Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor , it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors . Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI images , Fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumor representation. Fast detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. In this work, a new framework for radiomics analysis of GBM on FLAIR images is proposed. The framework can be used both for an initial detection of GBM and in case for its segmentation. The novelty of the methodology is the combination of new topological features computed by topological data analysis , textural features and of automatic interpretable machine learning algorithm. The framework was evaluated on a public available dataset and it reaches up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task .
Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumour , it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumours . Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI , Fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumour representation. Fast detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. In the present investigation, we intend to demonstrate by means of numerical experiments that topological features combined with textural features can be enrolled for GBM analysis and morphological characterization on FLAIR. To this extent, we have performed three numerical experiments. In the first experiment, Topological Data Analysis (TDA) of a simplified 2D tumour growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumour growth: the higher the concentration of chemical nutrients the more virulent the process. In the second experiment topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment (e.g., chemo-radiation therapy - CRT) completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we had developed a novel methodology based on topological and textural features and automatic interpretable machine learning for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy up to the 97\% .
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this work, a new framework for radiomics analysis of GBM on FLAIR images is proposed. The framework can be used both for an initial detection of GBM and in case for its segmentation. The novelty of the methodology is the combination of new topological features computed by topological data analysis ,
the present investigation, we intend to demonstrate by means of numerical experiments that topological features combined with textural features can be enrolled for GBM analysis and morphological characterization on FLAIR. To this extent, we have performed three numerical experiments. In the first experiment, Topological Data Analysis (TDA) of a simplified 2D tumour growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumour growth: the higher the concentration of chemical nutrients the more virulent the process. In the second experiment topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment (e.g., chemo-radiation therapy - CRT) completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we had developed a novel methodology based on topological and
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algorithm. The framework was evaluated on a public available dataset and it reaches
for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy
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of accuracy on the detection task and up to 95\% of accuracy on the segmentation task