Open Access

Purpose Positron emission tomography (PET) with O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) is a well-established tool for non-invasive assessment of adult central nervous system (CNS) tumors. However, data on its diagnostic utility and impact on clinical management in children and adolescents are limited. Methods Twenty-one children and young adults (13 males; mean age, 8.6 ± 5.2 years; range, 1–19 at initial diagnosis) with either newly diagnosed (n = 5) or pretreated (n = 16) CNS tumors were retrospectively analyzed. All patients had previously undergone neuro-oncological work-up including cranial magnetic resonance imaging. In all cases, [18F]FET-PET was indicated in a multidisciplinary team conference. The impact of PET imaging on clinical decision-making was assessed. Histopathology (n = 12) and/or clinical and imaging follow-up (n = 9) served as the standard of reference. Results The addition of [18F]FET-PET to the available information had an impact on further patient management in 14 out of 21 subjects, with avoidance of invasive surgery or biopsy in four patients, biopsy guidance in four patients, change of further treatment in another five patients, and confirmation of diagnosis in one patient. Conclusion [18F]FET-PET may provide important additional information for treatment guidance in pediatric and adolescent patients with CNS tumors.

Background Neuroblastoma is the most common extracranial solid tumour in children. Relapsed or refractory neuroblastoma is associated with a poor outcome. We assessed the combination of irinotecan–temozolomide and dasatinib–rapamycin (RIST) in patients with relapsed or refractory neuroblastoma. Methods The multicentre, open-label, randomised, controlled, phase 2, RIST-rNB-2011 trial recruited from 40 paediatric oncology centres in Germany and Austria. Patients aged 1–25 years with high-risk relapsed (defined as recurrence of all stage IV and MYCN amplification stages, after response to treatment) or refractory (progressive disease during primary treatment) neuroblastoma, with Lansky and Karnofsky performance status at least 50%, were assigned (1:1) to RIST (RIST group) or irinotecan–temozolomide (control group) by block randomisation, stratified by MYCN status. We compared RIST (oral rapamycin [loading 3 mg/m2 on day 1, maintenance 1 mg/m2 on days 2–4] and oral dasatinib [2 mg/kg per day] for 4 days with 3 days off, followed by intravenous irinotecan [50 mg/m2 per day] and oral temozolomide [150 mg/m2 per day] for 5 days with 2 days off; one course each of rapamycin–dasatinib and irinotecan–temozolomide for four cycles over 8 weeks, then two courses of rapamycin–dasatinib followed by one course of irinotecan–temozolomide for 12 weeks) with irinotecan–temozolomide alone (with identical dosing as experimental group). The primary endpoint of progression-free survival was analysed in all eligible patients who received at least one course of therapy. The safety population consisted of all patients who received at least one course of therapy and had at least one post-baseline safety assessment. This trial is registered at ClinicalTrials.gov, NCT01467986, and is closed to accrual. Findings Between Aug 26, 2013, and Sept 21, 2020, 129 patients were randomly assigned to the RIST group (n=63) or control group (n=66). Median age was 5·4 years (IQR 3·7–8·1). 124 patients (78 [63%] male and 46 [37%] female) were included in the efficacy analysis. At a median follow-up of 72 months (IQR 31–88), the median progression-free survival was 11 months (95% CI 7–17) in the RIST group and 5 months (2–8) in the control group (hazard ratio 0·62, one-sided 90% CI 0·81; p=0·019). Median progression-free survival in patients with amplified MYCN (n=48) was 6 months (95% CI 4–24) in the RIST group versus 2 months (2–5) in the control group (HR 0·45 [95% CI 0·24-0·84], p=0·012); median progression-free survival in patients without amplified MYCN (n=76) was 14 months (95% CI 9–7) in the RIST group versus 8 months (4–15) in the control group (HR 0·84 [95% CI 0·51–1·38], p=0·49). The most common grade 3 or worse adverse events were neutropenia (54 [81%] of 67 patients given RIST vs 49 [82%] of 60 patients given control), thrombocytopenia (45 [67%] vs 41 [68%]), and anaemia (39 [58%] vs 38 [63%]). Nine serious treatment-related adverse events were reported (five patients given control and four patients given RIST). There were no treatment-related deaths in the control group and one in the RIST group (multiorgan failure). Interpretation RIST-rNB-2011 demonstrated that targeting of MYCN-amplified relapsed or refractory neuroblastoma with a pathway-directed metronomic combination of a multkinase inhibitor and an mTOR inhibitor can improve progression-free survival and overall survival. This exclusive efficacy in MYCN-amplified, relapsed neuroblastoma warrants further investigation in the first-line setting. Funding Deutsche Krebshilfe.

BACKGROUND Understanding the long-term cognitive sequelae in infant brain tumor survivors remains incomplete, particularly regarding the impact of tumor type, multimodal treatment, and other patient-related factors. This retrospective analysis explores neuropsychological and quality of survival (QoS) outcomes in survivors of atypical teratoid/rhabdoid tumors (AT/RT) and extracranial malignant rhabdoid tumors of soft tissues (eMRT) and kidneys (RTK), all treated within the same framework. Neuropsychological data from children with AT/RT were compared to data from children with non-irradiated low-grade glioma (LGG). METHODS Patients (0 - 36 months at diagnosis) underwent various treatments, including radio-chemotherapy for AT/RT (n = 13) and eMRT/RTK (n = 7), chemotherapy only for LGG (n = 4) and eMRT/RTK (n = 1), or observation for LGG (n = 11). Neuropsychological evaluations were conducted at a median of 6.8 years (AT/RT), 6.6 years (eMRT/RTK), and 5.2 years (LGG) post-diagnosis. RESULTS Impairments were observed for all tumour types. Patients with AT/RT exhibited impairments in fluid intelligence (p =.041; d = 1.11) and visual processing (p =.001; d = 2.09) when compared to LGG-patients. Both groups demonstrated deficits in psychomotor speed and attention abilities (p <.001–.019; d = 0.79–1.90). Diagnosis significantly predicted cognitive outcomes, whereas gender and age-related variables did not. QoS outcomes for all rhabdoid patients indicated lower scores in psychosocial functioning (p =.023; d = 0.78) and quality of life (p =.006; d = 0.79) compared to healthy controls. CONCLUSIONS Infant rhabdoid tumor survivors experience cognitive and quality-of-life sequelae. Patients with AT/RT are especially vulnerable to impairments in fluid intelligence and visual processing, while infant LGG-patients without radiotherapy demonstrated comparable deficits in psychomotor and attention abilities. Close monitoring of neuropsychological and quality of life outcomes is crucial for early onset and multimodal treatment.

Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet classifier, utilizing automated tumor segmentation. Given the heterogeneity of imaging data (and available sequences), we used all individually available preoperative imaging sequences to make the model robust to varying input. We compared the classifier to diagnostic assessments by five readers with varying experience in pediatric brain tumors. Overall, we included 195 preoperative MRIs from children with medulloblastoma (n = 69) or pilocytic astrocytoma (n = 126) across six university hospitals. In the 64-patient test set, the DenseNet classifier achieved a high AUC of 0.986, correctly predicting 62/64 (97%) diagnoses. It misclassified one case of each tumor type. Human reader accuracy ranged from 100% (expert neuroradiologist) to 80% (resident). The classifier performed significantly better than relatively inexperienced readers (p < 0.05) and was on par with pediatric neuro-oncology experts. Our proof-of-concept study demonstrates a deep learning model based on automated tumor segmentation that can reliably preoperatively differentiate between medulloblastoma and pilocytic astrocytoma, even in heterogeneous data.