Comparison of Normalization Techniques for Radiomics Features From Magnetic Resonance Imaging in Predicting Histologic Grade of Meningiomas
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초록
'Purpose: This study aimed to compare the effects of different normalization methods on radiomics features extracted from magnetic resonance imaging (MRI). Materials and Methods: Preoperative T1-contrast enhanced MRI data from 212 patients with meningiomas were obtained from two university hospitals. The tumors were segmented using 3D Slicer software, and the PyRadiomics framework was used to extract radiomics features. We developed four experiments to predict the histological grade of meningiomas prior to surgery. The first experiment was performed without normalization. The next three experiments used the StandardScaler, MinMaxScaler, and RobustScaler to normalize radiomics features. The PyCaret framework was used for feature selection and to explore an optimized machine learning model for predicting meningioma grades. The prediction models were trained and validated using data from the first hospital. External test data from the second hospital were used to test the performance of the final models. Results: Our testing results demonstrated that meningioma grade prediction performance depends highly on the choice of the normalization method. The RobustScaler demonstrated a higher level of accuracy and sensitivity than the other normalization methods. The area under the receiver operating characteristic curve and specificity of the RobustScaler method were comparable to those of no-normalization but higher than those of the Standard and MinMaxScaler methods. Conclusion: The results of our study suggest that careful consideration of the normalization method may provide a way to optimize the experimental results.
Abstract
'Purpose: This study aimed to compare the effects of different normalization methods on radiomics features extracted from magnetic resonance imaging (MRI). Materials and Methods: Preoperative T1-contrast enhanced MRI data from 212 patients with meningiomas were obtained from two university hospitals. The tumors were segmented using 3D Slicer software, and the PyRadiomics framework was used to extract radiomics features. We developed four experiments to predict the histological grade of meningiomas prior to surgery. The first experiment was performed without normalization. The next three experiments used the StandardScaler, MinMaxScaler, and RobustScaler to normalize radiomics features. The PyCaret framework was used for feature selection and to explore an optimized machine learning model for predicting meningioma grades. The prediction models were trained and validated using data from the first hospital. External test data from the second hospital were used to test the performance of the final models. Results: Our testing results demonstrated that meningioma grade prediction performance depends highly on the choice of the normalization method. The RobustScaler demonstrated a higher level of accuracy and sensitivity than the other normalization methods. The area under the receiver operating characteristic curve and specificity of the RobustScaler method were comparable to those of no-normalization but higher than those of the Standard and MinMaxScaler methods. Conclusion: The results of our study suggest that careful consideration of the normalization method may provide a way to optimize the experimental results.
참고문헌 (16)
- Castellano G, "Texture analysis of medical images", Clin Radiol, 59, pp.1061~1069 (2004)
- Park YW, "Radiomics and deep learning in brain metastases : current trends and roadmap to future applications", Investig Magn Reson Imaging, 25, pp.266~280 (2021)
- Amano T, "Preoperative prediction of intracranial meningioma grade using conventional CT and MRI", Cureus, 14, pp.e21610~ (2022)
- Zhang S, "Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping", J Neuroradiol, 47, pp.272~277 (2020)
- Lin BJ, "Correlation between magnetic resonance imaging grading and pathological grading in meningioma", J Neurosurg, 121, pp.1201~1208 (2014)
- Kawahara Y, "Prediction of highgrade meningioma by preoperative MRI assessment", J Neurooncol, 108, pp.147~152 (2012)
- Coroller TP, "Radiographic prediction of meningioma grade by semantic and radiomic features", PLoS One, 12, pp.e0187908~ (2017)
- Park JH, "Predicting histologic grade of meningiomas using a combined model of radiomic and clinical imaging features from preoperative MRI", Biomedicines, 11, pp.3268~ (2023)
- Park JE, "Reproducibility and generalizability in radiomics modeling : possible strategies in radiologic and statistical perspectives", Korean J Radiol, 20, pp.1124~1137 (2019)
- Louis DN, "The 2016 World Health Organization classification of tumors of the central nervous system : a summary", Acta Neuropathol, 131, pp.803~820 (2016)
- van Griethuysen JJM, "Computational radiomics system to decode the radiographic phenotype", Cancer Res, 77, pp.e104~e107 (2017)
- "An open-source, low-code machine learning library in Python"[URL]
- "Compare the effect of different scalers on data with outliers"[URL]
- Ke G, "LightGBM : a highly efficient gradient boosting decision tree", Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS 2017), pp.3149~3157 (2017), Curran Associates Inc
- Friedman JH, "Greedy function approximation : a gradient boosting machine", Ann Stat, 29, pp.1189~1232 (2001)
- Lin X, "Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture notes in electrical engineering", pp.417~424 (2021), Springer