The roles of CT and EUS in the preoperative evaluation of gastric gastrointestinal stromal tumors larger than 2 cm

Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors

BACKGROUND

Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively.

PURPOSE

To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively.

METHODS

The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed.

RESULTS

The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891).

CONCLUSION

In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.

An ultrasound based method for predicting the malignant potential of primary gastrointestinal stromal tumors preoperatively

OBJECTIVE

Gastrointestinal stromal tumors (GISTs) are difficult to identify the risk level accurately without surgical pathological confirmation. The purpose of our study was to propose a noninvasive prediction method for predicting the malignant potential of GISTs preoperatively by using contrast-enhanced ultrasound (CEUS) with gastric distention.

METHODS

We reviewed 47 GISTs who underwent CEUS from April 2017 to August 2023 retrospectively, all the lesions were certificated by pathology after surgery. The age of the patient, size of the lesion, shape, necrosis, calcification in the lesion, perfusion parameters including arrival time (AT), peak intensity (PI), time to peak (TTP), and area under the curve (AUC) of the lesion and surrounding normal tissue were analyzed. Logistic regression analyses were performed. Of the 47 GISTs, 26 were high-risk and 21 low-risk tumors respectively.

RESULTS

Compared with low-risk GISTs, high-risk GIST had faster AT (7.7s vs. 11.5s, p < 0.05), higher PI (15.2dB vs. 12.5dB, p < 0.05), and larger size (4.4 cm vs. 2.2 cm, p < 0.001). In multivariate logistic regression, AT, PI, and size were significant features. The corresponding regression equation In (p/(1-p)=-5.9 + 4.5 size + 4.6 PI + 4.0 AT).

CONCLUSION

The size, AT, and PI of the GISTs on CEUS can be used as parameters for a noninvasive risk level prediction model of GISTs. This model may help identify the different risk levels of GISTs before surgery.

Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases

OBJECTIVES

To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs).

METHODS

A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets.

RESULTS

Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images.

CONCLUSION

The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs.

CLINICAL RELEVANCE STATEMENT

GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent.

KEY POINTS

• Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.

Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumors

BACKGROUND AND STUDY AIMS

Gastrointestinal stromal tumors (GIST) carry a potential risk of malignancy, and the treatment of GIST varies for different risk levels. However, there is no systematic preoperative assessment protocol to predict the malignant potential of GIST. The aim of this study was to develop a reliable and clinically applicable preoperative nomogram prediction model to predict the malignant potential of gastric GIST.

PATIENTS AND METHODS

Patients with a pathological diagnosis of gastric GIST from January 2015 to December 2021 were screened retrospectively. Univariate and multivariate logistic analyses were used to identify independent risk factors for gastric GIST with high malignancy potential. Based on these independent risk factors, a nomogram model predicting the malignant potential of gastric GIST was developed and the model was validated in the validation group.

RESULTS

A total of 494 gastric GIST patients were included in this study and allocated to a development group (n = 345) and a validation group (n = 149). In the development group, multivariate logistic regression analysis revealed that tumor size, tumor ulceration, CT growth pattern and monocyte-to- lymphocyte ratio (MLR) were independent risk factors for gastric GIST with high malignancy potential. The AUC of the model were 0.932 (95% CI 0.890-0.974) and 0.922 (95% CI 0.868-0.977) in the development and validation groups, respectively. The best cutoff value for the development group was 0.184, and the sensitivity and specificity at this value were 0.895 and 0.875, respectively. The calibration curves indicated good agreement between predicted and actual observed outcomes, while the DCA indicated that the nomogram model had clinical application.

CONCLUSIONS

Tumor size, tumor ulceration, CT growth pattern and MLR are independent risk factors for high malignancy potential gastric GIST, and a nomogram model developed based on these factors has a high ability to predict the malignant potential of gastric GIST.

Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics

PURPOSE

This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs).

METHODS

This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test.

RESULTS

Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813).

CONCLUSION

Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs.

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.

Multi-Slice CT Features Predict Pathological Risk Classification in Gastric Stromal Tumors Larger Than 2 cm: A Retrospective Study

BACKGROUND

The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with gastric stromal tumors (GSTs). The AFIP classification is frequently used in clinical applications. But the risk classification is only available for patients who are previously untreated and received complete resection. We aimed to investigate the feasibility of multi-slice MSCT features of GSTs in predicting AFIP risk classification preoperatively.

METHODS

The clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into a low-risk group (n = 282), a moderate-risk group (n = 72), and a high-risk group (n = 70). The clinical data and MSCT features of GSTs were compared among the three groups. Those variables (p < 0.05) in the univariate analysis were included in the multivariate analysis. The nomogram was created using the rms package.

RESULTS

We found significant differences in the tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat-positive signs around GSTs, CT value in the venous phase, CT value increment in the venous phase, longest diameter, and maximum short diameter (all p < 0.05). Two nomogram models were successfully constructed to predict the risk of GSTs. Low- vs. high-risk group: the independent risk factors of high-risk GSTs included the location, ulceration, and longest diameter. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.911 (95% CI: 0.872-0.951), and the sensitivity and specificity were 80.0% and 89.0%, respectively. Moderate- vs. high-risk group: the morphology, necrosis, and feeding artery were independent risk factors of a high risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759-0.893), and the sensitivity and specificity were 85.7% and 70.8%, respectively.

CONCLUSIONS

The MSCT features of GSTs and the nomogram model have great practical value in predicting pathological AFIP risk classification between high-risk and non-high-risk groups before surgery.

Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis

BACKGROUND

Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.

AIM

To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer.

METHODS

Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed.

RESULTS

Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists' values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819.

CONCLUSION

AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.

Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors

BACKGROUND

Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs).

AIM

To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.

METHODS

The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.

RESULTS

There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively.

CONCLUSION

CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.

Comparison of Modified Cap-Assisted Endoscopic Mucosal Resection and Endoscopic Submucosal Dissection in Treating Intraluminal Gastric Gastrointestinal Stromal Tumor (≤20 mm)

INTRODUCTION

A modified cap-assisted endoscopic mucosal resection (mEMR-C), introduced in this study, was a novel variation of the standard EMR. We aimed to compare the outcomes of mEMR-C and endoscopic submucosal dissection (ESD) for the treatment of small (≤20 mm) intraluminal gastric gastrointestinal stromal tumors (gGISTs).

METHODS

This retrospective study included 43 patients who underwent mEMR-C and 156 patients who received ESD at Nanjing Drum Tower Hospital. Baseline characteristics, adverse events, and clinical outcomes were compared between the 2 groups. Univariate and multivariable analyses were conducted to adjust for confounders. After propensity score matching using sex, year, location, and tumor size, outcomes were compared with 41 patients in each group.

RESULTS

A total of 199 patients underwent endoscopic resection and the en bloc resection rate was 100%. The complete resection rate was comparable in both groups ( P = 1.000). Approximately 9.5% of all patients had a positive margin. There was no significant difference in positive margin for patients undergoing mEMR-C or ESD (9.3% vs 9.6%, P = 1.000). No difference in adverse events in both groups ( P = 0.724). The mEMR-C was associated with shorter operation time and lower cost than the ESD. Recurrence was reported in 2 patients at 1 and 5 years after ESD during a median follow-up of 62 months. No metastasis and disease-related death were identified in both groups. Propensity score matching analysis revealed similar results.

DISCUSSION

The mEMR-C was found to be the preferable technique for small (≤20 mm) intraluminal gGISTs with shorter operation time and lower cost as compared with ESD.

Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors

Background

Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine.

Objectives

We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs.

Design

This was a retrospective study with external validation.

Methods

We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts.

Results

A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively.

Conclusion

We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs.

Registration

The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).

An ileal gastrointestinal stromal tumor misdiagnosed as pelvic metastases from rectal cancer: a case report

With the advancement of imaging and pathological diagnostic methods, it is not uncommon to see synchronous gastrointestinal stromal tumors (GIST) and other primary cancers, the most common of which are synchronous gastric cancer and gastric GIST. However, synchronous advanced rectal cancer and high-risk GIST in the terminal ileum are extremely rare, and they are easily misdiagnosed as rectal cancer with pelvic metastases due to their special location near iliac vessels. Herein, we report a 55-year-old Chinese woman with rectal cancer. Preoperative imaging revealed a middle and lower rectal lesion with a right pelvic mass (considered possible metastasis from rectal cancer). Through multidisciplinary discussions, we suspected the possibility of rectal cancer synchronous with a GIST in the terminal ileum. Intraoperative exploration by laparoscopy revealed a terminal ileal mass with pelvic adhesion, a rectal mass with plasma membrane depression, and no abdominal or liver metastases. Laparoscopic radical proctectomy (DIXON) plus partial small bowel resection plus prophylactic loop ileostomy was performed, and the pathological report confirmed the coexistence of advanced rectal cancer and a high-risk ileal GIST. The patient was treated with the chemotherapy (CAPEOX regimen) plus targeted therapy(imatinib) after surgery, and no abnormalities were observed on the follow-up examination. Synchronous rectal cancer and ileal GIST are rare and easily misdiagnosed as a rectal cancer with pelvic metastases, and careful preoperative imaging analysis and prompt laparoscopic exploration are required to determine the diagnosis and prolong patient survival.

Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis

OBJECTIVES

To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification.

METHODS

A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance.

RESULTS

The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively.

CONCLUSIONS

Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.

KEY POINTS

• The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.

Prognostic value of preoperative CT features for disease-free survival in patients with primary gastric gastrointestinal stromal tumors after resection

PURPOSE

Tumor size is an important prognostic factor without consideration of the necrotic and cystic components within tumor for patients with gastrointestinal stromal tumors (GISTs). We aimed to extract the enhancing viable component from the tumor using computed tomography (CT) post-processing software and evaluate the value of preoperative CT features for predicting the disease-free survival (DFS) after curative resection for patients with primary gastric GISTs.

METHODS

132 Patients with primary gastric GISTs who underwent preoperative contrast-enhanced CT and curative resection were retrospectively analyzed. We used a certain CT attenuation of 30 HU to extract the enhancing tissue component from the tumor. Enhancing tissue volume and other CT features were assessed on venous-phase images. We evaluated the value of preoperative CT features for predicting the DFS after surgery. Univariate and multivariate Cox regression analyses were performed to find the independent risk factor for predicting the DFS.

RESULTS

Of the 132 patients, 68 were males and 64 were females, with a mean age of 61 years. The median follow-up duration was 60 months, and 28 patients experienced disease recurrence and distant metastasis during the follow-up period. Serosal invasion (p < 0.001; HR = 5.277) and enhancing tissue volume (p = 0.005; HR = 1.447) were the independent risk factors for predicting the DFS after curative resection for patients with primary gastric GISTs.

CONCLUSION

Preoperative contrast-enhanced CT could be useful for predicting the DFS after the surgery of gastric GISTs, and serosal invasion and enhancing tissue volume were the independent risk factors.

Simple Scoring Model Based on Enhanced CT in Preoperative Prediction of Biological Risk of Gastrointestinal Stromal Tumor

Objective: To construct a simple scoring model for predicting the biological risk of gastrointestinal stromal tumors based on enhanced computed tomography (CT) features. Methods: The clinicopathological and imaging data of 149 patients with primary gastrointestinal stromal tumor were retrospectively analyzed in our hospital. According to the risk classification, the patients were divided into low-risk group and high-risk group. The features of enhanced CT were observed and recorded. Univariate and multivariate logistic regression models were used to determine the predictors of high-risk biological behaviors of gastrointestinal stromal tumor, and then a simple scoring model was constructed according to the regression coefficients of each predictor. The receiver operating characteristic curve was used to evaluate the predictive ability of the model. Results: There was no significant difference between the risk classification of gastrointestinal stromal tumor with gender and age (P = .168, .320), while significant difference was found between the tumor size and location (P < .001). Univariate and multivariate logistic regression analyses showed that tumor size, enlarged vessels feeding or draining the mass, peritumoral lymph node enlargement, and venous phase contrast enhancement rate were independent predictors of the biological risk of gastrointestinal stromal tumor (P < .05). The area under the curve value of tumor size, enlarged vessels feeding or draining the mass, peritumoral lymph node enlargement, and venous phase contrast enhancement rate as the high-risk predictor of gastrointestinal stromal tumor were 0.955, 0.729, 0.680, and 0.807, respectively. Receiver operating characteristic curve results showed that the area under the curve of the scoring model constructed based on enhanced CT features was 0.941 (95% confidence interval: 0.891-0.973). When the total score was >1, the sensitivity of the scoring model in diagnosing gastrointestinal stromal tumor was 85.58%, the specificity was 88.89%, the positive predictive value was 88.51%, the negative predictive value was 86.04%, and the accuracy was 86.18%. The results of DeLong test showed that the area under the curve of the scoring model was better than that of the receiver operating characteristic curve of tumor size, enlarged vessels feeding or draining the mass, peritumoral lymph node enlargement, venous phase contrast enhancement rate, and other indicators alone in predicting the high risk of gastrointestinal stromal tumor, and the differences were statistically significant (Z = 26.510, P < .001; Z = 3.992, P < .001; Z = 6.353, P < .001; Z = 4.052, P = .013). Conclusion: The simple scoring model based on enhanced CT features is a simple and practical clinical prediction model, which is helpful to make preoperative individualized treatment plan and improve the prognosis of gastrointestinal stromal tumor patients.

The Utility of Conventional CT, CT Perfusion and Quantitative Diffusion-Weighted Imaging in Predicting the Risk Level of Gastrointestinal Stromal Tumors of the Stomach: A Prospective Comparison of Classical CT Features, CT Perfusion Values, Apparent Diffusion Coefficient and Intravoxel Incoherent Motion-Derived Parameters

Background: The role of advanced functional imaging techniques in prediction of pathological risk categories of gastrointestinal stromal tumors (GIST) is still unknown. The purpose of this study was to evaluate classical CT features, CT-perfusion and magnetic-resonance-diffusion-weighted-imaging (MR-DWI)-related parameters in predicting the metastatic risk of gastric GIST. Patients and methods: Sixty-two patients with histologically proven GIST who underwent CT perfusion and MR-DWI using multiple b-values were prospectively included. Morphological CT characteristics and CT-perfusion parameters of tumor were comparatively analyzed in the high-risk (HR) and low-risk (LR) GIST groups. Apparent diffusion coefficient (ADC) and intravoxel-incoherent-motion (IVIM)-related parameters were also analyzed in 45 and 34 patients, respectively. Results: Binary logistic regression analysis revealed that greater tumor diameter (p < 0.001), cystic structure (p < 0.001), irregular margins (p = 0.007), irregular shape (p < 0.001), disrupted mucosa (p < 0.001) and visible EFDV (p < 0.001), as well as less ADC value (p = 0.001) and shorter time-to-peak (p = 0.006), were significant predictors of HR GIST. Multivariate analysis extracted irregular shape (p = 0.006) and enlarged feeding or draining vessels (EFDV) (p = 0.017) as independent predictors of HR GIST (area under curve (AUC) of predicting model 0.869). Conclusion: Although certain classical CT imaging features remain most valuable, some functional imaging parameters may add the diagnostic value in preoperative prediction of HR gastric GIST.

Building contrast-enhanced CT-based models for preoperatively predicting malignant potential and Ki67 expression of small intestine gastrointestinal stromal tumors (GISTs)

PURPOSE

To assess contrast-enhanced computed tomography (CE-CT) features for predicting malignant potential and Ki67 in small intestinal gastrointestinal stromal tumors (GISTs) and the correlation between them.

METHODS

We retrospectively analyzed the pathological and imaging data for 123 patients (55 male/68 female, mean age: 57.2 years) with a histopathological diagnosis of small intestine GISTs who received CE-CT followed by curative surgery from May 2009 to August 2019. According to postoperatively pathological and immunohistochemical results, patients were categorized by malignant potential and the Ki67 index, respectively. CT features were analyzed to be associated with malignant potential or the Ki67 index using univariate analysis, logistic regression and receiver operating curve analysis. Then, we explored the correlation between the Ki67 index and malignant potential by using the Spearman rank correlation.

RESULTS

Based on univariate and multivariate analysis, a predictive model of malignant potential of small intestine GISTs, consisting of tumor size (p < 0.001) and presence of necrosis (p = 0.033), was developed with the area under the receiver operating curve (AUC) of 0.965 (95% CI, 0.915-0.990; p < 0.001), with 91.53% sensitivity, 96.87% specificity, 96.43% PPV, 92.54% NPV, 94.31% diagnostic accuracy. For high Ki67 expression, a model made up of tumor size (p = 0.051), presence of ulceration (p = 0.054) and metastasis (p = 0.001) may be the best predictive combination with an AUC of 0.785 (95% CI, 0.702-0.854; p < 0.001), 63.33% sensitivity, 76.34% specificity, 46.34% PPV, 86.59% NPV, 73.17% diagnostic accuracy. Ki67 index showed a moderate positive correlation with mitotic count (r = 0.578, p < 0.001), a weak positive correlation with tumor size (r = 0.339, p < 0.001) and with risk stratification (r = 0.364, p < 0.001).

CONCLUSION

Features on CE-CT could preoperatively predict malignant potential and high Ki67 expression of small intestine GISTs, and Ki67 index may be a promising prognostic factor in predicting the prognosis of small intestine GISTs, independent of the risk stratification system.

Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors

This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient’s ultrasound image using ITK-SNAP, and the radiomics features were extracted. By filtering unstable features and using Spearman’s correlation analysis, and the least absolute shrinkage and selection operator algorithm, a radiomics score was derived to predict the malignant potential of GISTs. a radiomics nomogram that combines the radiomics score and clinical ultrasound predictors was constructed and assessed in terms of calibration, discrimination, and clinical usefulness. The radiomics score from ultrasound images was significantly associated with the malignant potential of GISTs. The radiomics nomogram was superior to the clinical ultrasound nomogram and the radiomics score, and it achieved an AUC of 0.90 in the validation cohort. Based on the decision curve analysis, the radiomics nomogram was found to be more clinically significant and useful. A nomogram consisting of radiomics score and the maximum tumor diameter demonstrated the highest accuracy in the prediction of risk grade in GISTs. The outcomes of our study provide vital insights for important preoperative clinical decisions.

Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study

Purpose

This study was to investigate the diagnostic efficacy of radiomics models based on the enhanced CT images in differentiating the malignant risk of gastrointestinal stromal tumors (GIST) in comparison with the clinical indicators model and traditional CT diagnostic criteria.

Materials and methods

A total of 342 patients with GISTs confirmed histopathologically were enrolled from five medical centers. Data of patients wrom two centers comprised the training group (n=196), and data from the remaining three centers constituted the validation group (n=146). After CT image segmentation and feature extraction and selection, the arterial phase model and venous phase model were established. The maximum diameter of the tumor and internal necrosis were used to establish a clinical indicators model. The traditional CT diagnostic criteria were established for the classification of malignant potential of tumor. The performance of the four models was assessed using the receiver operating characteristics curve.

Reuslts

In the training group, the area under the curves(AUCs) of the arterial phase model, venous phase model, clinical indicators model, and traditional CT diagnostic criteria were 0.930 [95% confidence interval (CI): 0.895-0.965), 0.933 (95%CI 0.898-0.967), 0.917 (95%CI 0.872-0.961) and 0.782 (95%CI 0.717-0.848), respectively. In the validation group, the AUCs of the models were 0.960 (95%CI 0.930-0.990), 0.961 (95% CI 0.930-0.992), 0.922 (95%CI 0.884-0.960) and 0.768 (95%CI 0.692-0.844), respectively. No significant difference was detected in the AUC between the arterial phase model, venous phase model, and clinical indicators model by the DeLong test, whereas a significant difference was observed between the traditional CT diagnostic criteria and the other three models.

Conclusion

The radiomics model using the morphological features of GISTs play a significant role in tumor risk stratification and can provide a reference for clinical diagnosis and treatment plan.

Effect of endoscopic resection of gastrointestinal stromal tumors in the stomach under double-channel gastroscopy: A retrospective observational study

We aimed to investigate the safety and efficacy of endoscopic resection for the treatment of gastric gastrointestinal stromal tumors (GISTs) under single-channel gastroscopy and double-channel gastroscopy. We identified 154 patients with GISTs of the stomach who underwent endoscopic resection and were retrospectively analyzed at our hospital between May 2016 and March 2020, including 49 patients by single-channel gastroscopy and 105 patients by double-channel gastroscopy. We observed the clinical efficacy, complications, and safety of endoscopic resection of gastric GISTs, and the data were evaluated retrospectively. All patients underwent endoscopic resection successfully, without conversion to open surgery. In the single-channel gastroscopy group, 7 patients had lesions in the gastric cardia, 17 in the gastric fundus, 20 in the gastric corpus, and 5 in the gastric antrum. In the double-channel gastroscopy group, 13 patients had lesions in the gastric cardia, 34 in the gastric fundus, 46 in the gastric body, 10 in the gastric antrum, 1 in the pylorus, and 1 in the gastric angular incisure. The double-channel gastroscopy group had a shorter operation time than the single-channel gastroscopy group (59.9 ± 34.9 minutes vs 74.8 ± 26.7 minutes; P = .009 and P < .01, respectively), while they also had a lower perforation rate than the single-channel gastroscopy group (34.3% vs 51.0%; P = .048 and P < .05, respectively). No residual or recurrent lesions were discovered in any patients by gastroscopy reexamination. Both single-channel gastroscopy and double-channel gastroscopy can provide safe, effective, feasible endoscopic resection. However, double-channel gastroscopy has some distinct advantages in endoscopic resection.

Gastrointestinal stromal tumors: a comprehensive radiological review

Gastrointestinal stromal tumors (GISTs) originating from the interstitial cells of Cajal in the muscularis propria are the most common mesenchymal tumor of the gastrointestinal tract. Multiple modalities, including computed tomography (CT), magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography, ultrasonography, digital subtraction angiography, and endoscopy, have been performed to evaluate GISTs. CT is most frequently used for diagnosis, staging, surveillance, and response monitoring during molecularly targeted therapy in clinical practice. The diagnosis of GISTs is sometimes challenging because of the diverse imaging findings, such as anatomical location (esophagus, stomach, duodenum, small bowel, colorectum, appendix, and peritoneum), growth pattern, and enhancement pattern as well as the presence of necrosis, calcification, ulceration, early venous return, and metastasis. Imaging findings of GISTs treated with antineoplastic agents are quite different from those of other neoplasms (e.g. adenocarcinomas) because only subtle changes in size are seen even in responsive lesions. Furthermore, the recurrence pattern of GISTs is different from that of other neoplasms. This review discusses the advantages and disadvantages of each imaging modality, describes imaging findings obtained before and after treatment, presents a few cases of complicated GISTs, and discusses recent investigations performed using CT and MRI to predict histological risk grade, gene mutations, and patient outcomes.

Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study

RATIONALE AND OBJECTIVES

Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs).

MATERIALS AND METHODS

A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve.

RESULTS

A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical.

CONCLUSION

The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.

Preoperative prediction of malignant potential of 2-5 cm gastric gastrointestinal stromal tumors by computerized tomography-based radiomics

BACKGROUND

The use of endoscopic surgery for treating gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs.

AIM

To develop and evaluate computerized tomography (CT)-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs.

METHODS

A total of 103 patients with pathologically confirmed gastric GISTs between 2 and 5 cm were enrolled. The malignant potential was categorized into low grade and high grade according to postoperative pathology results. Preoperative CT images were reviewed by two radiologists. A radiological model was constructed by CT findings and clinical characteristics using logistic regression. Radiomic features were extracted from preoperative contrast-enhanced CT images in the arterial phase. The XGboost method was used to construct a radiomics model for the prediction of malignant potential. Nomogram was established by combing the radiomics score with CT findings. All of the models were developed in a training group (n = 69) and evaluated in a test group (n = 34).

RESULTS

The area under the curve (AUC) value of the radiological, radiomics, and nomogram models was 0.753 (95% confidence interval [CI]: 0.597-0.909), 0.919 (95%CI: 0.828-1.000), and 0.916 (95%CI: 0.801-1.000) in the training group vs 0.642 (95%CI: 0.379-0.870), 0.881 (95%CI: 0.772-0.990), and 0.894 (95%CI: 0.773-1.000) in the test group, respectively. The AUC of the nomogram model was significantly larger than that of the radiological model in both the training group (Z = 2.795, P = 0.0052) and test group (Z = 2.785, P = 0.0054). The decision curve of analysis showed that the nomogram model produced increased benefit across the entire risk threshold range.

CONCLUSION

Radiomics may be an effective tool to predict the malignant potential of 2-5 cm gastric GISTs and assist preoperative clinical decision making.

Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors

OBJECTIVES

To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs).

MATERIALS AND METHODS

We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected. All patients were divided into low-, moderate-, and high-risk groups according to the modified NIH classification criteria. All indicators were analyzed by univariate analysis. The indicators with inter-group differences were used to establish regression and decision tree models to predict the biological risk of GISTs.

RESULTS

There were statistically significant differences in long diameter, S/L ratio, internal echo level, echo homogeneity, boundary, shape, necrotic cystic degeneration, and blood flow signals among the low-, moderate-, and high-risk groups (all p < .05). The logistic regression model based on the echo homogeneity, shape, necrotic cystic degeneration and blood flow signals had an accuracy rate of 76.96% for predicting the biological risk, which was higher than the 72.77% of the decision tree model (based on the long diameter, the location of tumor origin, echo homogeneity, shape, and internal echo) (p = .008). In the low-risk and high-risk groups, the predicting accuracy rates of the regression model reached 87.34 and 81.82%, respectively.

CONCLUSIONS

Transabdominal ultrasound is highly valuable in predicting the biological risk of GISTs. The logistic regression model has greater predictive value than the decision tree model.

Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction

BACKGROUND

Gastric cancer is one of the leading causes of cancer death in the world. Improving gastric cancer survival prediction can enhance patient prognostication and treatment planning.

METHODS

In this study, we performed gastric cancer survival prediction using machine learning and multi-modal data of 1061 patients, including 743 for model learning and 318 independent patients for evaluation. A Cox proportional-hazard model was trained to integrate clinical variables and CT imaging features (extracted by radiomics and deep learning) for overall and progression-free survival prediction. We further analyzed the prediction effects of clinical, radiomics, and deep learning features. Concordance index (c-index) was used as the model performance metric, and the predictive effects of multi-modal features were measured by hazard ratios (HRs) at pre- and post-operative settings.

RESULTS

Among 318 patients in the independent testing group, the hazard predicted by Cox from multi-modal features is associated with their survival. The highest c-index was 0.783 (95% CI, 0.782-0.783) and 0.770 (95% CI, 0.769-0.771) for overall and progression-free survival prediction, respectively. The post-operative variables are significantly (p<0.001) more predictive than the pre-operative variables. Pathological tumor stage (HR=1.336 [overall survival]/1.768 [progression-free survival], p<0.005), pathological lymph node stage (HR=1.665/1.433, p<0.005), carcinoembryonic antigen (CEA) (HR=1.632/1.522, p=0.02), chemotherapy treatment (HR=0.254/0.287, p<0.005), radiomics signature [HR=1.540/1.310, p<0.005], and deep learning signature [HR=1.950/1.420, p<0.005]) are significant survival predictors.

CONCLUSION

Our study showed that CT radiomics and deep learning imaging features are significant pre-operative predictors, providing additional prognostic information to the pathological staging markers. Lower CEA levels and chemotherapy treatments also increase survival chances. These findings can enhance gastric cancer patient prognostication and inform treatment planning.

Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors

BACKGROUND AND AIMS

This study aimed to investigate whether AI via a deep learning algorithm using endoscopic ultrasonography (EUS) images could predict the malignant potential of gastric gastrointestinal stromal tumors (GISTs).

METHODS

A series of patients who underwent EUS before surgical resection for gastric GISTs were included. A total of 685 images of GISTs from 55 retrospectively included patients were used as the training data set for the AI system. Convolutional neural networks were constructed to build a deep learning model. After applying the synthetic minority oversampling technique, 70% of the generated images were used for AI training and 30% were used to test AI diagnoses. Next, validation was performed using 153 EUS images of 15 patients with GISTs. In addition, conventional EUS features of 55 patients in the training cohort were evaluated to predict the malignant potential of GISTs and mitotic index.

RESULTS

The overall sensitivity, specificity, and accuracy of the AI system for predicting malignancy risk were 83%, 94%, and 82% in the training dataset, and 75%, 73%, and 66% in the validation cohort, respectively. When patients were divided into low-risk and high-risk groups, sensitivity, specificity, and accuracy increased to 99% in the training dataset and 99.7%, 99.7%, and 99.6%, respectively, in the validation cohort. No conventional EUS features were found to be associated with either malignant potential or mitotic index (P > 0.05).

CONCLUSIONS

AI via a deep learning algorithm using EUS images could predict the malignant potential of gastric GISTs with high accuracy.

Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors

PURPOSE

To investigate the use of the combined model based on clinical and enhanced CT texture features for predicting the liver metastasis of high-risk gastrointestinal stromal tumors (GISTs).

METHODS

This retrospective study was conducted including 204 patients with pathologically confirmed high-risk GISTs from the Zhejiang Cancer Hospital from January 2015 to June 2021, and 76 cases of them were diagnosed with simultaneous liver metastasis. We randomly divided the cohort into a training cohort (n = 142) and a validation cohort (n = 62) with a ratio of 7:3. All volumes of interest (VOIs) of the high-risk GISTs were manually segmented on the portal venous phase CT images using the ITK-SNAP software. The least absolute shrinkage and selection operator (Lasso) algorithm was performed to determine the most valuable features from a total of 110 texture features extracted by the A-K software to reflect the texture information of the given VOIs. Texture-based predictive model was built from the selected texture features. Independent clinical risk factors were identified through univariate logistic analysis. Then, the texture-based model incorporated the clinical predictors to develop a combined model by multivariate logistic regression. Receiver operating characteristic curve, calibration curve, and decision curve analysis were utilized to analyze the discrimination capacity and clinical application value of the predictive models.

RESULTS

The nine optimal texture features were remained after the reduction of dimension using Lasso method. Another four clinical parameters (BMI, location, gastrointestinal bleeding, and CA125 level) were included in the clinical-based predictive model. Finally, with the combination of remaining texture and clinical features, a multivariate logistic regression classifier was built to predict the liver metastasis potential of high-risk GISTs. The remarkable classification performance of the combined model for the prediction of liver metastasis in the subjects with high-risk GISTs was obtained with area under curve (AUC) = 0.919, sensitivity = 83.9%, specificity = 89.7%, and accuracy = 84.9% in our validation group.

CONCLUSION

The texture-based radiomic signature derived from the portal venous phase CT images could predict liver metastasis of high-risk GISTs in a non-invasive way. Integrating additional clinical variables into the model further leads to an improvement of liver metastasis risk prediction.

A CT-based nomogram for predicting the malignant potential of primary gastric gastrointestinal stromal tumors preoperatively

PURPOSE

To develop and validate a computerized tomography (CT)-based nomogram for predicting the malignant potential of primary gastric gastrointestinal stromal tumors (GISTs).

METHODS

The primary and validation cohorts consisted of 167 and 39 patients (single center, different time periods) with histologically confirmed primary gastric GISTs. Clinical data and preoperative CT images were reviewed. The association of CT characteristics with malignant potential was analyzed using univariate and stepwise logistic regression analyses. A nomogram based on significant CT findings was developed for predicting malignant potential. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curves. External validation was performed with the validation cohort.

RESULTS

CT imaging features including tumor size, tumor location, tumor necrosis, growth pattern, ulceration, enlarged vessels feeding or draining the mass (EVFDM), tumor contour, mesenteric fat infiltration, and direct organ invasion showed significant differences between the low- and high-grade malignant potential groups in univariate analysis (P < 0.05). Only tumor size (> 5 cm vs ≤ 5 cm), location (cardiac/pericardial region vs other), EVFDM, and mesenteric fat infiltration (present vs absent) were significantly associated with high malignant potential in multivariate logistic regression analysis. Incorporating these four independent factors into the nomogram model achieved good C-indexes of 0.946 (95% confidence interval [CI] 0.899-0.975) and 0.952 (95% CI 0.913-0.977) in the primary and validation cohorts, respectively. The cutoff point was 0.33, with sensitivity, specificity, and diagnostic accuracy of 0.865, 0.915, and 0.780, respectively.

DISCUSSION

Primary gastric GISTs originating in the cardiac/pericardial region appear to be associated with higher malignant potential. The nomogram consisting of CT features, including size, location, EVFDM, and mesenteric fat infiltration, could be used to accurately predict the high malignant potential of primary gastric GISTs.

Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors

To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ² value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.

A preoperative risk prediction model for high malignancy potential gastrointestinal stromal tumors of the stomach

BACKGROUND

Gastric gastrointestinal stromal tumors (GISTs) exhibit various degrees of aggression and malignant potential. However, no systematic preoperative evaluation strategy to predict the malignancy potential of gastric GISTs has yet been developed. This study aimed to develop a reliable and easy-to-use preoperative risk-scoring model for predicting high malignancy potential (HMP) gastric GISTs.

METHODS

The data of 542 patients with pathologically confirmed gastric GISTs who underwent resection were reviewed. Multivariate logistic regression analysis was used to identify significant predictors of HMP. The risk-scoring system (RSS) was based on the predictive factors for HMP, and its performance was validated using a split-sample approach.

RESULTS

A total of 239 of 542 (44.1%) surgically resected gastric GISTs had HMP. Multivariate analysis demonstrated that tumor size, location, and surface changes were independent risk factors for HMP. Based on the accordant regression coefficients, the presence of surface ulceration was assigned 1 point. Tumor sizes of 4-6 cm and > 6 cm were assigned 2 and 5 points, respectively. Two points were assigned to cardia or fundus locations. A score of 3 points was the optimal cut-off value for HMP prediction. HMP were found in 19.8% and 82.7% of the low and high-risk groups of the RSS, respectively. The area under the receiver-operating characteristic curve for predicting HMP was 0.81 (95% confidence interval (CI) 0.75-0.86). Discrimination was good after validation (0.75, 95% CI 0.69-0.81).

CONCLUSION

This simple RSS could be useful for predicting the malignancy potential of gastric GISTs and may aid preoperative clinical decision making to ensure optimal treatment.

Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors

OBJECTIVES

To investigate the correlation between CT imaging features and risk stratification of gastrointestinal stromal tumors (GISTs), prediction of mutation status, and prognosis.

METHODS

This retrospective dual-institution study included patients with pathologically proven GISTs meeting the following criteria: (i) preoperative contrast-enhanced CT performed between 2008 and 2019; (ii) no treatments before imaging; (iii) available pathological analysis. Tumor risk stratification was determined according to the National Institutes of Health (NIH) 2008 criteria. Two readers evaluated the CT features, including enhancement patterns and tumor characteristics in a blinded fashion. The differences in distribution of CT features were assessed using univariate and multivariate analyses. Survival analyses were performed by using the Cox proportional hazard model, Kaplan-Meier method, and log-rank test.

RESULTS

The final population included 88 patients (59 men and 29 women, mean age 60.5 ± 11.1 years) with 45 high-risk and 43 low-to-intermediate-risk GISTs (median size 6.3 cm). At multivariate analysis, lesion size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) were independently associated with the high-risk GISTs. Hyperenhancement was significantly more frequent in PDGFRα-mutated/wild-type GISTs compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). Ill-defined margins were associated with shorter progression-free survival (HR 9.66) at multivariate analysis, while ill-defined margins and hemorrhage remained independently associated with shorter overall survival (HR 44.41 and HR 30.22). Inter-reader agreement ranged from fair to almost perfect (k: 0.32-0.93).

CONCLUSIONS

Morphologic contrast-enhanced CT features are significantly different depending on the risk status or mutations and may help to predict prognosis.

KEY POINTS

• Lesions size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) are independent predictors of high-risk GISTs. • PDGFRα-mutated/wild-type GISTs demonstrate more frequently hyperenhancement compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). • Ill-defined margins (hazard ratio 9.66) were associated with shorter progression-free survival at multivariate analysis, while ill-defined margins (hazard ratio 44.41) and intralesional hemorrhage (hazard ratio 30.22) were independently associated with shorter overall survival.

Preoperative imaging of gastric GISTs underestimates pathologic tumor size: A retrospective, single institution analysis

BACKGROUND

How well imaging size agrees with pathologic size of gastric gastrointestinal stromal tumors (GISTs) is unknown. GIST risk stratification is based on pathologic size, location, and mitotic rate. To inform decision making, the size discrepancy between imaging and pathology for gastric GISTs was investigated.

METHODS

Imaging and pathology reports were reviewed for 113 patients. Bland-Altman analyses and intraclass correlation (ICC) assessed agreement of imaging and pathology. Changes in clinical risk category due to size discrepancy were identified.

RESULTS

Computed tomography (CT) (n = 110) and endoscopic ultrasound (EUS) (n = 50) underestimated pathologic size for gastric GISTs by 0.42 cm, 95% confidence interval (CI): (0.11, 0.73), p = 0.008 and 0.54 cm, 95% CI: (0.25, 0.82), p < 0.001, respectively. ICCs were 0.94 and 0.88 for CT and EUS, respectively. For GISTs ≤ 3 cm, size underestimation was 0.24 cm for CT (n = 28), 95% CI: (0.01, 0.47), p = 0.039 and 0.56 cm for EUS (n = 26), 95% CI: (0.27, 0.84), p < 0.0001. ICCs were 0.72 and 0.55 for CT and EUS, respectively. Spearman's correlation was ≥0.84 for all groups. For GISTs ≤ 3 cm, 6/28 (21.4% p = 0.01) on CT and 7/26 (26.9% p = 0.005) on EUS upgraded risk category using pathologic size versus imaging size. No GISTs ≤ 3 cm downgraded risk categories. Size underestimation persisted for GISTs ≤ 2 cm on EUS (0.39 cm, 95% CI: [0.06, 0.72], p = 0.02, post hoc analysis).

CONCLUSION

Imaging, particularly EUS, underestimates gastric GIST size. Caution should be exercised using imaging alone to risk-stratify gastric GISTs, and to decide between surveillance versus surgery.

Correlation of Endoscopic Ultrasonography Features with the Mitotic Index in 2- to 5-cm Gastric Gastrointestinal Stromal Tumors

BACKGROUND

Predicting the malignancy potential of gastrointestinal stromal tumor (GIST) before resection could improve patient management strategies as gastric GISTs with a low malignancy potential can be safely treated endoscopically, but surgical resection is required for those tumors with a high malignancy potential. This study aimed to evaluate endoscopic ultrasound (EUS) features of 2- to 5-cm gastric GISTs that might be used to predict their mitotic index using surgical specimens as the gold standard.

PATIENTS AND METHODS

Forty-nine patients (30 females and 19 males; mean age 55.1 ± 12.7 years) who underwent EUS examinations, followed by surgical resections of 2- to 5-cm gastric GISTs, were retrospectively reviewed.

RESULTS

The mean tumor size was 3.44 ± 0.97 (range 2.1-5.0) cm. A univariate analysis revealed no significant differences in age, sex, and tumor location in the low mitotic index and high mitotic index groups (all p > 0.05). In terms of EUS features, there were no significant differences in the mitotic indexes with respect to the shape, surface lobulation, border regularity, echogenicity, homogeneity, growth patterns, presence of mucosal ulceration, hyperechogenic foci, anechoic spaces, and hypoechoic halos (all p > 0.05). However, the tumor size was larger in the high mitotic index group than that in the low mitotic index group (3.97 ± 1.05 vs. 3.27 ± 0.9 cm, p = 0.03).

CONCLUSION

Conventional EUS features are not reliable for predicting the mitotic index of 2- to 5-cm gastric GISTs. Further modalities for predicting the mitotic index are needed to prevent unnecessary surgical resections in patients with a low risk of malignancy.

Preoperative CT feature of incomplete overlying enhancing mucosa as a high-risk predictor in gastrointestinal stromal tumors of the stomach

OBJECTIVES

To determine whether the CT finding of overlying enhancing gastric mucosa (OEGM) can be used to predict risk stratifications by observing CT features of gastrointestinal stromal tumors (GISTs) of the stomach.

METHODS

Clinical characteristics and CT features within pathologically demonstrated GISTs were retrospectively reviewed. Risk stratifications were classified into non-high group and high-risk group according to the modified National Institutes of Health criteria. Univariate analysis and multivariate logistic regression analysis were performed in order to determine significant predictors for high-risk stratification. Receiver operating characteristic (ROC) curve analysis, subgroup analysis, and pathologic-radiologic correlation analysis were all executed.

RESULTS

A total of 147 patients were finally enrolled as test subjects. Within the univariate analysis, high-risk tumors tended to have a larger diameter, irregular shape, exophytic growth pattern, present necrosis, incomplete OEGM, tumor vessels, heterogeneous enhancement, and present rupture. According to ROC curve analysis, incomplete OEGM showed the largest area under curve values for diagnosing lesions (0.835; 95% CI, 0.766-0.904; p < 0.001). Multivariate analysis showed that the incomplete OEGM was the strongest independent predictor for high-risk stratification of gastric GISTs (OR = 21.944; 95% CI, 4.344-110.863; p < 0.001). Within the subgroup analysis, incomplete OEGM was more frequently associated with tumors size > 10 cm, irregular shape, exophytic growth pattern, high mitotic count, and disrupted mucosa on pathology.

CONCLUSIONS

The CT feature of incomplete OEGM is an independent predictive factor for high-risk stratification of gastric GISTs and strongly correlated with pathological mucosal changes.

KEY POINTS

• Preoperative CT features can be helpful in assessment of risk stratifications of gastric GISTs. • OEGM is an independent predictor for high-risk stratification of gastric GISTs. • Incomplete OEGM likely indicates high-risk stratification of gastric GISTs.

Gastrointestinal stromal tumors (GIST): a proposal of a "CT-based predictive model of Miettinen index" in predicting the risk of malignancy

PURPOSE

To identify the predictors of malignancy on CT for the evaluation of gastrointestinal stromal tumors (GIST) by correlating CT findings with the mitotic index in order to propose a "CT-based predictive model of Miettinen index."

METHODS

One radiologist and one resident in radiology with 14- and 4-year experience in oncological field reviewed the CT findings of 42 patients by consensus, with respect to lesion site, size, contour, tumor growth pattern, enhancing pattern, degree of enhancement of tumor, percentage of tumor necrosis, mesenteric fat infiltration, ulceration, calcification, regional lymphadenopathy, direct invasion to adjacent organs, and distant metastasis. All parameters were correlated with the mitotic index evaluated at histopathological analysis following surgery. Normality of variables was evaluated using Shapiro-Wilk test. Pearson's correlation test was used to assess the interaction between variables. The diagnostic accuracy percentage of tumor necrosis was measured by receiver operating characteristic (ROC) analysis for detecting whether the number of mitosis per 50 high-power fields was > 5.

RESULTS

A significant statistical correlation was found between percentage of tumor necrosis and the mitotic index (p < 0.005), dimension, and location of the tumor.

CONCLUSION

CT could be an accurate technique in the prediction of malignancy of GIST in a CT risk assessment system, based on the location of the tumor, its size, and the percentage of tumor necrosis.

Endoscopic Submucosal Dissection of Gastrointestinal Stromal Tumours: A Retrospective Cohort Study

Background

Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal neoplasms. Endoscopic submucosal dissection (ESD) has been used to remove submucosal tumours for many years. However, whether ESD can be recommended for the treatment of GISTs is still controversial. Therefore, we evaluated the efficacy and safety of ESD for treating GISTs.

Patients and Methods

We retrospectively analysed 75 GIST patients who underwent ESD in our hospital from January 2016 to December 2018, and the demographic data, clinical presentation of tumours, operative parameters, postoperative complications and length of hospital stay were analysed.

Results

Seventy-five patients successfully underwent en bloc resection, and 74 (98.7%) patients underwent complete resection of the lesions, with an average tumour size of 1.7 cm (range 0.3–6.0 cm). The median operation time was 84.8 min (range 20–180 min). Forty-two (56.0%) patients underwent endoscopic purse-string suture with no conversions to an open operation. The median postoperative length of hospitalization was 6.6 days (range 3–14 days). Out of a total of 75 GIST patients, 48 (64.0%) were considered very low risk, 19 (25.3%) were low risk, 5 (6.7%) were mild risk, and 3 (4.0%) were high risk. The median follow-up was 24.0 months (range 6–45 months). During hospitalization and follow-up, no complications, recurrence or metastasis occurred.

Conclusion

Based on our study from a medical centre, ESD is a safe and effective method for treating GISTs. However, further studies are needed.

A multivariate prediction model for high malignancy potential gastric GI stromal tumors before endoscopic resection

BACKGROUND AND AIMS

Endoscopic resection is becoming an option in the management of gastric GI stromal tumors (GISTs). Although no consensus has been reached, patients with high malignancy potential GISTs are generally considered to be surgical candidates. However, no systematic preoperative evaluation strategy has yet been developed. The current study was performed to develop a preoperative multivariate model to predict the malignant potential of gastric GISTs.

METHODS

This study consisted of 2 stages. First, a multivariate prediction model for gastric GISTs smaller than 5 cm was developed using a multivariate logistic regression analysis in a retrospective cohort. Next, the prediction model was validated further in a validation cohort of gastric GISTs.

RESULTS

In the developing stage, 275 patients were included. The multivariate analysis demonstrated that independent risk factors for high malignancy potential gastric GISTs smaller than 5 cm were tumor size ≥2 cm (according to cutoff value), an irregular tumor shape, and mucosal ulceration (P < .05). