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)
出处
《河南师范大学学报（自然科学版）》
CAS
北大核心
2021年第2期25-30,共6页
Journal of Henan Normal University(Natural Science Edition)
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
