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.

Bovine anaplasmosis caused by Anaplasma marginale is a disease responsible for serious animal health problems and great economic losses all over the world. Thereby, the identification of A. marginale isolates from various bioclimatic areas in each country, the phylogeographic analysis of these isolates based on the most informative markers, and the evaluation of the most promising candidate antigens are crucial steps in developing effective vaccines against a wide range of A. marginale strains. In order to contribute to this challenge, a total of 791 bovine samples from various bioclimatic areas of Tunisia were tested for the occurrence of A. marginale DNA through msp4 gene fragment amplification. Phylogeographic analysis was performed by using lipA and sucB gene analyses, and the genetic relationship with previously characterized A. marginale isolates and strains was analyzed by applying similarity comparison and phylogenetic analysis. To evaluate the conservation of OmpA protein vaccine candidate, almost complete ompA nucleotide sequences were also obtained from Tunisian isolates, and various bioinformatics software were used in order to analyze the physicochemical properties and the secondary and tertiary structures of their deduced proteins and to predict their immunodominant epitopes of B and T cells. A. marginale DNA was detected in 19 bovine samples (2.4%). Risk factor analysis shows that cattle derived from subhumid bioclimatic area were more infected than those that originated from other areas. The analysis of lipA phylogeographic marker indicated a higher diversity of Tunisian A. marginale isolates compared with other available worldwide isolates and strains. Molecular, phylogenetic, and immuno-informatics analyses of the vaccine candidate OmpA protein demonstrated that this antigen and its predicted immunodominant epitopes of B and T cells appear to be highly conserved between Tunisian isolates and compared with isolates from other countries, suggesting that the minimal intraspecific modifications will not affect the potential cross-protective capacity of humoral and cell-mediated immune responses against multiple A. marginale worldwide strains.

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 dataset 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.

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.