Published online Aug 21, 2022. doi: 10.3748/wjg.v28.i31.4363
Peer-review started: March 16, 2022
First decision: June 11, 2022
Revised: June 11, 2022
Accepted: July 25, 2022
Article in press: July 25, 2022
Published online: August 21, 2022
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 carcinoma of the esophagogastric junction (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.
Radiomics technique uses a combined medical-industrial approach to transform traditional images into digital quantitative features, which has potential for digging the potential biological characteristics and heterogeneity of tumor images and has been widely and non-invasively used in the diagnosis, differential diagnosis, and disease evaluation. However, to the best of our knowledge, there is no literature that has evaluated whether a radiomics signature derived from computed tomography (CT) images would be useful in predicting pathological type in patients with CEGJ.
In the current study, we proposed a CT radiomics-based classification method by considering the performance of 3D or 2D segmentation and multiple CT imaging phases to discriminate SCCEG and AEG.
We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (n = 130) and AEG (n = 130). One thousand four hundred and nine radiomics features were separately extracted from 2D or 3D regions of interest in arterial and venous phases. Totally, 6 logistic regression models were established based on 2D and 3D multi-phase features.
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 the 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.
The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.
These models require further validation as decision support tools to guide clinical practice and develop individualized treatment plans for CEGJ patients.