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. 2024 Nov 14:14:1449235.
doi: 10.3389/fonc.2024.1449235. eCollection 2024.

Development and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence

Fabrizio Pignotti #  1   2 , Tamara Ius #  3 , Rosellina Russo  4 , Daniele Bagatto  5 , Francesco Beghella Bartoli  6 , Edda Boccia  6 , Luca Boldrini  6 , Silvia Chiesa  6 , Chiara Ciardi  5 , Davide Cusumano  7 , Carolina Giordano  4 , Giuseppe La Rocca  8 , Ciro Mazzarella  6 , Edoardo Mazzucchi  1   2 , Alessandro Olivi  2   8 , Miran Skrap  3 , Houng Elena Tran  6 , Giuseppe Varcasia  4 , Simona Gaudino  2   4 , Giovanni Sabatino  1   2   8
Affiliations

Development and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence

Fabrizio Pignotti et al. Front Oncol. .

Abstract

Purpose: Patients diagnosed with High Grade Gliomas (HGG) generally tend to have a relatively negative prognosis with a high risk of early tumor recurrence (TR) after post-operative radio-chemotherapy. The assessment of the pre-operative risk of early versus delayed TR can be crucial to develop a personalized surgical approach. The purpose of this article is to predict TR using MRI radiomic analysis.

Methods: Data were retrospectively collected from a database. A total of 248 patients were included based on the availability of 6-month TR results: 188 were used to train the model, the others to externally validate it. After manual segmentation of the tumor, Radiomic features were extracted and different machine learning models were implemented considering a combination of T1 and T2 weighted MR sequences. Receiver Operating Characteristic (ROC) curve was calculated with relative model performance metrics (accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) at the best threshold based on the Youden Index.

Results: Models performance were evaluated based on test set results. The best model resulted to be the XGBoost, with an area under ROC curve of 0.72 (95% CI: 0.56 - 0.87). At the best threshold, the model exhibits 0.75 (95% CI: 0.63 - 0.75) as accuracy, 0.62 (95% CI: 0.38 - 0.83) as sensitivity 0.80 (95% CI: 0.66 - 0.89 as specificity, 0.53 (95% CI: 0.31 - 0.73) as PPV, 0.88 (95% CI: 0.72 - 0.94) as NPV.

Conclusion: MRI radiomic analysis represents a powerful tool to predict late HGG recurrence, which can be useful to plan personalized surgical treatments and to offer pertinent patient pre-operative counseling.

Keywords: high grade glioma; machine learning; prognosis; radiomics; recurrence.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Segmentation of the tumoral areas: in image (A), delineation of the ROI on 3D T1 W post contrast images on the “enhancing” component of the tumor. In image (B), the ROI on axial 2D T2w images is delineated for the solid and infiltrative components of the tumor.
Figure 2
Figure 2
Patients classification.
Figure 3
Figure 3
Boxplots of the selected radiomics features used for radiomics modeling showing stability with respect to the outcome with corresponding p-values resulting from the WMW test. (‘1’=patients with 6-months TR, ‘0’=patients without 6-months TR).
Figure 4
Figure 4
Cross-correlation matrix of the T1wT2w significant features used for radiomics modeling. feat1: original_shape_MajorAxisLength; feat2: original_shape_Maximum2DDiameterColumn; feat3: original_shape_Maximum2DDiameterSlice; feat4: original_firstorder_TotalEnergy; feat5: Original_first_order_kurtosis.
See this image and copyright information in PMC

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