ESTRO 2023 - Abstract Book

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ESTRO 2023

Figure 1. Network architecture, hyperparameters and an input image pair comprising CBCT (top) and deformed vCT (bottom) scans

Results Table 1. Evaluation metrics of predicted DSC Rectum Root mean squared error (RMSE) 0.070

Prostate

Bladder

0.079

0.118

R ²

0.53

0.06

0.17

Classification threshold

0.6

0.8

0.8

Accuracy 0.81 % event in test & cross-val set 87.8%, 87.1% 82.6%, 84.1% 83.2%, 78.9% Sensitivity 0.97 0.94 0.97 Specificity 0.59 0.23 0.04 Positive predictive value (PPV) 0.95 0.85 0.83 Negative predictive value (NPV) 0.71 0.44 0.20 0.92 0.82

As seen in Table 1, the model showed promising results for predicting DSC, giving RMSE of 0.070, 0.079 and 0.118 for rectum, prostate, and bladder respectively on the holdout test set. Clinically, a low RMSE implies that the predicted DSC can be reliably used to determine if further DIR assessment from the physician is required. Taking rectum for instance, considering the event where a registered contour is classified as poor if its DSC is below 0.6 and good otherwise, the model achieves an accuracy of 92%. A sensitivity of 0.97 suggests that the model can correctly identify 97% of poorly registered contours, allowing manual assessment of DIR to be triggered. Figure 2 shows the model predictions on the training and test sets.