Artificial intelligence-enhanced magnetic resonance imaging-based pre-operative staging in patients with endometrial...

Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.

Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus. The deep learning method was trained using sagittal T2-weighted images from 142 patients and tested with a 3-fold stratified test with 36 patients in each fold. Our solution is based on a segmentation module, which employed a 2-stage pipeline for efficient uterus in the whole MRI volume and then tumor segmentation in the uterus predicted region of interest.

A total of 178 patients were included. For deep myometrial invasion prediction, the model achieved an average balanced test accuracy over 3-folds of 0.702, while experts reached an average accuracy of 0.769. For cervical stroma invasion prediction, our model demonstrated an average balanced accuracy of 0.721 on the 3-fold test set, while experts achieved an average balanced accuracy of 0.859. Additionally, the accuracy rates for uterus and tumor segmentation, measured by the Dice score, were 0.847 and 0.579 respectively.

Despite the current challenges posed by variations in data, class imbalance, and the presence of artifacts, our fully automatic approach holds great promise in supporting in pre-operative staging. Moreover, it demonstrated a robust capability to segment key regions of interest, specifically the uterus and tumors, highlighting the positive impact our solution can bring to health care imaging.