Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer.
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...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/11/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159720896&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159720896 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/11/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159720896 159720896 159720896 10.1155/2022/9646846 159720896 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer. aug: au: Yao, Huanchun Zhang, Xinglong affil: Department of Cancer, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004 Liaoning Province, China sug: subj: Prediction Models Neural Networks (Computer) Methods Lymph Nodes Pathology Neoplasm Metastasis Ovarian Neoplasms Epidemiology Human Tomography, Emission-Computed Tomography, X-Ray Computed Support Vector Machine Cluster Analysis Confidence Intervals Sensitivity and Specificity ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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