Deep Learning-Based Esophageal Tumor Identification on CT Slice

Abstract

The study aims to use deep learning to improve the accuracy of identifying esophageal tumors on CT slices for radiotherapy planning. The identification of Gross Tumor Volume (GTV) can be challenging due to low contrast with surrounding tissue. Other methods like endoscopy and PET scan can provide additional information, but may not be suitable for radiotherapy due to differences in tissue density and alignment needs. A multi-task deep learning network was developed, which perform both segmentation and slice classification, simultaneously. For segmentation, the normal esophagus and tumor were treated as a single structure due to the difficulty in distinguishing between them on CT images. The slices were divided into 3 categories, including tumor, normal esophageal and other. An Unet was used for segmentation and generate the mask to remove irrelevant areas. The masked image will be input into a Resnet to obtain the categories of slices. The performance of classification was accessed by ROC curve, AUC and confusion matrix on a new dataset and PET images. The multi-task deep learning network was developed on a dataset of 315 patients' CT images and GTV segmentations, which were reviewed and verified by physicians. The model was then evaluated on an additional validation dataset of 30 patients, resulting in an accuracy of 88%. In terms of sensitivity and specificity, the model showed high performance, with a sensitivity of 97% and specificity of 95% for tumor and normal esophagus in the validation dataset. Meanwhile, the specificity was 85% and the specificity was 80% for tumor and normal esophagus in PET images dataset. This multi-task deep learning approach effectively combines the benefits of both segmentation and classification techniques, resulting in improved accuracy and efficiency in identifying esophageal tumors on CT slices for radiotherapy planning.