Determining the current indications for endoscopic submucosal dissection in patients with Lauren mixed-type early gastric cancer

Endoscopic Submucosal Dissection Criteria for Differentiated-type Early Gastric Cancer Are Applicable to Mixed-type Differentiated Predominant

BACKGROUND

There is a lack of sufficient evidence on whether mixed-type differentiated predominant early gastric cancer (MD-EGC) can be treated endoscopically by referring to the criteria for differentiated-type early gastric cancer (EGC). This study aims to evaluate the efficacy of endoscopic submucosal dissection (ESD) in MD-EGC.

METHODS

Patients with differentiated-type EGC treated with ESD first from January 2015 to June 2021 were reviewed, including MD-EGC and pure differentiated-type EGC (PD-EGC). Clinical data, including the clinicopathological characteristics, resection outcomes of ESD, and recurrence and survival time, were collected, and the difference between MD-EGC and PD-EGC was tested.

RESULTS

A total of 48 patients (48 lesions) with MD-EGC and 850 patients (890 lesions) with PD-EGC were included. Compared with PD-EGC, MD-EGC had a higher submucosal invasion rate (37.5% vs. 13.7%, P<0.001) and lymphatic invasion rate (10.4% vs. 0.4%, P<0.001). The rates of complete resection (70.8% vs. 92.5%, P<0.001) and curative resection (54.2% vs. 87.4%, P<0.001) in MD-EGC were lower than those of PD-EGC. Multivariate analysis revealed that MD-EGC (OR 4.26, 95% CI, 2.22-8.17, P<0.001) was an independent risk factor for noncurative resection. However, when curative resection was achieved, there was no significant difference in the rates of recurrence (P=0.424) between the 2 groups, whether local or metachronous recurrence. Similarly, the rates of survival(P=0.168) were no significant difference.

CONCLUSIONS

Despite the greater malignancy and lower endoscopic curative resection rate of MD-EGC, patients who met curative resection had a favorable long-term prognosis.

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

OBJECTIVE

To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.

METHODS

A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.

RESULTS

Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).

CONCLUSION

The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer.

CRITICAL RELEVANCE STATEMENT

The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.

A radiomics nomogram analysis based on CT images and clinical features for preoperative Lauren classification in gastric cancer

PURPOSE

To develop a combined radiomics nomogram based on computed tomography (CT) images and clinical features to preoperatively distinguish Lauren's diffuse-type gastric cancer (GC) from intestinal-type GC.

METHODS

Ninety-five patients with Lauren's intestinal or diffuse-type GC confirmed by postoperative pathology had their preoperative clinical information and dynamic contrast CT images retrospectively analyzed and were subdivided into training and test groups in a 7:3 ratio. To select the optimal features and construct the radiomic signatures, we extracted, filtered, and minimized the radiomic features from arterial phase (AP) and venous phase (VP) CT images. We constructed four models (clinical model, AP radiomics model, VP radiomics model, and radiomics-clinical model) to assess and compare their predictive performance between the intestinal- and diffuse-type GC. Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), and the DeLong test were used for assessment and comparison. In this study, radiomic nomograms integrating combined radiomic signatures and clinical characteristics were developed.

RESULTS

Compared to the AP radiomics model, the VP radiomics model had better performance, with an AUC of 0.832 (95% confidence interval [CI], 0.735, 0.929) in the training cohort and 0.760 (95% CI 0.580, 0.940) in the test cohort. Among the combined models that assessed Lauren's type GC, the model including age and VP radiomics showed the best performance, with an AUC of 0.849 (95% CI 0.758, 0.940) in the training cohort and 0.793 (95% CI 0.629, 0.957) in the test cohort.

CONCLUSIONS

Nomogram incorporating radiomic signatures and clinical features effectively differentiated Lauren's diffuse-type from intestinal-type GC.

Mixed histology poses a greater risk for noncurative endoscopic resection in early gastric cancers regardless of the predominant histologic types

OBJECTIVES

Clinicopathologic characteristics and treatment outcomes of mixed-histological-type (MT) early gastric cancers (EGCs) treated with endoscopic submucosal dissection (ESD) have not been sufficiently elucidated. We aimed to clarify them in comparison with pure-histological-type EGCs.

METHODS

We used 3022 consecutive EGCs in 2281 patients treated with ESD from our prospectively maintained database. Cases were stratified into four groups according to the final diagnosis of the resected specimen are as follows: 2780 pure differentiated-type (DT), 127 DT-predominant MT (D-MT), 87 pure undifferentiated-type (UDT), and 28 UDT-predominant MT (U-MT). Clinicopathologic characteristics and treatment outcome were compared between pure DT and D-MT, and between pure UDT and U-MT separately. Risk factors for deep submucosal invasion, lymphovascular invasion, and a final diagnosis of MT were identified using multivariate analysis.

RESULTS

Both D-MT (41.7 vs. 92.0%; P < 0.0001) and U-MT (35.7 vs. 75.9%; P = 0.0002) showed a significantly lower curative resection rate than their pure histologic counterparts. Multivariate analysis revealed that MT was an independent risk factor for deep submucosal (OR 6.55; 95% CI, 4.18-10.14) and lymphovascular (OR 4.74; 95% CI, 2.72-8.29) invasion. Preoperative biopsy results that did not show well-differentiated tubular adenocarcinoma (OR 28.2; 95% CI, 18.9-42.9) were an independent risk factor for a final diagnosis of MT.

CONCLUSIONS

MT poses a greater risk for noncurative resection regardless of the predominant histologic types, reflecting more aggressive malignant potential. Although a biopsy examination rarely shows MT, clinicians should consider the possibility of MT when a biopsy examination does not show well-differentiated tubular adenocarcinoma.

Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

PURPOSE

To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation.

MATERIALS AND METHODS

The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model.

RESULTS

In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p > 0.05).

CONCLUSION

The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

Histopathological validation of magnifying endoscopy for diagnosis of mixed-histological-type early gastric cancer

BACKGROUND

The undifferentiated-type (UDT) component profoundly affects the clinical course of early gastric cancers (EGCs). However, an accurate preoperative diagnosis of the histological types is unsatisfactory. To date, few studies have investigated whether the UDT component within mixed-histological-type (MT) EGCs can be recognized preoperatively.

AIM

To clarify the histopathological characteristics of the endoscopically-resected MT EGCs for investigating whether the UDT component could be recognized preoperatively.

METHODS

This was a single-center retrospective study. First, we attempted to clarify the histopathological characteristics of the endoscopically-resected MT EGCs with emphasis on the UDT component. Histopathological examination investigated each lesion’s UDT component: (1) Whole mucosal layer occupation of the UDT component; (2) UDT component exposure to the surface of the mucosa; and (3) existence of a clear border between the differentiated-type and UDT components. Then, preoperative endoscopic images with magnifying endoscopy with narrow-band imaging (ME-NBI) were examined to identify whether the endoscopic UDT component finding was recognizable within the area where it was present in the histopathological examination. The preoperative biopsy results and comparative relationships between endoscopic and histopathological findings were also examined.

RESULTS

In the histopathological examination, the whole mucosal layer occupation of the UDT component and exposure of the UDT component to the mucosal surface were observed in 67.3% (33/49) and 79.6% (39/49) of samples, respectively. A clear distinction of the border between the differentiated-type and UDT components could not be drawn in 65.3% (32/49) of MT lesions. In the endoscopic examination, the preoperative endoscopic images showed that only 24.5% (12/49) of MT EGCs revealed the UDT component within the area where it was present histopathologically. Histopathological UDT predominance was the single significant factor associated with the presence of the endoscopic UDT component finding (61.5% vs 11.1%, P = 0.0009). Only 26.5% (13/49) of the lesions were diagnosed from the pretreatment biopsy as having a UDT component. Combined results of the pretreatment biopsy and ME-NBI showed the preoperative presence of the UDT component in 40.8% (20/49) of MT EGCs.

CONCLUSION

Recognition of a UDT component within MT EGCs is difficult even when pretreatment biopsy and ME-NBI are combined. Endoscopic resection plays a significant role in both treatment and diagnosis.