Developing a MRI-based Deep Learning Model to Predict MMR Status

In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to establish a deep learning model based on MRI to predict the MMR status of endometrial cancer. Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected. Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model. The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds).

Sex

Female

Inclusion Criteria:

* Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery from 2017 to 2022

Exclusion Criteria:

* (1) There was no immunohistochemical detection result of MMR-related protein; (2) Radiotherapy and chemotherapy before MRI; (3) small tumors that are difficult to identify on the image (\<5mm) ; (4) The T2-weighted imaging quality is insufficient to plot ROI, such as obvious motion artifacts; (5) There are other gynecological malignancies