通过PET-CT图像纹理特征预测软组织肉瘤转移性

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摘要:

提出了一种针对软组织肉瘤转移性预测的辅助诊断方法,该方法通过对患者的FDG-PET和CT诊断图像进行纹理特征分析,共提取了105个特征,其中包括灰度共生矩阵的24个特征和其他81个灰度等级的特征,分别利用支持向量机、K近邻和随机森林等机器学习算法建立预测模型,并采用网格搜索法对其参数进行优化.最后使用留一交叉验证法对各模型进行验证.通过评估各模型性能,选择支持向量机作为最终预测模型,得到了80%的平均精确度.此外,该模型的敏感度达到81%,特异性达到79%,表明该模型预测结果具有一定的可靠性,可以对STS进行辅助诊断并通过更好的适应性治疗来改善患者的预后.

This paper proposes an auxiliary diagnostic method for soft tissue sarcoma metastasis prediction.This method extracts 105 features which include 24 features of the Gray Level Co-occurrence Matrix(GLCM)and other 81 grayscale features by analyzing the texture features of FDG-PET and CT diagnostic images.Machine learning algorithms such as Support Vector Machine(SVM),K-Nearest Neighbor(KNN)and Random Forest(RF)are used to build prediction models,and their parameters are optimized by grid search method.Finally,the models are evaluated by the leave-one-out cross-validation method.By evaluating the performance of each model,support vector machine can be selected as the final prediction model,and the average accuracy of 80%is obtained.In addition,the sensitivity and specificity of this model reached 81%and 79%respectively,indicating that the predicted results of this model have certain reliability,which can be used to aid diagnosis of STS and improve patient outcomes through better adaptive treatment.

作者:

申俊丽余堃

Shen Junli;Yu Kun(School of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China)

机构地区:

betway官方app 计算机与信息工程学院

出处:

《betway官方app 学报：自然科学版》 CAS 北大核心 2021年第2期25-30,共6页

基金:

国家自然科学基金青年基金(11601130)。

关键词:

软组织肉瘤纹理特征机器学习转移性预测

soft tissue sarcoma texture feature machine learning metastatic prediction