A Novel System for Precise Grading of Glioma

Alksas, Ahmed; Shehata, Mohamed; Atef, Hala; Sherif, Fatma Mohamed; Alghamdi, Norah Saleh; Ghazal, Mohammed; Fattah, Sherif Abdel; El-Serougy, Lamiaa; El-Baz, Ayman

doi:10.3390/bioengineering9100532

Cited by 10 publications

(6 citation statements)

References 44 publications

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“…Furthermore, a recent study (46) has shown the superiority of segmenting tumor volume over its ellipsoidal approximation to assess tumor burden in DIPG. The fine delineation of contours will make possible to further test the impact of additional morphological features, including the histogram of oriented gradients as proposed by Alksas et al (47) for the estimation of the genomic mutations. The manual segmentation is however tedious and its reproducibility still needs to be tested.…”

Section: Discussionmentioning

confidence: 99%

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

et al. 2023

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PurposePredicting H3.1, TP53, and ACVR1 mutations in DIPG could aid in the selection of therapeutic options. The contribution of clinical data and multi-modal MRI were studied for these three predictive tasks. To keep the maximum number of subjects, which is essential for a rare disease, missing data were considered. A multi-modal model was proposed, collecting all available data for each patient, without performing any imputation.MethodsA retrospective cohort of 80 patients with confirmed DIPG and at least one of the four MR modalities (T1w, T1c, T2w, and FLAIR), acquired with two different MR scanners was built. A pipeline including standardization of MR data and extraction of radiomic features within the tumor was applied. The values of radiomic features between the two MR scanners were realigned using the ComBat method. For each prediction task, the most robust features were selected based on a recursive feature elimination with cross-validation. Five different models, one based on clinical data and one per MR modality, were developed using logistic regression classifiers. The prediction of the multi-modal model was defined as the average of all possible prediction results among five for each patient. The performances of the models were compared using a leave-one-out approach.ResultsThe percentage of missing modalities ranged from 6 to 11% across modalities and tasks. The performance of each individual model was dependent on each specific task, with an AUC of the ROC curve ranging from 0.63 to 0.80. The multi-modal model outperformed the clinical model for each prediction tasks, thus demonstrating the added value of MRI. Furthermore, regardless of performance criteria, the multi-modal model came in the first place or second place (very close to first). In the leave-one-out approach, the prediction of H3.1 (resp. ACVR1 and TP53) mutations achieved a balanced accuracy of 87.8% (resp. 82.1 and 78.3%).ConclusionCompared with a single modality approach, the multi-modal model combining multiple MRI modalities and clinical features was the most powerful to predict H3.1, ACVR1, and TP53 mutations and provided prediction, even in the case of missing modality. It could be proposed in the absence of a conclusive biopsy.

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Section: Discussionmentioning

confidence: 99%

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

et al. 2023

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“…To date the application of radiomics in GM setting has shown considerable progress in demonstrating that it can be a tool capable of deriving much information, with implications in diagnostics, such as differentiating tumors based on texture analysis, differentiating treatment effects (radiation necrosis, pseudo-progression) and tumor recurrence, in prognosis such as survival stratification (1)(2)(3)(4)(34)(35)(36)(37)(38) and applications in the choice of optimal therapy (39-41), e.g., stratification of response to anti-angiogenic treatment for recurrent glioblastoma.…”

Section: Discussionmentioning

confidence: 99%

“…On the contrary, the use of medical imaging, as a non-invasive tool to derive prognostic factors that can predict outcome such as survival, PFS, and response to therapy, is becoming increasingly popular. The images can be described not only qualitatively in order to highlight the presence of necrotic, edemigenous, malignant, suspected or metabolically active areas, but also quantitatively in order to generate numbers that become real measurable data (3)(4)(5).…”

Section: Introductionmentioning

confidence: 99%

MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation

Chiesa

Russo²,

Bartoli³

et al. 2023

Front. Med.

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BackgroundThe glioblastoma’s bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation.Materials and methodsWe retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model.ResultsTwo-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68–0.88).ConclusionThis is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy.

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“…Analysis of molecular mutations using MRI features proved to be a useful method for diffuse LGG prediction, with the advantage of being a non-invasive procedure 15 . Alksas et al 16 proposed an imaging-based glioma grading system that uses contrast-enhanced MRI, fluid-attenuated inversion-recovery MRI, and diffusion-weighted MRI to extract morphological, textural, and functional features. Then, the optimal features given by the Gini impurity index are fed to a multi-layer perceptron (MLP) to discriminate between different grades of glioma.…”

Section: Introductionmentioning

confidence: 99%

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data

Sánchez-Marqués,

García,

Sánchez

2024

Preprint

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Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms.

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A Novel System for Precise Grading of Glioma

Cited by 10 publications

References 44 publications

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data

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