Intensive chemotherapy followed by autologous stem cell transplantation in primary central nervous system lymphomas (PCNSLs). Therapeutic outcomes in real life-experience of the French Network.

We analysed the therapeutic outcomes of all consecutive patients with primary central nervous system lymphoma (PCNSL) registered in the prospective French database for PCNSL and treated with intensive chemotherapy (IC) followed by autologous stem cell transplantation (IC-ASCT) between 2011 and November 2019 (271 patients recruited, 266 analysed). In addition, treatment-related complications of thiotepa-based IC-ASCT were analysed from the source files of 85 patients from 3 centers. Patients had received IC-ASCT either in first-line treatment (n = 147) or at relapse (n = 119). The median age at IC-ASCT was 57 years (range: 22-74). IC consisted of thiotepa-BCNU (n = 64), thiotepa-busulfan (n = 24), BCNU-etoposide-cytarabine-melphalan (BEAM, n = 36) and thiotepa-busulfan-cyclophosphamide (n = 142). In multivariate analysis, BEAM and ASCT beyond the first relapse were adverse prognostic factors for relapse risk. The risk of treatment-related mortality was higher for ASCT performed beyond the first relapse and seemed higher for thiotepa-busulfan-cyclophosphamide. Thiotepa-BCNU tends to result in a higher relapse rate than thiotepa-busulfan-cyclophosphamide and thiotepa-busulfan. This study confirms the role of IC-ASCT in first-line treatment and at first-relapse PCNSL (5-year overall survival rates of 80 and 50%, respectively). The benefit/risk ratio of thiotepa-busulfan/thiotepa-busulfan-cyclophosphamide-ASCT could be improved by considering ASCT earlier in the course of the disease and dose adjustment of the IC.

Initial PCV Chemotherapy Followed by Radiotherapy Is Associated With a Prolonged Response But Late Neurotoxicity in 20 Diffuse Low-Grade Glioma Patients.

Background: Study RTOG 9802 in high-risk diffuse low-grade gliomas (DLGGs) showed the potential synergistic effect on survival of the procarbazine, CCNU, and vincristine (PCV) radiotherapy (RT) combination. Limited data on long-term neurocognitive impact and quality of life (QoL) have yet been reported. Patients and Methods: We described a monocentric series of patients treated at first line by the combination of PCV immediately followed by RT between January 01, 1982 and January 01, 2017. Radiological data were collected and included volume, velocity of diametric expansion (VDE), and MRI aspects. Long-term neurocognitive and QoL were analyzed. Results: Twenty patients fulfilled the eligibility criteria. The median response rate was 65.1% (range, 9.6%-99%) at the time of maximal VDE decrease corresponding to a median volume reduction of 79.7 cm3 (range, 3.1 to 174.2 cm3), which occurred after a median period of 7.2 years (range, 0.3-21.9) after the end of RT. An ongoing negative VDE was measured in 13/16 patients after the end of RT, with a median duration of 6.7 years (range, 9 months-21.9 years). The median follow-up since radiological diagnosis was 17.5 years (range, 4.8 to 29.5). Estimated median survival was 17.4 years (95% CI: 12; NR). After a long-term follow-up, substantial neurotoxicity was noticed with dementia in six progression-free patients (30%), leading to ventriculo-peritoneal shunt procedures in three, and premature death in five. Thirteen patients (65%) were unable to work with disability status. Successive longitudinal neurocognitive assessments for living patients showed verbal episodic memory deterioration. Conclusions: PCV-RT combination seems to have not only an oncological synergy but also a long-term neurotoxic synergy to consider before initial therapeutic decision.

Adult Diffuse Low-Grade Gliomas: 35-Year Experience at the Nancy France Neurooncology Unit.

