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. Author manuscript; available in PMC: 2023 Apr 1.
Published in final edited form as: Semin Ultrasound CT MR. 2022 Feb 11;43(2):153–169. doi: 10.1053/j.sult.2022.02.005

Table 4:

Studies investigating the use of ML to differentiate between types of brain tumors

Study Purpose Number of Patients Findings
Kim et al.56 (2018) Differentiate GBM vs. primary central nervous system lymphoma (PCNSL) using multiparametric MRI-based radiomics 143 patients (n = 86 training; n = 57 validation)
  • 15 features used in final model

  • AUC validation = 0.956

  • AUC training = 0.979

Shrot et al.57 (2019) Differentiating different brain tumors using basic and advanced MRI-based radiomics 141 patients (41 GBM, 38 METS, 50 meningioma & 12 PCNSL)
  • Classification used morphologic MRI, perfusion MRI & DTI metrics

  • Feature subset selection via SVMs

  • Binary SVM classification accuracy ranged from 81.6 to 97.0

Niu et al.58 (2019) Differentiating between different meningioma subtypes using basic MRI-based radiomics 241 patients (n = 80 meningiothelial meningioma, n = 80 fibrous meningioma, n = 81 transitional meningioma)
  • Fisher discriminant analysis model for binary differentiation between meningioma types had accuracies between 98.8% and 100%

  • Leave one out cross validation had accuracies between 91.3% and 100%

Nakagawa et al.59 (2018) Differentiating GBM vs PCNSL using ML method based on texture features in multiparametric MRI 70 patients
  • Prediction model developed using univariate logistic regression and XGBoost

  • rCBV offered highest AUC of 0.86 (rCBV AUC = 0.83; skewness of CE-T1WI AUC = 0.78)

  • AUC of XGBoost was significantly higher than that of two radiologists (0.98 vs 0.84).

Dong et al.60 (2019) Differentiating between pilocytic astrocytoma (PA) and GBM using MRI quantitative radiomic features by a decision tree model 66 patients (PA n = 31; GBM n = 35)
  • Subset of 12 features selected by feature stability and Boruta algorithm to build decision tree model

  • Training set: accuracy = 87%; sensitivity = 90%; specificity = 83%

  • Validation set: accuracy = 86%, sensitivity = 80%; specificity = 91%

Zhang et al.61 (2018) Using MRI-based radiomics to differentiate between non-functioning pituitary adenoma subtypes 112 patients (training set n = 75; test set n = 37)
  • T1WI had AUC of 0.83 and 0.80 in training and test sets, respectively

  • CE-T1WI features added no additional value to model

Chakrabarty et al.62 (2021) Train a CNN to differentiate between tumor types (HGG, LGG, metastases, meningioma, pituitary adenoma, acoustic neuroma & healthy tissue) 1373 (BraTs, TCGA, LGG-1p19q dataset, internal and external dataset)
  • Internal data set: sensitivities, PPVs, AUCs and area under the precision-recall curves (AUPRCs) ranged from 87%−100%, 85% to 100%, 0.98 to 1.0, and 0.91 to 1.0, respectively

  • External data set: sensitivities, PPVs, AUCs and AUPRCs ranged from 91% to 97%, 73% to 99%, 0.97 to 0.98, and 0.9 to 1.0, respectively

Qian et al.53 (2019) To identify the optimal radiomic ML classifier for differentiating GBM vs METS 412
  • SVM + LASSO classifier had highest prediction efficacy (AUC = 0.90, accuracy = 82.7%, sensitivity = 79.8%, specificity = 87.3%, PPV = 90%, and NPV = 72.9%)

Artzi et al.54 (2019) To differentiate between GBM and METS using CE-T1WI MRI-based radiomics 439
  • Best results for differentiating GBM vs. METS were obtained using SVM classifier which had a mean accuracy, AUC, sensitivity, and specificity of 85%, 0.96, 86% and 85%, respectively

  • Optimal differentiation of GBM and METS subtypes achieved using SVM classifier with accuracy, AUC, sensitivity, and specificities ranging between 75%−90%, 0.57–0.98, 11%−100%, and 76%−99% respectively,

Kniep et al.55 (2018) Using multiparametric MRI-based radiomics to predict tumor type in brain metastasis (SCLC, BC, MM, GC and NSCLC) 189
  • AUC for predicting type of brain metastasis ranged between 0.64 (NSCLC) and 0.82 (MM)

  • Prediction performance of classifier was superior to radiologists’ readings

  • MM had highest increase in sensitivity (17%) using classifier compared to radiologists’ readings