Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer

• Published in: BioMed research international 2022-10, Vol.2022 (1), p.9646846-9646846
• Main Authors: Yao, Huanchun, Zhang, Xinglong
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Cancer; Chemotherapy; Clustering; Complications and side effects; Computed tomography; Deep learning; Feature extraction; Feature maps; Health aspects; Hospitals; Image processing; Image segmentation; Information processing; Lymph nodes; Lymphatic metastasis; Lymphatic system; Mathematical models; Medical imaging; Medical prognosis; Metastases; Metastasis; Neural networks; Ovarian cancer; Parameters; Patients; PET imaging; Positron emission; Prediction models; Prognosis; Research methodology; Researchers; Risk factors; Sensitivity; Software; Support vector machines; Surgery; Surgical outcomes; Technology application; Tumors; Wavelet transforms
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2022/9646846

Purpose. We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian cancer patients. We use the feature map formed by wavelet transform and the parameters obtained by image segmentation to build the model. The model is expected to help clinicians and provide additional information about what to do with first-visit patients. Materials and Methods. Our study included 224 patients with ovarian cancer. We have chosen two main methods to extract information from images. On the one hand, we segmented the image to extract the parameters to evaluate the clustering effect. On the other hand, we used wavelet transform to extract the image’s texture information to form the image’s feature map. Based on the above two kinds of information, we used residual neural network and support vector machine for modeling. Results. We established a model to predict lymph node metastasis in patients with primary ovarian cancer using PET images. On the training set, our accuracy was 0.8854, AUC: 0.9472, CI: 0.9098-0.9752, sensitivity was 0.9865, and specificity was 0.7952. On the test set, our accuracy was 0.9104, AUC: 0.9259, CI: 0.8417-0.9889, sensitivity was 0.8125, and specificity was 1.0000. Conclusions. We used wavelet transform to process the preoperative medical images of ovarian cancer patients, and the residual neural network can effectively predict the lymph node metastasis of ovarian cancer patients, which is undoubted of great significance for patients’ staging and treatment options.