基于随机森林的宫颈鳞癌放化疗疗效预测
作者:
作者单位:

作者简介:

邓成龙(1993-),男,硕士,主要研究领域为人工智能,计算机辅助诊断.
关贝(1986-),男,博士,高级工程师,主要研究领域为人工智能和大数据,网络安全技术,虚拟化技术,操作系统技术,云计算.
刘德丰(1979-),男,副主任医师,主要研究领域为神经及腹盆部影像诊断.
刘兰祥(1963-),男,主任医师,主要研究领域为脑科学研究,磁共振技术与影像诊断.
石清磊(1982-),男,高级工程师,主要研究领域为医学图像处理.
王浩然(1993-),男,硕士,CCF学生会员,主要研究领域为组合优化.
王永吉(1962-),男,博士,研究员,博士生导师,主要研究领域为人工智能,大数据分析,智能制造,云计算,知识工程,虚拟化技术,隐蔽信道,高可信网络技术,系统仿真,实时系统,传感器网络,数据挖掘,软件工程,图像处理.

通讯作者:

王永吉,E-mail:ywang@itechs.iscas.ac.cn

中图分类号:

TP391

基金项目:

国家重点研发计划(2017YFB1002300,2017YFB1002301,2017YFB1002303)


Prediction of the Efficacy of Radiotherapy and Chemotherapy for Cervical Squamous Cell Carcinoma Based on Random Forests
Author:
Affiliation:

Fund Project:

National Key Research and Development Program of China (2017YFB1002300, 2017YFB1002301, 2017YFB1002303)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    对ⅡB~IVA期的宫颈鳞癌患者来说,放化疗治疗后肿瘤区域可能会出现完全缓解和不完全缓解的情况.根据临床经验可知,如果放化疗后肿瘤区域不能完全缓解,那么患者的生存率很低,而且再采取手术治疗或口服靶向药治疗等其他疗法很难有效.因此,在治疗前筛选出对放化疗不敏感的患者,转而探索个性化治疗方案很有必要.针对上述问题,将放化疗疗效预测问题视为图像分类问题,提出一种基于随机森林算法的宫颈鳞癌放化疗疗效预测模型,筛选出对放化疗不敏感的患者.该模型首先利用小波变换和高斯拉普拉斯算子对3D宫颈鳞癌MRI(magnetic resonance imaging)进行预处理;其次,利用U-net分割宫颈鳞癌MR图像中肿瘤区域;再次,结合3D宫颈鳞癌MR图像和相应的肿瘤区域分割结果提取宫颈鳞癌病变区域的纹理及形状特征,并对提取的特征进行筛选,训练随机森林模型.实验数据集由已标记的85位宫颈鳞癌ⅡB~IVA期患者治疗前MR图像序列组成.实验结果表明,基于随机森林算法的疗效预测模型预测宫颈鳞癌放化疗疗效AUC值为0.921,优于目前最先进的预测方法.

    Abstract:

    For patients with cervical squamous cell carcinoma (SCC) of stage IIB~IVA, complete or incomplete remission may occur in the tumor area after radiotherapy and chemotherapy. According to clinical experience, if the tumor area cannot be completely relieved after receiving chemoradiotherapy, the patient’s survival rate is very low, and other treatments such as surgery or oral targeted drug therapy are difficult to be effective. Therefore, it is necessary to screen patients who are not sensitive to radiotherapy and chemotherapy before treatment and then to explore personalized treatment plans. In view of the above problems, this paper regards the prediction of the efficacy of radiotherapy and chemotherapy as the image classification problem, and proposes a model to predict the efficacy of radiotherapy and chemotherapy for SCC based on random forests algorithm, and screens out patients who are not sensitive to radiotherapy and chemotherapy. First, the 3D SCC MRI (magnetic resonance imaging) is preprocessed by wavelet transform and Gaussian Laplacian; Second, U-net is used to segment the tumor area in MR images; Then, combined with 3D SCC MRI and corresponding tumor segmentation results, the texture and shape features of lesions are extracted and the extracted features are screened to train random forests. The experimental data set consisted of pre-treatment MR image slices of 85 patients with SCC stage IIB~IVA. The experimental results shows that the prediction model based on random forests predicts the efficacy of radiotherapy and chemotherapy for SCC with an AUC value of 0.921, which is better than the most advanced prediction method.

    参考文献
    相似文献
    引证文献
引用本文

邓成龙,关贝,刘德丰,刘兰祥,石清磊,王浩然,王永吉.基于随机森林的宫颈鳞癌放化疗疗效预测.软件学报,2021,32(12):3960-3976

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-10-15
  • 最后修改日期:2020-04-28
  • 录用日期:
  • 在线发布日期: 2021-12-02
  • 出版日期: 2021-12-06
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号