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基于计算机断层扫描的放射组学机器学习模型用于鉴别长骨内生软骨瘤和非典型软骨性肿瘤

Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones.

作者信息

Hong Rui, Li Qian, Ma Jielin, Lu Chunmiao, Zhong Zhiwei

机构信息

Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, China.

Oncology, The Third Hospital of Hebei Medical University, Shijiazhuang, China.

出版信息

Rofo. 2025 Apr;197(4):416-423. doi: 10.1055/a-2344-5398. Epub 2024 Jul 29.

Abstract

To explore the value of CT-based radiomics machine learning models for differentiating enchondroma from atypical cartilaginous tumor (ACT) in long bones and methods to improve model performance.59 enchondromas and 53 ACTs in long bones confirmed by pathology were collected retrospectively. The features were extracted from preoperative CT images of these patients, and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and dimensionality reduction. The selected features were used to construct classification models by thirteen machine learning algorithms. The data set was randomly divided into a training set and a test set at a proportion of 7:3 by ten-fold cross-validation to evaluate the performance of these models.A total of 1199 features were extracted, 9 features were selected, and 13 radiomics machine learning models were constructed. The area under the curve (AUC) of 11 models was more than 0.8, and that of 3 models was more than 0.9. The Extremely Randomized Trees model achieved the best performance (AUC = 0.9375 ± 0.0884), followed by the Adaptive Boosting model (AUC = 0.9188 ± 0.1010) and the Linear Discriminant Analysis model (AUC = 0.9062 ± 0.1459).CT-based radiomics machine learning models had great ability to distinguish enchondroma and ACT in long bones. By using filters to deeply mine high-order features in the original image and selecting appropriate machine learning algorithms, the performance of the model can be improved. · CT-based radiomics machine learning models can distinguish enchondroma and ACT in long bones.. · Using filters and selecting advanced machine learning algorithms can improve model performance.. · Clinical features have limited utility in distinguishing enchondroma and ACT in long bones.. · Hong R, Li Q, Ma J et al. Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones. Fortschr Röntgenstr 2025; 197: 416-423.

摘要

探讨基于CT的放射组学机器学习模型在鉴别长骨内生软骨瘤与非典型软骨肿瘤(ACT)中的价值以及提高模型性能的方法。回顾性收集59例经病理证实的长骨内生软骨瘤和53例长骨ACT患者。从这些患者的术前CT图像中提取特征,并使用最小绝对收缩和选择算子(LASSO)回归进行特征选择和降维。所选特征用于通过13种机器学习算法构建分类模型。通过十折交叉验证将数据集按7:3的比例随机分为训练集和测试集,以评估这些模型的性能。共提取1199个特征,选择9个特征,构建13个放射组学机器学习模型。11个模型的曲线下面积(AUC)大于0.8,3个模型的AUC大于0.9。极端随机树模型性能最佳(AUC = 0.9375 ± 0.0884),其次是自适应增强模型(AUC = 0.9188 ± 0.1010)和线性判别分析模型(AUC = 0.9062 ± 0.1459)。基于CT的放射组学机器学习模型在鉴别长骨内生软骨瘤和ACT方面具有很强的能力。通过使用滤波器深入挖掘原始图像中的高阶特征并选择合适的机器学习算法,可以提高模型的性能。·基于CT的放射组学机器学习模型可以鉴别长骨内生软骨瘤和ACT。·使用滤波器并选择先进的机器学习算法可以提高模型性能。·临床特征在鉴别长骨内生软骨瘤和ACT方面的作用有限。·Hong R, Li Q, Ma J等。基于计算机断层扫描的放射组学机器学习模型鉴别长骨内生软骨瘤和非典型软骨肿瘤。Fortschr Röntgenstr 2025; 197: 416 - 423。

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