内生软骨瘤
医学
人工智能
软骨肉瘤
接收机工作特性
卷积神经网络
放射科
磁共振成像
模式识别(心理学)
人工神经网络
计算机科学
机器学习
内科学
作者
Fatih Erdem,İpek Tamsel,Gülen Demirpolat
摘要
Abstract Purpose To construct and compare machine learning models for differentiating chondrosarcoma from enchondroma using radiomic features from T1 and fat suppressed Proton density (PD) magnetic resonance imaging (MRI). Methods Eighty‐eight patients (57 with enchondroma, 31 with chondrosarcoma) were retrospectively included. Histogram matching and N4ITK MRI bias correction filters were applied. An experienced musculoskeletal radiologist and a senior resident in radiology performed manual segmentation. Voxel sizes were resampled. Laplacian of Gaussian filter and wavelet‐based features were used. One thousand eight hundred eighty‐eight features were obtained for each patient, with 944 from T1 and 944 from PD images. Sixty‐four unstable features were removed. Seven machine learning models were used for classification. Results Classification with all features showed neural network was the best model for both readers' datasets with area under the curve (AUC), classification accuracy (CA), and F1 score of 0.979, 0.984; 0.920, 0.932; and 0.889, 0.903, respectively. Four features, including one common to both readers, were selected using fast correlation based filter. The best performing models with selected features were gradient boosting for Fatih Erdem's dataset and neural network for Gülen Demirpolat's dataset with AUC, CA, and F1 score of 0.990, 0.979; 0.943, 0.955; 0.921, 0.933, respectively. Neural Network was the second‐best model for FE's dataset based on AUC (0.984). Conclusion Using pathology as a gold standard, this study defined and compared seven well‐performing models to distinguish enchondromas from chondrosarcomas and provided radiomic feature stability and reproducibility among the readers.
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