Effects of deep learning on radiologists’ and radiology residents’ performance in identifying esophageal cancer on CT

Abstract

Objective: To investigate the effectiveness of a deep learning model in helping radiologists or radiology residents detect esophageal cancer on contrast-enhanced CT images. Methods: This retrospective study included 250 and 25 patients with and without esophageal cancer, respectively, who underwent contrast-enhanced CT between December 2014 and May 2021 (mean age, 67.9 ± 10.3 years; 233 men). A deep learning model was developed using data from 200 and 25 patients with esophageal cancer as training and validation datasets, respectively. The model was then applied to the test dataset, consisting of additional 25 and 25 patients with and without esophageal cancer, respectively. Four readers (one radiologist and three radiology residents) independently registered the likelihood of malignant lesions using a 3-point scale in the test dataset. After the scorings were completed, the readers were allowed to reference to the deep learning model results and modify their scores, when necessary. Results: The AUC of the deep learning model was 0.95 and 0.98 in the image- and patient-based analyses, respectively. By referencing to the deep learning model results, the AUCs for the readers were improved from 0.96/0.93/0.96/0.93 to 0.97/0.95/0.99/0.96 (p = 0.100/0.006/<0.001/<0.001, DeLong’s test) in the image-based analysis, with statistically significant differences noted for the three less experienced readers. Furthermore, the AUCs for the readers tended to improve from 0.98/0.96/0.98/0.94 to 1.00/1.00/1.00/1.00 (p = 0.317/0.149/0.317/0.073, DeLong’s test) in the patient-based analysis. Conclusion: The deep learning model mainly helped less experienced readers improve their performance in detecting esophageal cancer on contrast-enhanced CT. Advances in knowledge: A deep learning model could mainly help less experienced readers to detect esophageal cancer by improving their diagnostic confidence and diagnostic performance.

Esophageal cancer detection via non-contrast CT and deep learning

“…However, our experimental results suggested that the DL can also assist radiologists in improving the workups of esophageal cancers by reducing FPs and FNs in non-contrast chest CT scans. In particular, the DL model may improve the performance of junior radiologists to the senior level, which resonates with the conclusion of the studies ( 33 , 35 ). Accordingly, this may shed light on the early detection of esophageal cancers, especially in the context of health check-up examinations.…”

“…The detection of early-stage esophageal cancer can be very challenging for both the radiologist and the DL model (such as Figure 6), but it is important for clinical practice. Referencing the other studies (33,36), the contrast-enhanced CT images may provide more information about esophageal cancer from early to late stage than the non-contrast images. Accordingly, we will consider incorporating contrast-enhanced CT to augment the capability of the DL model.…”

“…Accordingly, a segmentation model may be helpful to improve the detection performance with slightly lower specificity. On the other hand, another image classification CNN for the contrast-enhanced chest images suggested a performance with metrics of sensitivity, specificity, accuracy, and AUC as 0.87, 0.92, 0.92, and 0.95 ( 33 ), respectively, since the contrast-enhanced CT may better depict the esophageal cancers and may ease the algorithmic difficulty for DL models. However, our experimental results suggested that the DL can also assist radiologists in improving the workups of esophageal cancers by reducing FPs and FNs in non-contrast chest CT scans.…”

“…The ICC (95%CI) was 0.942 (0.924 and 0.955), which showed good diagnosis consistency. To further compare the performance of the DL model as well as the readers’ performance with and without the referencing of the DL model, DeLong’s test for AUC was adopted ( 33 ). The overall statistical analyses were carried out with software packages of SPSS 26.0 and MedCalc 22.016.…”

Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis

“…9 Recently, deep-learning applications have gained wide attention in the field of radiology. [10][11][12] Recent studies have demonstrated that deep learning is applicable to both imaging diagnosis [13][14][15] and image processing. 16 Deep-learning reconstruction (DLR) is one such method, which employed deep convolutional neural network to improve image quality.…”