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.

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.

Entropy-coded lattice vector quantization dedicated to the block mixture densities.

Entropy-coded lattice vector quantization (ECLVQ) with codebooks dedicated to independent identically distributed (i.i.d.) generalized Gaussian sources have proven their high coding performances in the wavelet domain. It is well known that wavelet coefficients with high magnitude (corresponding to edges and textures) tend to be clustered in a few amount of vectors. In this paper, we first show that this property has a major influence on the performances of ECLVQ schemes. Since this clustering property cannot be taken into account by the classical i.i.d. assumption, our first proposal is to model the joint distribution of vectors by a multidimensional mixture of generalized Gaussian (MMGG) densities. The main outcome of this MMGG model is to provide a theoretical framework to simply derive from i.i.d. R- D models, the corresponding MMGG R- D models. In a second part, a new codebook better suited to wavelet coding is proposed: the so-called dead zone lattice vector quantizers (DZLVQ). It consists of generalizing the scalar dead zone to vector quantization by thresholding vectors according to their energy. We show that DZLVQ improves the rate-distortion tradeoff. Experimental results are provided for the pyramidal LVQ scheme under the assumption of a multidimensional mixture of Laplacian (MML) densities. Results performed on a set of real life images show the precision of the analytical R- D curves and the efficiency of the DZLVQ scheme.

Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams.

RATIONALE AND OBJECTIVES: To assess the effect of three-dimensional (3D) lossy image compression of multidetector computed tomography chest scans on computer-aided detection (CAD) of solid lung nodules greater than 4 mm in size. MATERIALS AND METHODS: A total of 120 cases, acquired with 1.25-mm collimation, were collected from 5 different sites, of which 66/120 were low-dose cases. Two chest radiologists established that 37 cases had no actionable lung nodules; the remaining 83 cases contained 169 nodules (range 3.8-35.0 mm, mean 5.8 mm +/- 3.0 [SD]). All cases were compressed using the 3D Set Partitioning in Hierarchical Trees algorithm to 24:1, 48:1, and 96:1 levels. A study of the effect of compression on computer-aided detection (CAD) sensitivity was performed at operating points of 2.5 false marks (FM), 5 FM, and 10 FM per case using McNemar's test. Logistic regression models were used to evaluate the impact on CAD sensitivity by compression level on nodule and image characteristics. RESULTS: Compared with no compression, there was no significant degradation in CAD sensitivity found at any of the studied compression levels and operating points. However, between compression levels, there was marginal association with sensitivity. Specifically, 24:1 level was significantly better than 96:1 at all operating points, and occasionally better than no compression at 10 FM/case. Based on multivariate analysis, nodule location was found to be a significant predictor (P = .01) with a lower sensitivity associated with juxtapleural nodules. Nodule size, dose, reconstruction filter, and contrast medium were not significant predictors. CONCLUSION: CAD detection performance of solid lung nodules did not suffer until 48:1 compression.