Despite the recent advances in minimally invasive surgery, kidney stones still pose a significant clinical challenge due to their high recurrence rate of 50% in 5–10 years after the first stone episode. Using the methods of geosciences and biology, the GeoBioMed approach treats kidney stones as biogenic minerals, offering a novel perspective on their formation and dissolution processes. In this review, we discuss kidney stones’ structural and mechanical properties as emerging biomarkers of urolithiasis, emphasizing the importance of a comprehensive stone analysis in developing personalized treatment strategies. By focusing on unexplored properties like crystalline architecture, porosity, permeability, cleavage, and fracture, alongside the conventionally used composition and morphology, we show how these stone characteristics influence the treatment efficacy and the disease recurrence. This review also highlights the potential of advanced imaging techniques to uncover novel biomarkers, contributing to a deeper understanding of stone pathogenesis. We discuss how the interdisciplinary collaboration within the GeoBioMed approach aims to enhance the diagnostic accuracy, improve the treatment outcomes, and reduce the recurrence of urolithiasis.

Despite advances in minimally invasive surgery, urolithiasis still recurs within the first five years after the first episode in more than 50% of patients. Researchers continue to search for new crystallisation inhibitors, metaphylaxis strategies and laser sources for stone destruction. Therefore, to achieve these goals, it is necessary to study kidney stones not only as the result of an isolated process of pathological biomineralisation in the human body, but also as a biogenic mineral or rock that obeys universal patterns and has certain properties. Consequently, kidney stones need to be studied using methods that are widely used in the geological sciences for the study of minerals, such as computed microtomography and petrological analysis. In this review, the properties of kidney stones studied using various research methods used in geosciences are discussed. These properties are also considered as new biomarkers of urolithiasis. This review discusses how new data from multimodal stone analysis can be used to develop personalised metaphylaxis and treatment strategies for all types of urolithiasis, including the most common idiopathic calcium-oxalate urolithiasis.

Background . Preoperative differentional diagnosis of meningioma grade remains challenging with routine brain magnetic resonance imaging (MRI). The lack of reliable non-invasive tools limits the potential for early risk stratification and treatment planning. Aim : To develop an interpretable classification radiomic model RadMenGR for predicting meningioma grade (Grade I or Grade II) based on contrast-enhanced T1-weighted images. Material and Methods . This retrospective single-center study was conducted using the open-source anonymized dataset MeningiomaSEG-CLASS. 95 patients were included in the analysis (53 with Grade 1 and 42 with Grade 2 tumors). 105 radiomic features were extracted from manually segmented MR images using PyRadiomics. Classification was performed with a Naive Bayes algorithm following feature discretization using the Entropy / MDL method. Diagnostic performance was assessed using the area under the curve (AUC), sensitivity, specificity, and accuracy. Bootstrap analysis with 10,000 iterations and a 95% confidence interval was used for validation. Results . On the validation cohort (n = 46), the ROC-AUC was 0.805 (95% CI: 0.671–0.915). The lower bound of the 95% CI for the AUC exceeded the value under the null hypothesis (AUC = 0.63), confirming the statistical significance of the results (p < 0.05). Conclusion. This study developed an interpretable radiomic classification model for the differential diagnosis of Grade 1 and Grade 2 meningiomas. The application of a Naive Bayes algorithm to features extracted from contrast-enhanced T1-weighted images and transformed using a discretization method enabled the achievement of a significant level of diagnostic accuracy. However, the width of the confidence interval points to a lack of model robustness, necessitating validation on an independent cohort.