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Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.

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Title: Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.
Authors: Sadeghinasab, Amirreza, Fatahiasl, Jafar, Tahmasbi, Marziyeh, Razmjoo, Sasan, Yousefipour, Mohammad
Source: Health Science Reports; Jan2025, Vol. 8 Issue 1, p1-11, 11p
Subject Terms: MACHINE learning, RECEIVER operating characteristic curves, MAGNETIC resonance imaging, GLIOBLASTOMA multiforme, RADIOMICS
Abstract: Background and Objectives: Assessing treatment response in glioblastoma multiforme (GBM) tumors necessitates developing more objective and quantitative approaches. A machine learning‐based approach is presented in this exploratory study for GBM patients' treatment response assessment based on radiomics extracted from magnetic resonance (MR) images. Methods: MR images from 77 GBM patients were acquired at two post‐surgery stages and preprocessed. From these images, 107 radiomics were extracted from the segmented tumoral cavities. The most informative features for training machine learning (ML) classifiers were identified using the Spearman correlation analysis of features retained by the forward sequential and LASSO algorithms. Applied machine learning models included support vector machine (SVM), random forest (RF), K‐nearest neighbors (KNN), AdaBoost, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), Naïve Bayes (NB) and logistic regression (LR). Ten‐fold cross‐validation was used to validate the models. Statistical analysis was conducted using SPSS version 27; p‐value < 0.05 was considered significant. Results: The Naïve Bayes classifier demonstrated the highest performance among the trained models, achieving an AUC (area under the receiver operating characteristic curve) of 0.86 ± 0.13 when trained on the seven features selected by the forward sequential algorithm and an AUC of 0.84 ± 0.14 when trained using the five features chosen by the LASSO algorithm. The second‐best performance was observed with the KNN classifier, which achieved an AUC of 0.80 ± 0.17 when trained on the features selected by the forward sequential algorithm. Conclusion: Findings demonstrated that MRI‐based radiomics could be used as distinctive features to train ML models for GBM patients' treatment response assessment. Trained ML classifiers based on these features serve as aiding tools to expedite the quantitative assessment of GBM patients' treatment response besides qualitative evaluations. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Background and Objectives: Assessing treatment response in glioblastoma multiforme (GBM) tumors necessitates developing more objective and quantitative approaches. A machine learning‐based approach is presented in this exploratory study for GBM patients' treatment response assessment based on radiomics extracted from magnetic resonance (MR) images. Methods: MR images from 77 GBM patients were acquired at two post‐surgery stages and preprocessed. From these images, 107 radiomics were extracted from the segmented tumoral cavities. The most informative features for training machine learning (ML) classifiers were identified using the Spearman correlation analysis of features retained by the forward sequential and LASSO algorithms. Applied machine learning models included support vector machine (SVM), random forest (RF), K‐nearest neighbors (KNN), AdaBoost, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), Naïve Bayes (NB) and logistic regression (LR). Ten‐fold cross‐validation was used to validate the models. Statistical analysis was conducted using SPSS version 27; p‐value < 0.05 was considered significant. Results: The Naïve Bayes classifier demonstrated the highest performance among the trained models, achieving an AUC (area under the receiver operating characteristic curve) of 0.86 ± 0.13 when trained on the seven features selected by the forward sequential algorithm and an AUC of 0.84 ± 0.14 when trained using the five features chosen by the LASSO algorithm. The second‐best performance was observed with the KNN classifier, which achieved an AUC of 0.80 ± 0.17 when trained on the features selected by the forward sequential algorithm. Conclusion: Findings demonstrated that MRI‐based radiomics could be used as distinctive features to train ML models for GBM patients' treatment response assessment. Trained ML classifiers based on these features serve as aiding tools to expedite the quantitative assessment of GBM patients' treatment response besides qualitative evaluations. [ABSTRACT FROM AUTHOR]
ISSN:23988835
DOI:10.1002/hsr2.70323