Glioblastoma (GBM) and meningothelial meningioma (MM) are the most frequent malignant and benign brain lesions, respectively. Mechanical cues play a major role in the progression of both malignancies that is modulated by the occurrence of aberrant physical interactions between neoplastic cells and the extracellular matrix (ECM). Here we investigate the nano-mechanical properties of human GBM and MM tissues by atomic force microscopy. Our measures unveil the mechanical fingerprint of the main hallmark features of both lesions, such as necrosis in GBM and dural infiltration in MM. These findings have the potential to positively impact on the development of novel AFM-based diagnostic methods to assess the tumour grade. Most importantly, they provide a quantitative description of the tumour-induced mechanical modifications in the brain ECM, thus being of potential help in the search for novel ECM targets for brain tumours and especially for GBM that, despite years of intense research, has still very limited therapeutic options.
Atomic Force Microscopy (AFM) has the unique capability of probing the nanoscale mechanical properties of biological systems that affect and are affected by the occurrence of many pathologies, including cancer. This capability has triggered growing interest in the translational process of AFM from physics laboratories to clinical practice. A factor still hindering the current use of AFM in diagnostics is related to the complexity of AFM data analysis, which is time-consuming and needs highly specialized personnel with a strong physical and mathematical background. In this work, we demonstrate an operator-independent neural-network approach for the analysis of surgically removed brain cancer tissues. This approach allowed us to distinguish—in a fully automated fashion—cancer from healthy tissues with high accuracy, also highlighting the presence and the location of infiltrating tumor cells.
Atomic force microscopy (AFM) in spectroscopy mode receives a lot of attention because of its potential in distinguishing between healthy and cancer tissues. However, the AFM translational process in clinical practice is hindered by the fact that it is a time-consuming technique in terms of measurement and analysis time. In this paper, we attempt to address both issues. We propose the use of neural networks for pattern recognition to automatically classify AFM force–distance (FD) curves, with the aim of avoiding curve-fitting with the Sneddon model or more complicated ones. We investigated the applicability of this method to the classification of brain cancer tissues. The performance of the classifier was evaluated with receiving operating characteristic (ROC) curves for the approach and retract curves separately and in combination with each other. Although more complex and comprehensive models are required to demonstrate the general applicability of the proposed approach, preliminary evidence is given for the accuracy of the results, and arguments are presented to support the possible applicability of neural networks to the classification of brain cancer tissues. Moreover, we propose a possible strategy to shorten measurement times based on the estimation of the minimum number of FD curves needed to classify a tissue with a confidence level of 0.005. Taken together, these results have the potential to stimulate the design of more effective protocols to reduce AFM measurement times and to get rid of curve-fitting, which is a complex and time-consuming issue that requires experienced staff with a strong data-analysis background.
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