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

Abstract

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.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

“…Results showed that the model achieved high accuracy, AUC, sensitivity, and specificity on the training set (accuracy: 88.54%, AUC: 0.94, sensitivity: 98.65%, specificity: 79.52%) and the test set (accuracy: 91.04%, AUC: 0.92, sensitivity: 81.25%, specificity: 100%). In conclusion, the study displays the effectiveness of wavelet transform and the residual neural network in predicting lymph node metastasis in ovarian cancer patients, providing valuable insights for patient staging and treatment decisions [ 46 ].…”

Development and validation of an ultrasound‑based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis

“…However, the sample size of this study was small and the credibility of the conclusions is speculative. Yao et al [ 28 ] developed a model for predicting the lymph node status based on PET images of patients with ovarian cancer using residual neural networks and SVM for modeling. Their model had an AUC of 0.92 in the test cohort, but the model only included patients with early-stage ovarian cancer.…”

Predictive value of PET metabolic parameters for occult lymph node metastases in PET/CT defined node-negative patients with advanced epithelial ovarian cancer

“…Previous studies have demonstrated that hypermetabolic LN on PET/CT, initial CA125 ≥ 500, and initial peritoneal cancer index ≥ 10 were independently and signi cantly associated with pelvic and/or para-aortic LNM 26,27 . Recently, many researchers have focused on radiomics features of PET/CT or contrast-enhanced CT in predicting LNM in AEOC, among which LNM assessment has been proven to have a certain signi cant improvement 28,29 . However, the manually timeconsuming delineation of ROI and complex models limit its clinical application.…”