n  = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the “late fusion” theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. Results In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model ( P  = 0.02). Conclusion The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. Clinical Relevance Statement The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.

Objectives To evaluate the ability of the initial area under the curve (IAUC) derived from dynamic contrast-enhanced MR imaging (DCE-MRI) and apparent diffusion coefficient (ADC) in differentiating between primary central nervous system lymphoma (PCNSL) and atypical glioblastoma (GBM). Methods We retrospectively identified 19 patients with atypical GBM (less than 13 % necrosis of the enhancing tumour), and 23 patients with PCNSL. The histogram parameters of IAUC at 30, 60, 90 s (IAUC30, IAUC60, and IAUC90), and ADC were compared between PCNSL and GBM. The diagnostic performances and added values of the IAUC and ADC for differentiating between PCNSL and GBM were evaluated. Interobserver agreement was assessed via intraclass correlation coefficient (ICC). Results The IAUC and ADC parameters were higher in GBM than in PCNSL. The 90th percentile (p90) of IAUC30 and 10th percentile (p10) of ADC showed the best diagnostic performance. Adding p90 of IAUC30 to p10 of ADC improved the differentiation between PCNSL and GBM (area under the ROC curve [AUC] = 0.886), compared to IAUC30 or ADC alone (AUC = 0.789 and 0.744; P  < 0.05 for all). The ICC was 0.96 for p90 of IAUC30. Conclusions The IAUC may be a useful parameter together with ADC for differentiating between PCNSL and atypical GBM. Key Points • High reproducibility is essential for practical implementation of advanced MRI parameters. • IAUC and ADC are highly reproducible parameters. • IAUC values were higher in atypical GBM than in PCNSL. • Adding IAUC to ADC improved the differentiation between PCNSL and GBM. • IAUC with ADC are useful for differentiating PCNSL from GBM.