Volumetric Evaluation of Staging 18F-FDG PET/CT Images in Patients with Esophageal Cancer

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network

OBJECTIVE

To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis.

METHODS

Attention U-Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their segmentation effectiveness. The volume of EC was calculated using the segmentation results, and the outcomes and times of automatic and human segmentation were compared. All statistical analyses were completed using SPSS 25.0 software.

RESULTS

Among the four EC autosegmentation models, the UperNet Swin had the best autosegmentation results with a DSC of 0.7820 and the highest values of EC sensitivity and PPV. The esophagus, pericardium, aorta and MLN had DSCs of 0.7298, 0.9664, 0.9496 and 0.5091. The DSCs of the UperNet Swin were 0.6164, 0.7842, 0.8190, and 0.7259 for T1-4 EC. The volume of EC and its adjacent structures between the ground truth and UperNet Swin model were not significantly different.

CONCLUSIONS

The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy.

Advances in the Imaging of Esophageal and Gastroesophageal Junction Malignancies

Accurate imaging is key for the diagnosis and treatment of esophageal and gastroesophageal junction cancers . Current imaging modalities, such as computed tomography (CT) and 18F-FDG (2-deoxy-2-[18F]fluoro-D-glucose) positron emission tomography (PET)/CT, have limitations in accurately staging these cancers. MRI shows promise for T staging and residual disease assessment. Novel PET tracers, like FAPI, FLT, and hypoxia markers, offer potential improvements in diagnostic accuracy. 18F-FDG PET/MRI combines metabolic and anatomic information, enhancing disease evaluation. Radiomics and artificial intelligence hold promise for early detection, treatment planning, and response assessment. Theranostic nanoparticles and personalized medicine approaches offer new avenues for cancer therapy.