Multi-modal Medical Information based Data Mining for Expression and Characteristic Pattern Prediction of TP53 in Endometrial Carcinoma
Tianming Du, Chen Li, Tao Jiang, Jinzhu Yang, Marcin Grzegorzek, Hongzan Sun · 2023
In the medical field, on the one hand, data mining can effectively establish evaluation models to supplement gold standards; on the other hand, it can guide the direction of scientific research by establishing connections between knowledge. Radiology images and pathological images are considered to be the most suitable medical data for data mining due to their large amount of information. Endometrial carcinoma is a common malignant tumor in women, and TP53 mutation status is an important factor affecting the occurrence and development of tumors. In this study, we propose a neural network structure based on multi-modal medical data that can predict TP53 mutations in endometrial carcinoma, with an accuracy of 86.21% in test set. Then, we clustered TP53-related deep learning features, and we believe that there is heterogeneity in TP53-related deep learning features.