BACKGROUND: To report survival, spontaneous prognostic factors, and treatment efficacy in a French monocentric cohort of diffuse low-grade glioma (DLGG) patients over 35 years of follow-up. METHODS: A monocentric retrospective study of 339 patients diagnosed with a new DLGG between 01/01/1982 and 01/01/2017 was created. Inclusion criteria were patient age ≥18 years at diagnosis and histological diagnosis of WHO grade II glioma (according to 1993, 2007, and 2016 WHO classifications). The survival parameters were estimated using the Kaplan-Meier method with a 95% confidence interval. Differences in survival were tested for statistical significance by the log-rank test. Factors were considered significant when p ≤ 0.1 and p ≤ 0.05 in the univariate and multivariate analyses, respectively. RESULTS: A total of 339 patients were included with a median follow-up of 8.7 years. The Kaplan-Meier median overall survival was 15.7 years. At the time of radiological diagnosis, Karnofsky Performance Status score and initial tumor volume were significant independent prognostic factors. Oncological prognostic factors were the extent of resection for patients who underwent surgery and the timing of radiotherapy for those concerned. In this study, patients who had delayed radiotherapy (provided remaining low grade) did not have worse survival compared with patients who had early radiotherapy. The functional capabilities of the patients were preserved enough so that they could remain independent during at least three quarters of the follow-up. CONCLUSION: This large monocentric series spread over a long time clarifies the effects of different therapeutic strategies and their combination in the management of DLGG.

A probabilistic map of negative motor areas of the upper limb and face: a brain stimulation study.

Negative motor responses (NMRs) are defined as movement arrests induced by direct electrical stimulation of the brain. The NMRs manifest themselves after the disruption of a corticosubcortical network involved in motor control, referred to as the 'negative motor network'. At present, the spatial topography of the negative motor areas (NMAs) is poorly known. Hence, the objectives of the present study were to establish the first probabilistic map of the NMAs of the upper limbs and face, identify potential subareas, and investigate the NMAs' relationships with the primary motor cortex. A total of 117 patients with low grade glioma underwent awake surgery with direct electrostimulation. The Montreal Neurological Institute coordinates of sites eliciting NMRs (face and upper limbs) were registered. A probabilistic map was created, and subareas were identified in a cluster analysis. Each cluster was then plotted on the Glasser atlas and the 1200 Subjects Group Average Data from the Human Connectome Project, in order to study connectivity and compare the results with recent parcellation data. We elicited 386 NMRs (mean ± standard deviation current intensity: 2.26 ± 0.5 mA) distributed throughout the precentral gyrus in both hemispheres. In each hemisphere, we found two clusters for facial NMRs. For upper limb NMRs, we found two clusters in the right hemisphere; and three in the left. Each cluster overlapped with parcellations from the Glasser atlas. For the face, the NMAs were associated with areas 55b and 6v. For the upper limbs, the NMAs were linked to areas 6v, 6d, and 55b. Each NMA cluster showed a specific pattern of functionally connected areas, such as the inferior frontal gyrus, supplementary motor area, parietal areas, and posterior superior temporal gyrus. The white matter pathways projecting to these subareas involved the frontal aslant tract and the frontostriatal tract-both of which are well known to be associated with NMRs. This study constitutes the largest series to date of NMRs mapped to the lateral surface of both hemispheres. Rather than being randomly distributed, the NMAs appeared to be well structured and corresponded to parcellations identified by functional neuroimaging. Moreover, the white matter pathways known to drive NMRs are also connected to regions encompassing NMAs. Taken as a whole, our results suggest that NMAs belong to a large-scale modulatory motor network. Our new probabilistic map might constitute a valuable tool for use in further clinical and fundamental studies of motor control.

Data-Driven Predictive Models of Diffuse Low-Grade Gliomas Under Chemotherapy.

