Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction.

BACKGROUND

The biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.

AIM: To establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively.

METHODS

We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (n = 130) and AEG (n = 130). The data were divided into either a training (n = 182) or a test group (n = 78) at a ratio of 7:3. A total of 1409 radiomics features were separately extracted from two dimensional (2D) or three dimensional (3D) regions of interest in arterial and venous phases. Intra-/inter-observer consistency analysis, correlation analysis, univariate analysis, least absolute shrinkage and selection operator regression, and backward stepwise logical regression were applied for feature selection. Totally, six logistic regression models were established based on 2D and 3D multi-phase features. The receiver operating characteristic curve analysis, the continuous net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) were used for assessing model discrimination performance. Calibration and decision curves were used to assess the calibration and clinical usefulness of the model, respectively.

RESULTS

The 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI > 0, IDI > 0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (P < 0.05). Decision curve analysis showed that combined 3D-arterial-venous model and 3D-venous model had a higher net clinical benefit within the same threshold probability range in the test group.

CONCLUSION

The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.