Extracting Radiomic features from pre-operative and segmented MRI scans improved survival prognosis of glioblastoma Multiforme patients through machine learning: a retrospective study

Kaur, Gurinderjeet; Rana, Prashant Singh; Arora, Vinay

doi:10.1007/s11042-022-14223-x

Cited by 1 publication

(1 citation statement)

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“…Hence, to address these constraints, numerous efficient networks have been introduced without compromising performance, such as ERV-Net, 19 light-weighted U-Net, 20 with atrous convolution block, 21 Triplanar network or 2.5D, 22 and ESPNet. 23 In the context of SD predictions, existing literature indicates that morphological, spatial location, and radiomics-based features have demonstrated significant importance, also mentioned in the study by Kaur et al 24 Mckinsley et al 9 proposed an ensemble of linear and RF regressor models. Age, number of TCs, and WTs predict the SD of patients, with the latter two features derived from segmented results.…”

Section: Recent Developments In Bts and Sd Predictionmentioning

confidence: 98%

Deriving and interpreting robust features for survival prediction of brain tumor patients

Rajput,

Kapdi,

Raval

et al. 2024

Int J Imaging Syst Tech

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Accurate prediction of survival days (SD) is vital for planning treatments in glioma patients, as type‐IV tumors typically have a poor prognosis and meager survival rates. SD prediction is challenging and heavily dependent on the extracted feature sets. Additionally, comprehending the behavior of complex machine learning models is a vital yet challenging aspect, particularly to integrate such models into the medical domain responsibly. Therefore, this study develops a robust feature set and an ensemble‐based regressor model to predict patients' SD accurately. We aim to understand how these features behave and contribute to predicting SD. To accomplish this, we employed post‐hoc interpretable techniques, precisely Shapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE) plots. Furthermore, we introduced an investigation to establish a direct connection between radiomic features and their biological significance to enhance the interpretability of radiomic features. The best SD scores on the BraTS2020 training set are 0.504 for accuracy, 59927.38 mean squared error (MSE), 20101.86 median squared error (medianSE), and 0.725 Spearman ranking coefficient (SRC). The validation set's accuracy is 0.586, MSE is 76529.43, medianSE is 41402.78, and SRC is 0.52. The proposed predictor model exhibited superior performance compared with leading contemporary approaches across multiple performance metrics.

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