Diagnostic and Prognostic Value of AI-Assisted Image Identification in Endometrial Cancer

A study published in Scientific Reports by Xinyu Qi aimed to analyze the value of deep learning algorithm combined with MRI in the risk rate of diagnosis and prognosis of endometrial cancer.

Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model.

A retrospective collection of MRI images from 210 patients with endometrial cancer were used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set.

The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101). It used accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics.

Among the 70 validation cases, 45 were cases of low-risk endometrial cancer and 25 were cases of high-risk endometrial cancer. Receiver operating characteristic (ROC) analysis found that the area under the curve (AUC) for the diagnosis of high-risk endometrial cancer of the proposed model was visibly larger (0.918) compared with traditional esNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758).

The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P<.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases.

The proposed model, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity and specificity. It also demonstrated excellent predictive AC in postoperative recurrence prediction.