Esophageal cancer detection via non-contrast CT and deep learning

Abstract

Background: Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images.

Methods: In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted.

Results: In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test).

Conclusion: The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

Keywords: deep learning; diagnosis; esophageal cancer; no new net; non-contrast chest computed tomography.

Figure 1

Flow diagram of the nnU-net.

Figure 2

Flow diagram of the deep learning model.

Figure 3

Experimental flow chart of the study.

Figure 4

The ROC curve in the deep learning model and radiologists with or without the deep learning model. Blue, red, green, orange, lemon-yellow, blue-green, and pink lines indicate the ROC curve of the deep learning model, radiologist1, radiologist2, radiologist3, radiologist1 with the model, radiologist2 with the model, and radiologist3 with the model.

Figure 5

Visualization of two cancer cases. For the easy case, there is a significant thickening of the diameter and thickening of the esophageal wall; the difficult case is a 77-year-old man diagnosed with T1 stage esophageal cancer; the radiologists failed to accurately diagnose the cancer, whereas the deep learning model successfully detected it by the subtle variation of the esophageal wall thickness. The cancer part is indicated by the red color, and the green color part presents a normal esophagus.

Figure 6

Visualization of a missed diagnosed case by DL. A 62-year-old man diagnosed with T1 stage esophageal cancer under an endoscope; the pathology showed the cancer was confined to the lamina propria of the mucosa and very close to the cardia. The cancer part is indicated by the red color, and the green color part presents a normal esophagus.