Prognosis prediction of high grade serous adenocarcinoma based on multi-modal convolution neural network

The prognostic analysis for high grade serous adenocarcinoma (HGSC) holds significant clinical importance. However, current prognostic analysis primarily relies on statistical techniques like logistic regression and chi-square analysis alongside traditional machine learning methods based on pattern recognition. These approaches face challenges in addressing the limited reliability and validity of evaluation results, as well as the absence of reliable prognostic indicators. To identify a reliable prognostic evaluation method for high grade serous adenocarcinoma, a novel prognostic evaluation method was constructed using multi-modal deep learning techniques and compared with existing methods using data from 210 patients with high grade serous adenocarcinoma (stage III). The experimental results showed that the accuracy of this method for prognostic analysis was 80.0%, and the detection rate for poor prognosis cases was 82.87%, which was superior to current methods. Our proposed method could also automatically extract key features from different datasets and efficiently predict patient outcomes. Overall, this study laid the groundwork to overcome the difficulties in the prognostic evaluation of HGSC, help clinicians better understand the pathogenesis, and improve the long-term survival rates of this patient population.