Diffuse low-grade gliomas (DLGG) are brain tumors of young adults. They affect the quality of life of the inflicted patients and, if untreated, they evolve into higher grade tumors where the patient's life is at risk. Therapeutic management of DLGGs includes chemotherapy, and tumor diameter is particularly important for the follow-up of DLGG evolution. In fact, the main clinical basis for deciding whether to continue chemotherapy is tumor diameter growth rate. In order to reliably assist the doctors in selecting the most appropriate time to stop treatment, we propose a novel clinical decision support system. Based on two mathematical models, one linear and one exponential, we are able to predict the evolution of tumor diameter under Temozolomide chemotherapy as a first treatment and thus offer a prognosis on when to end it. We present the results of an implementation of these models on a database of 42 patients from Nancy and Montpellier University Hospitals. In this database, 38 patients followed the linear model and four patients followed the exponential model. From a training data set of a minimal size of five, we are able to predict the next tumor diameter with high accuracy. Thanks to the corresponding prediction interval, it is possible to check if the new observation corresponds to the predicted diameter. If the observed diameter is within the prediction interval, the clinician is notified that the trend is within a normal range. Otherwise, the practitioner is alerted of a significant change in tumor diameter.

Relevance of two manual tumour volume estimation methods for diffuse low-grade gliomas.

Management of diffuse low-grade glioma (DLGG) relies extensively on tumour volume estimation from MRI datasets. Two methods are currently clinically used to define this volume: the commonly used three-diameters solution and the more rarely used software-based volume reconstruction from the manual segmentations approach. The authors conducted an initial study of inter-practitioners' variability of software-based manual segmentations on DLGGs MRI datasets. A panel of 13 experts from various specialties and years of experience delineated 12 DLGGs' MRI scans. A statistical analysis on the segmented tumour volumes and pixels indicated that the individual practitioner, the years of experience and the specialty seem to have no significant impact on the segmentation of DLGGs. This is an interesting result as it had not yet been demonstrated and as it encourages cross-disciplinary collaboration. Their second study was with the three-diameters method, investigating its impact and that of the software-based volume reconstruction from manual segmentations method on tumour volume. They relied on the same dataset and on a participant from the first study. They compared the average of tumour volumes acquired by software reconstruction from manual segmentations method with tumour volumes obtained with the three-diameters method. The authors found that there is no statistically significant difference between the volumes estimated with the two approaches. These results correspond to non-operated and easily delineable DLGGs and are particularly interesting for time-consuming CUBE MRIs. Nonetheless, the three-diameters method has limitations in estimating tumour volumes for resected DLGGs, for which case the software-based manual segmentation method becomes more appropriate.

Predictive models for diffuse low-grade glioma patients under chemotherapy.

Diffuse low-grade gliomas are rare primitive cerebral tumours of adults. These tumors progress continuously over time and then turn to a higher grade of malignancy associated with neurological disability, leading ultimately to death. Tumour size is one of the most important prognostic factors. Thus, it is of great importance to be able to assess the volume of the tumor during the patients' monitoring. MRI is nowadays the recommended modality to achieve this. Furthermore, if surgery remains the first option for diffuse low-grade gliomas, chemotherapy is increasingly used (before or after a possible surgery). However, crucial and difficult questions remain to be answered: identifying subgroups of patients who could benefit from chemotherapy, determining the best time to initiate chemotherapy, defining the duration of chemotherapy and evaluating the optimal time to perform surgery, or otherwise radiotherapy. In this study, we propose to help clinicians in decision-making, by designing new predictive models dedicated to the evolution of the diameter of the tumor. Two proposed statistical models (linear and exponential) have been validated on a database of 16 patients whose temozolomide-based chemotherapy lasted between 14 and 32 months, with an average duration of 22.8 months. The selection of the most appropriate model has been achieved with the corrected Akaike's Information Criterion. The results are very promising, with coefficients of determination varying from 0.79 to 0.97 with an average value of 0.90 for the linear model. This shows it is possible to alert the clinician to a change in the tumor diameter's dynamics.

Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset.

Software-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment's choice. However, manual segmentation being time-consuming, it is difficult to include it in the clinical routine. An alternative to circumvent the time cost of manual segmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas' manual segmentation's reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable.