Submucosal Saline Injection Followed by Endoscopic Ultrasound versus Endoscopic Ultrasound Only for Distinguishing between T1a and T1b Esophageal Cancer

Preclinical evaluation of a novel EGFR&c-Met bispecific near infrared probe for visualization of esophageal cancer and metastatic lymph nodes

PURPOSE

This study aimed to establish a near infrared fluorescent (NIRF) probe based on an EGFR&c-Met bispecific antibody for visualization of esophageal cancer (EC) and metastatic lymph nodes (mLNs).

METHODS

EGFR and c-Met expression were assessed by immunohistochemistry. EGFR&c-Met bispecific antibody EMB01 was labeled with IRDye800cw. The binding of EMB01-IR800 was assessed by enzyme linked immunosorbent assay, flow cytometry, and immunofluorescence. Subcutaneous tumors, orthotopic tumors, and patient-derived xenograft (PDX) were established for in vivo fluorescent imaging. PDX models using lymph nodes with or without metastasis were constructed to assess the performance of EMB01-IR800 in differential diagnosis of lymph nodes.

RESULTS

The prevalence of overexpressing EGFR or c-Met was significantly higher than single marker either in EC or corresponding mLNs. The bispecific probe EMB01-IR800 was successfully synthesized, with strong binding affinity. EMB01-IR800 showed strong cellular binding to both Kyse30 (EGFR overexpressing) and OE33 (c-Met overexpressing) cells. In vivo fluorescent imaging showed prominent EMB01-IR800 uptake in either Kyse30 or OE33 subcutaneous tumors. Likewise, EMB01-IR800 exhibited superior tumor enrichment in both thoracic orthotopic esophageal squamous cell carcinoma and abdominal orthotopic esophageal adenocarcinoma models. Moreover, EMB01-IR800 produced significantly higher fluorescence in patient-derived mLNs than in benign lymph nodes.

CONCLUSION

This study demonstrated the complementary overexpression of EGFR and c-Met in EC. Compared to single-target probes, the EGFR&c-Met bispecific NIRF probe can efficiently depict heterogeneous esophageal tumors and mLNs, which greatly increased the sensitivity of tumor and mLN identification.

Aorta and tracheobronchial invasion in esophageal cancer: comparing diagnostic performance of 3.0-T MRI and CT

OBJECTIVES

To compare between the diagnostic performance of 3.0-T MRI and CT for aorta and tracheobronchial invasion in patients with esophageal cancer (EC).

METHODS

We prospectively included patients with pathologically confirmed EC from November 2018 to June 2021, who had baseline stage of T3-4N0-2M0 and restaging after neoadjuvant chemotherapy. All patients underwent contrast-enhanced CT and MRI of the thorax. Two independent blinded radiologists scored image quality and the presence of invasion. Agreements between the two readers were calculated using kappa test. The sensitivity, specificity, accuracy, positive predict value (PPV), and negative predict value (NPV) of MRI and CT in evaluating invasion were calculated. The net reclassification index (NRI) was used to evaluate the change in the number of patients correctly classified by MRI and CT.

RESULTS

A total of 70 patients (64.8 ± 9.0 years; 53 men) were enrolled. Inter-reader agreements of image quality scores and presence of invasion by MRI and CT between the two readers were almost perfect (kappa > 0.80). The accuracy of MRI in evaluating thoracic aorta invasion was significantly higher than that of CT (reader 1: 90.0% vs. 71.4%; reader 2: 92.9% vs. 70.0%, respectively), and the accuracy of MRI in evaluating tracheobronchial invasion also was significantly higher than that of CT (reader 1: 92.9% vs. 72.9%; reader 2: 95.7% vs. 70.0%, respectively). NRI values were positive in both the evaluation of aorta and tracheobronchial invasion.

CONCLUSIONS

The accuracy of 3-T MRI in determining thoracic aorta and tracheobronchial invasion is significantly higher than that of CT.

KEY POINTS

• 3.0-T MRI was significantly more accurate than CT in assessing invasion of the thoracic aorta in patients with esophageal cancer. • 3.0-T MRI was also significantly more accurate than CT in assessing tracheobronchial invasion in patients with esophageal cancer. • 3.0-T MRI has a higher diagnostic performance than CT in evaluating patients with suspected aortic or tracheobronchial invasion in esophageal cancer.

Diagnostic evaluation of endoscopic ultrasonography with submucosal saline injection for differentiating between T1a and T1b early gastric cancer

BACKGROUND

Endoscopic ultrasonography (EUS) has become a reliable method for predicting the invasion depth of early gastric cancer (EGC). However, diagnostic accuracy of EUS is affected by several factors. In particular, it is difficult to differentiate between T1a and T1b EGC through EUS.

AIM

To confirm whether submucosal saline injection (SSI) could improve the accuracy of EUS in distinguishing T1a and T1b lesions in EGC.

METHODS

Twenty-four patients with EGC were examined by EUS and subsequently by SSI combined EUS to compare the degree of tumor invasion. Then, they underwent endoscopic or surgical resection within 7 d. The diagnostic accuracy of EUS and SSI combined EUS was evaluated based on the final pathological findings postoperatively. Saline injected into the submucosa acted as an echoic contrast enhancing agent and had the effect of distinguishing the mucosal and submucosal layers clearly.

RESULTS

Of total 24 patients, 23 were diagnosed with EGC (T1 cancer: 13 as T1a, and 10 as T1b). Standard EUS identified 6 of 13 T1a cancer patients and 3 of 10 T1b cancer patients. Whereas, EUS-SSI identified 12 of 13 T1a cancer patients and 6 of 10 T1b cancer patients. In this study, SSI combined EUS was more accurate than EUS alone in diagnosing T1a and T1b lesions of EGC (75.0% and 37.5%, respectively).

CONCLUSION

SSI improved the diagnostic accuracy of EUS in distinguishing between the T1a and T1b stages in EGC.

Endoscopic submucosal dissection versus esophagectomy for t1 esophageal squamous cell carcinoma: a propensity score-matched analysis

BACKGROUND

Endoscopic submucosal dissection (ESD) has been a preferred treatment option for superficial esophageal squamous cell carcinoma (SESCC).

OBJECTIVES

To compare the outcomes of ESD and esophagectomy in the treatment of SESCC, especially for lesions invading muscularis mucosa or submucosa (pT1a-MM/T1b).

DESIGN

We retrospectively analyzed data from patients with SESCC who underwent ESD or esophagectomy between 2015 and 2021.

METHODS

After propensity score matching, overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS), and treatment-related events were compared between the ESD and esophagectomy groups. Furthermore, we performed a Cox regression analysis to identify factors associated with survival.

RESULTS

OS and DSS were significantly higher in the ESD group (n = 508) than that in the esophagectomy group (n = 466). After matching, 404 patients (202 per group) were included in the study. No significant differences were found between the ESD and esophagectomy groups in OS (p = 0.566), RFS (p = 0.586), and DSS (p = 0.912). The ESD group showed less blood loss, shorter procedure duration and hospital stay, lower hospital cost, and fewer adverse events. However, a lower R0 resection rate was observed in the ESD group compared to the esophagectomy group. Subgroup analysis showed comparable survival outcomes between the two groups. In Cox regression analysis, age was the independent factor associated with OS.

CONCLUSION

In the treatment of SESCC, ESD showed sufficient safety and advantages. Even for pT1a-MM/pT1b SESCC, ESD may be an alternative treatment to esophagectomy.

Esophageal wall thickness on CT scans: can it predict the T stage of primary thoracic esophageal squamous cell carcinoma?

BACKGROUND

CT is the most commonly used method to stage esophageal cancer (EC). However, the reported CT T-staging criteria for EC are controversial.

PURPOSE

To determine and validate the optimal esophageal wall thickness (EWT) threshold on CT to distinguish lesions with different T stages in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

One thousand, one hundred-two consecutive patients with histopathologically confirmed ESCC between July 2014 and April 2020 were retrospectively reviewed. All patients underwent a preoperative CT examination and surgical treatment. The maximal EWT of the lesions on CT was measured. Patients were divided into pT1, pT2, pT3 and pT4 subgroups according to the pathologic stage. We employed the support vector machine, where linear kernels were leveraged to determine the optimal threshold to classify samples with different T stages. 90% of samples from each subgroup were randomly selected as the training set, while the remainder comprised the testing set.

RESULTS

The mean EWTs of the pT1, pT2, pT3 and pT4 subgroups were 4.9 ± 2.6 mm, 8.1 ± 2.3 mm, 12.4 ± 3.6 mm, and 18.6 ± 4.4 mm, respectively. Differences in the EWT between the four subgroups or between adjacent subgroups were significant (p < 0.001), and esophageal wall became thicker with increasing pT stage. We utilized MATLAB 2020a to implement the SVM model and ran the code 10 times. The accuracy of the model was 60.29 ± 2.33%. The thresholds between samples from pT1/pT2, pT2/pT3 and pT3/pT4 lesions were 5.5 ± 0.3 mm, 10.8 ± 0.8 mm and 15.9 ± 0.5 mm, respectively.

CONCLUSIONS

Possibility of predicting T stage of ESCC by EWT on CT scans was limited to 60% by model examination with large sample size.

A Population-Based Study: How to Identify High-Risk T1-2 Esophageal Cancer Patients?

Background

Due to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1-2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1-2 ESCA patients is of great significance to clinical practice.

Methods

A total of 1,747 T1-2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively.

Results

The age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1-2 ESCA patients (p < 0.05). The age, T-stage, tumor grade, and tumor size were significant factors for predicting DM of T1-2 ESCA patients (p < 0.05). The age, race, sex, histology, primary tumor site, tumor size, N-stage, M-stage, and surgery were significant factors for predicting OS of T1-2 ESCA patients (p < 0.05). The C-indexes of the three nomograms constructed by these factors were 0.737, 0.764, and 0.740, respectively, suggesting that they were clinically effective.

Conclusions

The newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1-2 ESCA patients, which contribute to the individualized decision making before clinical management.

Systematic identification of key functional modules and genes in esophageal cancer

BACKGROUND

Esophageal cancer is associated with high incidence and mortality worldwide. Differential expression genes (DEGs) and weighted gene co-expression network analysis (WGCNA) are important methods to screen the core genes as bioinformatics methods.

METHODS

The DEGs and WGCNA were combined to screen the hub genes, and pathway enrichment analyses were performed on the hub module in the WGCNA. The CCNB1 was identified as the hub gene based on the intersection between DEGs and the greenyellow module in WGCNA. Expression levels and prognostic values of CCNB1 were verified in UALCAN, GEPIA2, HCMDB, Kaplan-Meier plotter, and TIMER databases.

RESULTS

We identified 1,044 DEGs from dataset GSE20347, 1,904 from GSE29001, and 2,722 from GSE111044, and 32 modules were revealed by WGCNA. The greenyellow module was identified as the hub module in the WGCNA. CCNB1 gene was identified as the hub gene, which was upregulated in tumour tissues. Moreover, esophageal cancer patients with higher expression of CCNB1 showed a worse prognosis. However, CCNB1 'might not play an important role in immune cell infiltration.

CONCLUSIONS

Based on DEGs and key modules related to esophageal cancer, CCNB1 was identified as the hub gene, which offered novel insights into the development and treatment of esophageal cancer.