Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer

Chenwen Sun¹QIanling Li¹Yanan Huang²Yang Xia³Meiping Li³Xiucong Zhu²Jinke Zhu²Zhenhua Zhao^2*

• ¹School of Medicine, Graduate School, Zhejiang University, Hangzhou, China
• ²Department of Radiology, Shaoxing People's Hospital, Shaoxing, Zhejiang Province, China
• ³Shaoxing Maternity and Child Health Care Center, Shaoxing, Zhejiang Province, China

Background: To develop a machine learning model integrates multi-parametric magnetic resonance imaging (MRI) radiomics features and clinicopathological features to predict the expression status of phosphatase and tension homolog (PTEN), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and mammalian target of rapamycin (mTOR), which are frequently linked with targeted therapy for endometrial cancer (EC), in order to establish a dependable foundation for personalized adjuvant therapy for EC patients. Methods: we retrospectively recruited 82 EC patients who underwent preoperative MRI and radical resection at two independent hospitals. 60 patients from Center 1 were utilized as the training set for constructing the machine learning model, while 22 patients from Center 2 served as an external validation set to assess the model's performance. We evaluated the performance of models predicted three proteins' expression using receiver operating characteristic (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). Result: To construct machine learning models for predicting the expression of PTEN, PIK3CA, and mTOR, we respectively screened 5 radiomic and 7 clinicopathologic features, 4 radiomic and 9 clinicopathologic features, and 2 radiomic and 10 clinicopathologic features. The area under the curve (AUC) values of the radscore, clinicopathology, and combination models predicting PTEN expression were 0.875, 0.703, and 0.891 in the training set, and 0.750, 0.844, and 0.833 in the validation set, respectively. The AUC values for the models predicted PIK3CA expression in the training set were 0.856, 0.633, and 0.880, respectively, in the validation set, they were 0.842, 0.667, and 0.825. The AUC of each model for mTOR were 0.896, 0.831, and 0.912 in the training set, and 0.729, 0.847, and 0.829 in the validation set. Calibration curve analysis and DCA showed that the combination models were both well calibrated and clinically useful. Conclusion: Machine learning models integrating multi-parametric MRI radiomics and clinicopathological features can be a potential tool for predicting PTEN, PIK3CA, and mTOR expression status in EC patients.