Objective Real-life studies on patients with primary CNS lymphoma (PCNSL) are scarce. Our objective was to analyze, in a nationwide population-based study, the current medical practice in the management of PCNSL. Methods The French oculo-cerebral lymphoma network (LOC) database prospectively records all newly diagnosed PCNSL cases from 32 French centers. Data of patients diagnosed between 2011 and 2016 were retrospectively analyzed. Results We identified 1,002 immunocompetent patients (43% aged >70 years, median Karnofsky Performance Status [KPS] 60). First-line treatment was high-dose methotrexate-based chemotherapy in 92% of cases, with an increasing use of rituximab over time (66%). Patients 60 years of age, WBRT and HCT-ASCT consolidation were administered in only 9% and 2%, respectively. The complete response rate to initial chemotherapy was 50%. Median progression-free survival was 10.5 months. For relapse, second-line chemotherapy, HCT-ASCT, WBRT, and palliative care were offered to 55%, 17%, 10%, and 18% of patients, respectively. The median, 2-year, and 5-year overall survival was 25.3 months, 51%, and 38%, respectively (60 years: 15.4 months, 44%, and 28%). Age, KPS, sex, and response to induction CT were independent prognostic factors in multivariate analysis. Conclusions Our study confirms the increasing proportion of elderly within the PCNSL population and shows comparable outcome in this population-based study with those reported by clinical trials, reflecting a notable application of recent PCNSL advances in treatment.

PET plays an increasingly important role in the management of brain tumors. This review outlines currently available PET radiotracers and their respective indications. It specifically focuses on ¹⁸F-FDG, amino acid and somatostatin receptor radiotracers, for imaging gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in radiopharmaceuticals, image analyses and translational applications to therapy are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain MRI for all medical professionals implicated in brain tumor diagnosis and care.

Objective: To assess whether RAF and MEK inhibitors (RAFi/MEKi) can provide long-term clinical benefit in adult patients with BRAF V600-mutant glial and glioneuronal tumors (GGNT), we analyzed tumor response and long-term outcome in a retrospective cohort. Methods: We performed a retrospective search in the institutional databases of six neuroncology departments for adult patients with recurrent or disseminated BRAF V600-mutant GGNT treated with RAFi/MEKi. Results: Twenty-eight adults with recurrent or disseminated BRAF V600-mutant gangliogliomas (n=9), pleomorphic xanthoastrocytomas (n=9), and diffuse gliomas (n=10) were included in the study. At the time treatment with RAFi/MEKi was started, all tumors displayed radiological features of high-grade neoplasms. Thirteen patients received RAFi as single agents [vemurafenib (n=11), dabrafenib (n=2)], and 15 combinations of RAFi/MEKi [vemurafenib+cobimetinib (n=5), dabrafenib+trametinib (n=10)]. Eleven patients achieved a partial or complete response (11/28, 39%), with a median reduction of -78% in their tumor burden. Responders experienced a median increase of 10 points in their Karnofsky Performance Status (KPS) and a median progression-free survival of 18 months, which was longer than achieved with first-line treatment (i.e., 7 months, p=0.047). Responders had better KPS (p=0.018), tended to be younger (p=0.061) and to be treated earlier (p=0.099) compared to non-responders. Five patients were rechallenged with RAFi/MEKi at progression, with novel tumor responses in two. On univariate and multivariate analyses, response to RAFi/MEKi was an independent predictor of overall survival. Conclusions: Our study highlights the clinical benefits of RAFi/MEKi in adult patients with BRAF-mutant GGNT, encourages the systematic screening and rechallenge in responders.

The incidence of primary central nervous system lymphoma has increased over the past two decades in immunocompetent patients and the prognosis remains poor. A diagnosis and complete evaluation of the patient is needed without delay, but histologic evaluation is not always available and PCNSL can mimic a variety of brain lesions on MRI. In this article, we review the potential role of ¹⁸F-FDG PET for the diagnosis of PCNSL in immunocompetent and immunocompromised patients. Its contribution to systemic assessment at the time of diagnosis has been well established by expert societies over the past decade. In addition, ¹⁸F-FDG provides valuable information for differential diagnosis and outcome prediction. The literature also shows the potential role of ¹⁸F-FDG as a therapeutic evaluation tool during the treatment and the end of the treatment. Finally, we present several new radiotracers that may have a potential role in the management of PCNSL in the future.

This study evaluates the relevance of ¹⁸F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic ¹⁸F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBR_mean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.