Prediction of the Efficacy of Radiotherapy and Chemotherapy for Cervical Squamous Cell Carcinoma Based on Random Forests [基于随机森林的宫颈鳞癌放化疗疗效预测]

作者：Deng C.-L.; Guan B.; Liu D.-F.; Liu L.-X.; Shi Q.-L.; Wang H.-R.; Wang Y.-J.

来源：Journal of Software, 2021, 32(12): 3960-3976.

DOI：10.13328/j.cnki.jos.006136

• Cervical squamous cell carcinoma
• Prediction of the efficacy of chemoradiotherapy
• Random forests
• Tumor feature extraction
• U-net

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.

• 单位
  计算机科学国家重点实验室; 中国科学院大学