Délégation Paris 11

Abstract BACKGROUND Isocitrate dehydrogenase-mutant (mIDH) diffuse gliomas, including astrocytomas and oligodendrogliomas, initially grow slowly before transitioning to a malignant phase. Despite advances in surgery, radiotherapy, and chemotherapy, survival remains limited. Assessing tumour size and growth rate is crucial but complicated by measurement variability, impacting treatment decisions and outcome prediction. OBJECTIVES This systematic review examines the correlation between tumour size and growth rate measured after resection with overall survival (OS), progression-free survival (PFS), malignant progression-free survival (M-PFS), and symptom burden (seizures, headaches, neurological deficits) in mIDH gliomas. METHODS A comprehensive literature search was conducted in MEDLINE/PubMed, EMBASE, Cochrane Library, and grey literature sources (scientific congresses and health authority communications) from January 1, 2014, to January 17, 2024. Eligible studies focused on adult patients (≥18 years) with mIDH gliomas and assessed post-resection tumour size (diameter or volume) or tumour growth rate, and clinical outcomes such as OS, PFS, M-PFS, time to treatment (TTT), malignant transformation (MT) and symptoms, including seizures, headaches, and neurological deficits. Two independent reviewers screened articles, extracted data, and assessed study quality. RESULTS A total of 56 studies were selected, of which 28 studies met the inclusion criteria and collectively included 11,510 patients. Of these, 25 assessed tumour volume, 1 measured tumour diameter, and 5 examined tumour growth. Most studies (82.1%, n=23) were retrospective. The selected studies classified tumors according to the 2016 WHO classification, with 46.0% (n=5,295) identified as IDH-mutant, 27.3% (n=3,141) as 1p/19q-codeleted, and 5.2% (n=597) as IDH-WT. Most studies identified a significant correlation between residual tumour size/growth rate and OS (85.7%, n=18/21) or PFS (84.6%, n=11/13). Additionally, of the 14 studies assessing a correlation with other outcomes such as M-PFS, symptom progression, MT and TTT, 92.9% (n=12/13) identified an inverse correlation with tumour size or growth rate. Only 6 studies did not demonstrate a significant association between tumour diameter or volume and OS, PFS or M-PFS. Altogether, these findings suggest that residual tumour size and tumour growth rate may serve as predictors of clinical outcomes, including OS, PFS, M-PFS, MT, TTT, and symptoms such as seizures in mIDH diffuse gliomas. CONCLUSIONS Tumour volume and growth rate are significant predictors of clinical outcomes in mIDH gliomas, especially OS and PFS. Implementing volumetric assessment and tumor growth monitoring into clinical practice may enhance the management and follow-up of patients with mIDH gliomas.

Urban stormwater management is a major challenge for limiting the degradation of surface aquatic environments. Given the limitations of traditional sewer systems, nature-based solutions are now being implemented at various scales to control the pollutants generated by runoff during rain events. Two main families of solutions can be distinguished: 'decentralised' systems, aimed at limiting runoff at source, and 'centralised' systems, aimed at storing or treating volumes associated with catchment areas of several hectares, potentially mixing rainwater and wastewater. While the first type of solution should be more advantageous, the second is no less relevant, particularly in constrained urban contexts where the scope for building new facilities is limited. In this context, the City of Paris set up an experiment involving an innovative centralised system based on the planted filter concept. The main goal of this project is to evaluate the efficiency of this system to reduce the pollutant loadings of stormwater runoff. Mineral and organic micro-pollutants are specially investigated with a focus on the dissolved phase that is not sufficiently taken into account in the classic depollution systems of rainwater. In order to compare the innovative filter to a “classic” planted filter, two filters were built. The two filters have an area of 600m2. The classical one consists of, from the bottom to the top: 30cm of gravel 20/40 within which drains are installed, 20cm of gravel 8/20, 10 cm of gravel 4/8 containing aeration network and the filtration layer composed of 40 cm of sand 0/4. The innovative filter only differs by the 40-cm filtration layer which consists of 10cm of sand 0/4, 20 cm of Rainclean® (Funke Kunststoffe GmbH, Hamm, Germany) and 10cm of sand 0/4. Rainclean® was specially selected during the preliminary design studies to optimise the treatment of dissolved organic and metallic micropollutants. It has never been tested before in planted filters. Filters receive runoff waters coming from 21ha of the ring road of the city of Paris (1.3 million vehicles/day) but also clear water during dry wheather. Since the beginning of the experiment (in May, 2021), each filters received about 56 m and 90 m of water during rain events and dry weather respectively. These large volumes of clear water could affect the pollution reduction efficiency of the filters with faster exhaustement of the filter media. However this impact still has to clarified. More generally, an assessment of the current functioning of the filters is still necessary. To do so, water sampling to estimate the capacity of the filters to reduce pollution is undergoing.

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff.