Simple Analysis of the Computed Tomography Features of Gastric Schwannoma

Preoperative differentiation of gastric schwannomas and gastrointestinal stromal tumors based on computed tomography: a retrospective multicenter observational study

Introduction

Gastric schwannoma is a rare benign tumor accounting for only 1-2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2-5 cm).

Methods

Gastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves.

Results

Logistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105-11.900), absence of necrosis (OR 4.752; 95% CI 1.464-15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499-88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042-1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080-1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900-0.973) and validation (AUC, 0.921; 95% CI, 0.830-1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value.

Conclusion

The proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

Gastric schwannoma: The gastrointestinal tumor simulator - case report and review of the literature

INTRODUCTION AND IMPORTANCE

Gastric schwannoma is a rare and benign tumor originating from the peripheral nerves of the stomach. Despite its benign nature, this tumor typically remains asymptomatic for an extended period, and its radiological and endoscopic presentation poses challenges in distinguishing it from other gastric mesenchymal tumors.

CASE PRESENTATION

Here, we present a rare case of a patient experiencing gastric pain and melena secondary to a gastric mass. The initial preoperative diagnosis indicated a gastrointestinal stromal tumor, but subsequent pathological and immunohistochemical staining of the surgical specimen confirmed the presence of gastric schwannoma.

DISCUSSION

To gain insights into this uncommon condition, we conducted an electronic search on PubMed using the keywords "gastric schwannoma" and "gastric neurinoma." Our focus centered on case series containing more than five cases of gastric localization, resulting in the analysis of 14 case series involving a total of 321 patients. Our review aims to comprehensively discuss the clinical, radiological, and therapeutic aspects associated with this rare disease.

CONCLUSION

In the absence of a definitive preoperative diagnosis, the surgical approach is considered the primary treatment for resectable gastric schwannoma, given its excellent long-term outcomes. However, further studies are imperative to better define the role of endoscopic resection in managing this condition.

Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors

OBJECTIVE

The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs.

METHOD

A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models.

RESULTS

In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05).

CONCLUSIONS

The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

Radiomics analysis of contrast-enhanced computerized tomography for differentiation of gastric schwannomas from gastric gastrointestinal stromal tumors

PURPOSE

To assess the performance of radiomics-based analysis of contrast-enhanced computerized tomography (CE-CT) images for distinguishing GS from gastric GIST.

METHODS

Forty-nine patients with GS and two hundred fifty-three with GIST were enrolled in this retrospective study. CT features were evaluated by two associate chief radiologists. Radiomics features were extracted from portal venous phase images using Pyradiomics software. A non-radiomics dataset (combination of clinical characteristics and radiologist-determined CT features) and a radiomics dataset were used to build stepwise logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models, respectively. Model performance was evaluated according to sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve, and Delong's test was applied to compare the area under the curve (AUC) between different models.

RESULTS

A total of 1223 radiomics features were extracted from portal venous phase images. After reducing dimensions by calculating Pearson correlation coefficients (PCCs), 20 radiomics features, 20 clinical characteristics + CT features were used to build the models, respectively. The AUC values for the models using radiomics features and those using clinical features were more than 0.900 for both the training and validation groups. There were no significant differences in predictive performance between the radiomic and clinical data models according to Delong's test.

CONCLUSION

A radiomics-based model applied to CE-CT images showed comparable predictive performance to senior physicians in the differentiation of GS from GIST.

Machine learning-based predictive model for the differential diagnosis of ≤ 5 cm gastric stromal tumor and gastric schwannoma based on CT images

The clinical symptoms of ≤ 5 cm gastric stromal tumor (GST) and gastric schwannoma (GS) are similar, but the treatment regimens are different. This study explored the value of computed tomography (CT) combined with machine learning (ML) algorithms to find the best model to discriminate them. A total of 126 patients with GST ≤ 5 cm and 35 patients with GS ≤ 5 during 2013-2022 were included. CT imaging features included qualitative data (tumor location, growth pattern, lobulation, surface ulcer status, necrosis, calcification, and surrounding lymph nodes) and quantitative data [long diameter (LD); short diameter (SD); LD/SD ratio; degree of enhancement (DE); heterogeneous degree (HD)]. Patients were randomly divided into a training set (n = 112) and test set (n = 49) using 7:3 stratified sampling. The univariate and multivariate logistic regression analysis were used to identify independent risk factors. Five ML algorithms were used to build prediction models: Support Vector Machine, k-Nearest Neighbor, Random Forest, Extra Trees, and Extreme Gradient Boosting Machine. The analysis identified that HDv, lobulation, and tumor growth site were independent risk factors (P < 0.05). We should focus on these three imaging features of tumors, which are relatively easy to obtain. The area under the curve for the SVM, KNN, RF, ET, and XGBoost prediction models were, respectively, 0.790, 0.895, 0.978, 0.988, and 0.946 for the training set, and were, respectively, 0.848, 0.892, 0.887, 0.912, and 0.867 for the test set. The CT combined with ML algorithms generated predictive models to improve the differential diagnosis of ≤ 5 cm GST and GS which has important clinical practical value. The Extra Trees algorithm resulted in the optimal model.

Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors

Purpose

To develop a point-based scoring system (PSS) based on contrast-enhanced computed tomography (CT) qualitative and quantitative features to differentiate gastric schwannomas (GSs) from gastrointestinal stromal tumors (GISTs).

Methods

This retrospective study included 51 consecutive GS patients and 147 GIST patients. Clinical and CT features of the tumors were collected and compared. Univariate and multivariate logistic regression analyses using the stepwise forward method were used to determine the risk factors for GSs and create a PSS. Area under the receiver operating characteristic curve (AUC) analysis was performed to evaluate the diagnostic efficiency of PSS.

Results

The CT attenuation value of tumors in venous phase images, tumor-to-spleen ratio in venous phase images, tumor location, growth pattern, and tumor surface ulceration were identified as predictors for GSs and were assigned scores based on the PSS. Within the PSS, GS prediction probability ranged from 0.60% to 100% and increased as the total risk scores increased. The AUC of PSS in differentiating GSs from GISTs was 0.915 (95% CI: 0.874-0.957) with a total cutoff score of 3.0, accuracy of 0.848, sensitivity of 0.843, and specificity of 0.850.

Conclusions

The PSS of both qualitative and quantitative CT features can provide an easy tool for radiologists to successfully differentiate GS from GIST prior to surgery.

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p < 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.

What About Gastric Schwannoma? A Review Article

PURPOSE

Gastric schwannomas (GSs) are rare mesenchymal neoplasms of the gastrointestinal tract. Diagnosis is often achieved postoperatively, based on pathology reports of retrieved specimens. The aim of the present study is to follow up all patients with gastric schwannoma (Gs) undergoing endoscopic, partial, or more extended surgery and to evaluate the appearance of local or distant recurrence.

METHODS

A PubMed, Cochrane, and Embase systematic review of the literature has been performed. Original papers, review articles, and case reports published between 1988 and 2019 were considered eligible. All the studies who met the inclusion criteria were analyzed. Statistical analysis of data has been performed using GraphPad Prism 7 software.

RESULTS

Three hundred twenty-eight articles were found, and a total of 102 were included and analyzed in depth. Fifty-three papers reported the follow-up information, ranging from 1 to 417 months across different studies. Among them, 31 patients underwent endoscopic removal of the gastric lesions; 140 patients underwent local surgery, including wedge resection or partial gastrectomy; and 148 patients underwent subtotal or total gastrectomy. The median follow-up was of 27-38-33 months, respectively. No recurrence or distant metastasis was detected in the endoscopy group. Among local surgery group, liver metastasis was reported in one case; in extended surgery group, one patient died for multiple liver metastases.

CONCLUSIONS

Local or more extended surgery involved a larger cohort of patients and reported satisfactory long-term results compared with endoscopy group. Surgery in absence of a definite preoperative diagnosis is considered the gold standard treatment for resectable Gs.

Gastric calcifying fibrous tumors: Computed tomography findings and clinical manifestations

To retrospectively analyze the computed tomography (CT) findings and clinical manifestations of gastric calcifying fibrous tumor (CFTs).The features of 7 cases with pathologically proven gastric CFTs who had undergone CT were assessed, including tumor location, contour, growth, degree of enhancement, calcification and clinical data. In addition, the size and CT value of each lesion were measured. The mean values of these CT findings and clinical data were statistically analyzed only for continuous variables.Four patients were female and three were male (mean age: 33.3 years; range: 22 ∼ 47 years). Nonspecific clinical symptoms: abdominal pain and discomfort were observed in four cases and the CFTs were incidentally detected in the other three cases. Regarding tumor markers, lower ferritin levels were observed in three female patients. All of the gastric CFTs were solitary and mainly located inside the body; they were in round or oval shape and exhibited endophytic growth. Gastric CFTs are usually small sized and could contain confluent and coarse calcifications; cyst, necrosis, ulcer, bleeding and surrounding lymphadenopathy were not found in any of the cases. Unenhanced CT values of gastric CFTs were higher than those of same-transect soft tissue. Mild-to-moderate enhancement in the arterial phase and progressive enhancement in the portal venous phase were mainly noted.A gastric mass with a high unenhanced CT attenuation value, confluent and coarse calcifications and mild-to-moderate enhancement could prompt a diagnosis of gastric CFT. In addition, (1) being young- or middle-aged, (2) having relatively low ferritin levels, and (3) tumor located in the gastric body have critical reference value for diagnosis of gastric CFT.

Clinical Characteristics and Surgical Management of Gastrointestinal Schwannomas

Objectives

Schwannomas are tumors arising from Schwan cells of the neural sheath. Gastrointestinal schwannomas (GS) are rare and easily confused with a heterogeneous group of neuroectodermal or mesenchymal neoplasms. The aim of the present study is to analyze the clinicopathological features, surgical management methods, and long-term prognoses of GS patients.

Methods

Between August 2004 and July 2019, 51 patients with GS were treated at the Peking Union Medical College Hospital. The medical records were reviewed retrospectively. A database containing demographic characteristics, clinical symptoms, imaging tests, operation details, pathological results, and prognoses was constructed and analyzed.

Results

GS accounted for 2.0% of all schwannomas. The cohort comprised 19 men (37.3%) and 32 women (62.7%). The mean age was 55.7 ± 11.4 years. The most common symptom was abdominal pain (29.4%). Twenty-seven patients (52.9%) were asymptomatic and diagnosed incidentally. The most common tumor location of GS was the stomach (90.2%). S-100 had the highest positive rate (100%) in immunohistochemical staining. Forty-six patients (90.2%) were followed-up at a mean period of 49.5 ± 41.4 months. Forty-four patients (95.7%) survived without tumor, 1 patient survived with tumor, and 1 patient died. The 5-year cumulative overall survival rate and cumulative disease-free survival rate were 97.5% and 95.2%, respectively.

Conclusion

GS are rare gastrointestinal tumors with favorable prognoses after surgical resection. Stomach is the most common site. Definitive diagnosis is determined by postoperative pathology. S-100 expression has diagnostic significance.

Laparoscopic resection of gastric schwannoma: A case report

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This is one of only 220 cases of reported cases of gastric Schwannoma.

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This is one of the very few cases of these rare tumors that has been managed with a laparoscopic approach.

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We present images of the preoperative evaluation (CT) and of the laparoscopic approach (intraoperative photos).

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The patient has over a one-year follow-up and the patient is in perfect condition with no evidence of disease.

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We accompany the case report with a thorough current review on this subject.

Introduction

Gastric schwannomas are an extremely rare presentation of mesenchymal tumors originating from Schwann cells, accounting for 0.2% of all gastric tumors. Patients are usually asymptomatic, so these tumors are frequently detected incidentally.

Presentation of case

68-year old male patient found to have a 5 cm mass in the lesser curvature of the stomach. After a careful preoperative evaluation, complete laparoscopic resection was performed. Pathology review confirmed a completely resected gastric Schwannoma. The patient’s recovery was uneventful. At a one-year follow-up he remains asymptomatic and with no evidence of disease.

Discussion

We present the uncommon case of a gastric schwannoma that was appropriately treated with a laparoscopic approach and present a current literature review focusing on diagnostic and treatment methods of these rare tumors.

Conclusion

Schwannomas should be included in the differential diagnosis of gastric tumors and can be appropriately treated with a laparoscopic approach.