Gastric poorly cohesive carcinoma: differentiation from tubular adenocarcinoma using nomograms based on CT findings in the 40 s late arterial phase

Dual-energy CT for predicting serosal invasion in gastric cancer and subtype analysis

PURPOSE

To predict the serosal invasion of gastric cancer (GC) using dual-energy CT (DECT)-based parameters and analyze the diagnostic performance according to different subtypes.

METHODS

The patients were divided into the T1-3 group and T4a group. The irregular region of interest (ROI) was manually delineated on the largest cross-section of the lesion. The ROI area, iodine concentration (IC), normalized iodine concentration (nIC), fat fraction, CT value mean, and standard deviation were measured in the late arterial (LAP) and venous phase (VP). The Mann-Whitney U test was used to assess differences between different T-stage groups and histopathological subtypes of GC. A model was established based on DECT parameters, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance.

RESULTS

Preliminary analysis showed that there were significant differences in ROI area, IC, nIC and CT value mean in VP and ROI area in LAP between T1-3 and T4a GC (all p < 0.05). The AUC of the comprehensive model composed of ROI and nIC in VP was 0.805. For different subtypes, multiple DECT parameters of poorly cohesive carcinoma (PCC) showed significant differences.

CONCLUSION

ROI area in LAP and VP, IC, nIC, and CT value mean in VP have significant differences in distinguishing between T1-3 and T4a GC. Iodine-related parameters in VP differed significantly between T1-3 and T4a in PCCs, rather than TACs. Considering the heterogeneity of different WHO subtypes, DECT iodine-related parameters in VP are more predictive of the serosal invasion status of GC compared to LAP.

Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction

PURPOSE

To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC.

METHODS

This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM).

RESULTS

T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively.

CONCLUSION

A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.

Borrmann Type IV Gastric Cancer: Computed Tomography Features and Corresponding Pathological Findings

OBJECTIVE

We aimed to analyze the association between computed tomography (CT) features and the corresponding pathological findings in Borrmann type IV (BT-4) gastric cancers and explore the pathological basis of the characteristic CT features.

METHODS

This retrospective study included 84 patients with BT-4 gastric cancers who underwent contrast-enhanced CT and surgical resection. Preoperative CT features were evaluated, including the major location, range, circumferential invasion, perigastric fat infiltration, enlarged lymph nodes, layered enhancement, degree of enhancement, and peak enhanced phase. Postoperative pathological findings were also recorded. Differences in CT features according to different World Health Organization types, surgical margin, adjacent organ invasion, and peritoneal status were assessed using the χ 2 or Fisher exact test (n < 5).

RESULTS

The most common World Health Organization type of BT-4 gastric cancer was poorly cohesive carcinoma (65.5%), which tended to show circumferential invasion, fewer enlarged lymph nodes, and layered enhancement. Although 82 patients with BT-4 gastric cancer (97.6%) had positive lymph nodes, only 26 (31.0%) had enlarged lymph nodes. Lesions originating from the gastroesophageal junction had a higher rate of positive margins ( P < 0.05). Adjacent organ invasion was more likely to occur in lesions with perigastric fat infiltration ( P < 0.05). Patients with circumferential invasion tended to show peritoneal metastasis ( P < 0.05).

CONCLUSIONS

The characteristic CT features of BT-4 gastric cancer may be attributed to the corresponding pathological findings. Recognizing the association between CT features and pathological findings may help evaluate the aggressiveness of BT-4 gastric cancers.

The role of computed tomography features in assessing response to neoadjuvant chemotherapy in locally advanced gastric cancer

OBJECTIVE

To compare the computed tomography (CT) images of patients with locally advanced gastric cancer (GC) before and after neoadjuvant chemotherapy (NAC) in order to identify CT features that could predict pathological response to NAC.

METHODS

We included patients with locally advanced GC who underwent gastrectomy after NAC from September 2016 to September 2021. We retrieved and collected the patients' clinicopathological characteristics and CT images before and after NAC. We analyzed CT features that could differentiate responders from non-responders and established a logistic regression equation based on these features.

RESULTS

We included 97 patients (69 [71.1%] men; median [range] age, 60 [26-75] years) in this study, including 66 (68.0%) responders and 31 (32.0%) non-responders. No clinicopathological variable prior to treatment was significantly associated with pathological response. Out of 16 features, three features (ratio of tumor thickness reduction, ratio of reduction of primary tumor attenuation in arterial phase, and ratio of reduction of largest lymph node attenuation in venous phase) on logistic regression analysis were used to establish a regression equation that demonstrated good discrimination performance in predicting pathological response (area under receiver operating characteristic curve 0.955; 95% CI, 0.911-0.998).

CONCLUSION

Logistic regression equation based on three CT features can help predict the pathological response of patients with locally advanced GC to NAC.

Gastric Cancer (GC) with Peritoneal Metastases (PMs): An Overview of Italian PSM Oncoteam Evidence and Study Purposes

Gastric cancer (GC) continues to be one of the leading types of malignancies worldwide, despite an ongoing decrease in incidence. It is the fifth most frequent type of cancer in the world and the fourth leading cause of cancer death. Peritoneal metastases (PMs) occur in 20-30% of cases during the natural history of the disease. Systemic chemotherapy (SC) is undoubtedly the standard of care for patients with GC and PMs. However, with the development of highly effective regimens (SC combined with intraperitoneal chemotherapy), significant tumor shrinkage has been observed in many patients with synchronous GC and PMs, allowing some to undergo curative resection "conversion surgery" with long-term survival. In recent years, there has been growing interest in intraperitoneal chemotherapy for PMs, because the reduced drug clearance associated with the peritoneal/plasma barrier allows for direct and prolonged drug exposure with less systemic toxicity. These procedures, along with other methods used for peritoneal surface malignancies (PSMs), can be used in GCs with PMs as neoadjuvant chemotherapy or adjuvant treatments after radical surgery or as palliative treatments delivered either laparoscopically or-more recently-as pressurized intraperitoneal aerosol chemotherapy. The great heterogeneity of patients with stage IV gastric cancer did not allow us to carry out a systemic review; therefore, we limited ourselves to providing readers with an overview to clarify the indications and outcomes of integrated treatments for GCs with PMs by analyzing reports from the international clinical literature and the specific experiences of our oncoteam.

Application of preoperative CT texture analysis in papillary gastric adenocarcinoma

Background

This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs.

Methods

This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis.

Results

Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p < 0.05). The diagnostic performance of CT signatures based on the AP and VP achieved AUCs of 0.873 and 0.859 in distinguishing PGCs. Multivariate models that integrated two CT signatures and age based on regression analysis and the SVM algorithm showed favorable performance in preoperatively predicting PGCs (AUC = 0.922 and 0.914, respectively).

Conclusion

CT texture analysis based multivariate models could preoperatively predict PGCs with satisfactory diagnostic efficacy.

Clinicopathological features and CT findings of papillary gastric adenocarcinoma

PURPOSE

This study aimed to analyze the clinicopathological and computed tomography (CT) findings of papillary gastric adenocarcinoma and to evaluate the feasibility of the multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas.

METHODS

This retrospective study included 22 patients with papillary gastric adenocarcinoma and 88 patients with tubular adenocarcinoma. The demographic data, tumor markers, histopathological information, CT morphological characteristics, and CT value-related parameters of all patients were collected and analyzed. The multivariate model based on regression analysis was performed to improve the diagnostic efficacy for discriminating papillary gastric adenocarcinomas preoperatively. The diagnostic performance of the established nomogram was evaluated by receiver operating characteristic curve analysis.

RESULTS

The distribution of age, carcinoembryonic antigen, differentiation degree, neural invasion, human epidermal growth factor receptor 2 overexpression, P53 mutation status, 4 CT morphological characteristics, and 10 CT valued-related parameters differed significantly between papillary gastric adenocarcinoma and tubular adenocarcinoma groups (all p < 0.05). The established multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas preoperatively achieved the area under the curve of 0.920.

CONCLUSION

There existed differences in clinicopathological features and CT findings between papillary gastric adenocarcinomas and tubular adenocarcinomas. The combination of demographic data, tumor markers, CT morphological characteristics, and CT value-related parameters could discriminate papillary gastric adenocarcinomas preoperatively with satisfactory diagnostic efficiency.