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Accurate response evaluation is crucial to select complete responders from rectal cancer patients diagnosed and treated with neoadjuvant (chemo)radiotherapy.
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The aim of this study was to evaluate the accuracy of response prediction with deep learning methods based on endoscopic images and clinical features.
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Present study shows clinical features are combined with endoscopic imaging features to improve the performance of the deep learning models and provide more confident clinical decisions.
- Details could be found in the below link (note that the codes and results provided in this repo are slightly modified):
- The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation
- 722 endoscopic images with clinical variables having 6 features
- Roughly out of 722 records half of them are complete responses and half of them are non-complete responses.
- Basic augmentation techniques (rotation, flipping, shearing, and zooming of the original images) are applied.
- Since endoscopic images are RGB natural images, transfer learning from ImageNet was also applied.
- Clinical variables model for response prediction (Clinical features model)
- Endoscopic images and clinical features model for response prediction (Combined model)
| Dataset | Model | AUC |
|---|---|---|
| Clinical variables (with all 6 features) | FFN | 73% |
| Selected clinical variables (with selected top 3 features) | FFN | 76% |
| Endoscopic image (trained in endoscopic images only) | EfficientNet-B2 | 79% |
| Combined model (endoscopic image and selected clinical features) | EfficientNet-B2 and FFN | 83% |
Reference