Current State-of-the-Art of AI Methods Applied to MRI

Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.publishersversionpublishe

Correction of beta-thalassemia major by gene transfer in haematopoietic progenitors of pediatric patients

Beta-thalassemia is a common monogenic disorder due to mutations in the beta-globin gene and gene therapy, based on autologous transplantation of genetically corrected haematopoietic stem cells (HSCs), holds the promise to treat patients lacking a compatible bone marrow (BM) donor. We recently showed correction of murine beta-thalassemia by gene transfer in HSCs with the GLOBE lentiviral vector (LV), expressing a transcriptionally regulated human beta-globin gene. Here, we report successful correction of thalassemia major in human cells, by studying a large cohort of pediatric patients of diverse ethnic origin, carriers of different mutations and all candidates to BM transplantation. Extensive characterization of BM-derived CD34(+) cells before and following gene transfer shows the achievement of high frequency of transduction, restoration of haemoglobin A synthesis, rescue from apoptosis and correction of ineffective erythropoiesis. The procedure does not significantly affect the differentiating potential and the relative proportion of haematopoietic progenitors. Analysis of vector integrations shows preferential targeting of transcriptionally active regions, without bias for cancer-related genes. Overall, these results provide a solid rationale for a future clinical translation

Does elastic anisotropy significantly affect a tunnel's plane strain behavior?

Rock masses are anisotropic because their properties depend on the orientation considered. The diverging opinions in the literature on whether the elastic anisotropy of a rock mass significantly affects the plane strain behavior of a tunnel are contrasted. A two-dimensional parametric study is presented to answer the questions raised by the literature review. For a given premining state of stress, the stress field around a tunnel in an anisotropic rock mass is not significantly different from the stress field around a tunnel in an isotropic rock mass. For a given anisotropic rock mass, the stress and displacement fields, as well as slip zones, obtained under the hypothesis of no lateral strain are radically different from that obtained under the hypothesis of uniform premining state of stress. In the first case, the slip zones can penetrate more than two diameters into the rock mass, especially for vertical or inclined joints. In the second case, the slip zones extend for a maximum of half a tunnel diameter into the rock mass, regardless of rock mass anisotropy. Displacement vector magnitudes are highly influenced by the elastic anisotropy of the rock mass, even if the premining state of stress is fixed. For a given premining state of stress, slip zones around a tunnel are unaffected by the elastic anisotropy of the rock mass. The slip zones depend only on the orientation of the joints along which slippage can occur

A Search Algorithm for Calculating Validated Reliability Bounds

Abstract. The search algorithm presented allows the CDF of a dependent variable to be bounded with 100 % confidence, and allows for a guaranteed evaluation of the error involved. These reliability bounds are often enough to make decisions, and require a minimal number of function calls. The procedure is not intrusive, i.e. it can be equally applied when the function is a complex computer model (black box). The proposed procedure can handle input information consisting of probabilistic, interval-valued, set-valued, or random-set-valued information, as well as any combination thereof. The function as well as the joint pdf of the input variables can be of any type. 1