The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer

Abstract

Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage cervical cancer preoperatively. Methods: US images of 148 cervical cancer patients were collected and manually contoured by two senior radiologists. The four deep learning-based automatic segmentation models, namely U-net, context encoder network (CE-net), Resnet, and attention U-net were constructed to segment the tumor volumes automatically. Radiomics features were extracted and selected from manual and automatically segmented regions of interest (ROIs) to predict the LNM of these cervical cancer patients preoperatively. The reliability and reproducibility of radiomics features and the performances of prediction models were evaluated. Results: A total of 449 radiomics features were extracted from manual and automatic segmented ROIs with Pyradiomics. Features with an intraclass coefficient (ICC) > 0.9 were all 257 (57.2%) from manual and automatic segmented contours. The area under the curve (AUCs) of validation models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net were 0.692, 0.755, 0.696, 0.689, and 0.710, respectively. Attention U-net showed best performance in the LNM prediction model with a lowest discrepancy between training and validation. The AUCs of models with automatic segmentation features from attention U-net, CE-net, Resnet, and U-net were 9.11%, 0.58%, -0.44%, and 2.61% higher than AUC of model with manual contoured features, respectively. Conclusion: The reliability and reproducibility of radiomics features, as well as the performance of radiomics models, were affected by manual segmentation and automatic segmentations.

Keywords: cervical cancer; lymph node metastasis; radiomics; segmentation; ultrasound.

Figure 1.

(a) The architecture of attention U-net: U-net with added attention gate (AG); (b) U-net with Resnet: the backbone of U-net is replaced by Resnet; context encoder network (CE-net): context extractor consisted of dense atrous convolution (DAC) and residual multi-kernel pooling (RMP) block was added into U-net with Resnet.

Figure 2.

A typical segmentation results from manual delineation, U-net, attention-net, context encoder network (CE-net), and Resnet models

Figure 3.

Selection of lymph node metastasis (LNM)-associated radiomics features using the elastic net method: (a, c, e, h, j) Tuning parameter (λ) in the elastic net used 10-fold cross-validation via maximum area under the curve (AUC); (b, d, f, i, k) The coefficient profiles of radiomics features against the L1 norm (inverse proportional to log λ) for manual, U-net, CE-Net, Resnet, and attention U-net segmentations, respectively.

Figure 4.

The area under the curve (AUCs) of models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net in (a) training cohort and in (b) validation cohort.