| 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 |
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| 基于随机森林的宫颈鳞癌放化疗疗效预测 |
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邓成龙1,2, 关贝1,2, 刘德丰3, 刘兰祥3, 石清磊4, 王浩然5,2, 王永吉1,5,2
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1.中国科学院 软件研究所 协同创新中心, 北京 100190;2.中国科学院大学, 北京 100049;3.秦皇岛市第一医院 核磁科, 河北 秦皇岛 066000;4.山东大学 软件学院, 山东 济南 250101;5.计算机科学国家重点实验室(中国科学院 软件研究所), 北京 100190
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| 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. |
| 关键词: 宫颈鳞癌 放化疗疗效预测 随机森林 U-net 肿瘤特征提取 |
| DOI：10.13328/j.cnki.jos.006136 |
| 分类号:TP391 |
| 基金项目:国家重点研发计划（2017YFB1002300，2017YFB1002301，2017YFB1002303） |
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| Prediction of the Efficacy of Radiotherapy and Chemotherapy for Cervical Squamous Cell Carcinoma Based on Random Forests |
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DENG Cheng-Long1,2, GUAN Bei1,2, LIU De-Feng3, LIU Lan-Xiang3, SHI Qing-Lei4, WANG Hao-Ran5,2, WANG Yong-Ji1,5,2
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1.Collaborative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Department of Magnetic Resonance Imaging, First Hospital of Qinhuangdao, Qinhuangdao 066000, China;4.School of Software, Shandong University, Jinan 250101, China;5.State Key Laboratory of Computer Science(Institute of Software, Chinese Academy of Sciences), Beijing 100190, China
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| 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. |
| Key words: cervical squamous cell carcinoma prediction of the efficacy of chemoradiotherapy random forests U-net tumor feature extraction |
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