基于多参数术前磁共振成像的影像组学和机器学习可准确预测软组织肉瘤的组织病理学分级。
Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas.
作者信息
Wang Hexiang, Chen Haisong, Duan Shaofeng, Hao Dapeng, Liu Jihua
机构信息
Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
GE Healthcare, Shanghai, China.
出版信息
J Magn Reson Imaging. 2020 Mar;51(3):791-797. doi: 10.1002/jmri.26901. Epub 2019 Sep 5.
BACKGROUND
Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning.
PURPOSE
To assess the value of radiomics features in distinguishing histological grades of STSs.
STUDY TYPE
Retrospective.
POPULATION
In all, 113 patients with pathology-confirmed low-grade (grade I), intermediate-grade (grade II), or high-grade (grade III) soft tissue sarcoma were collected.
FIELD STRENGTH/SEQUENCE: The 3.0T axial T -weighted imaging (T WI) with 550 msec repetition time (TR); 18 msec echo time (TE), 312 × 312 matrix, fat-suppressed fast spin-echo T WI with 4291 msec TR, 85 msec TE, 312 × 312 matrix.
ASSESSMENT
Multiple machine-learning methods were trained to establish classification models for predicting STS grades. Eighty STS patients (18 low-grade [grade I]; 62 high-grade [grades II-III]) were enrolled in the primary set and we tested the model with a validation set with 33 patients (7 low-grade, 26 high-grade).
STATISTICAL TESTS
- Student's t-tests were applied for continuous variables and the χ test were applied for categorical variables between low-grade STS and high-grade STS groups. 2) For feature subset selection, either no subset selection or recursive feature elimination was performed. This technology was combined with random forest and support vector machine-learning methods. Finally, to overcome the disparity in the frequencies of the STS grades, each machine-learning model was trained i) without subsampling, ii) with the synthetic minority oversampling technique, and iii) with random oversampling examples, for a total of 12 combinations of machine-learning algorithms that were assessed, trained, and tested in the validation cohort.
RESULTS
The best classification model for the prediction of STS grade was a combination of features selected by recursive feature elimination and random forest classification algorithms with a synthetic minority oversampling technique, which had an area under the curve of 0.9615 (95% confidence interval 0.8944-1.0) in the validation set.
DATA CONCLUSION
Radiomics feature-based machine-learning methods are useful for distinguishing STS grades.
LEVEL OF EVIDENCE
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:791-797.
背景
软组织肉瘤（STSs）术前分级预测因其对治疗方案的影响而具有重要意义。
目的
评估放射组学特征在区分软组织肉瘤组织学分级中的价值。
研究类型
回顾性研究。
研究对象
共收集了113例经病理证实为低级别（I级）、中级别（II级）或高级别（III级）软组织肉瘤的患者。
场强/序列：3.0T轴位T加权成像（T WI），重复时间（TR）为550毫秒；回波时间（TE）为18毫秒，矩阵为312×312，脂肪抑制快速自旋回波T WI，TR为4291毫秒，TE为85毫秒，矩阵为312×312。
评估
采用多种机器学习方法训练以建立预测软组织肉瘤分级的分类模型。80例软组织肉瘤患者（18例低级别[I级]；62例高级别[II - III级]）纳入主要数据集，并用33例患者（7例低级别，26例高级别）的验证集对模型进行测试。
统计检验
1）对低级别软组织肉瘤组和高级别软组织肉瘤组之间的连续变量应用Student's t检验，分类变量应用χ检验。2）对于特征子集选择，未进行子集选择或递归特征消除。该技术与随机森林和支持向量机学习方法相结合。最后，为克服软组织肉瘤分级频率的差异，每个机器学习模型在以下三种情况下进行训练：i）不进行欠采样，ii）采用合成少数类过采样技术，iii）随机过采样示例，总共12种机器学习算法组合在验证队列中进行评估、训练和测试。
结果
预测软组织肉瘤分级的最佳分类模型是递归特征消除和随机森林分类算法结合合成少数类过采样技术选择的特征组合，在验证集中曲线下面积为0.9615（95%置信区间0.8944 - 1.0）。
数据结论
基于放射组学特征的机器学习方法有助于区分软组织肉瘤分级。证据水平：3 技术效能：2期 J. Magn. Reson. Imaging 2020;51:791 - 797。
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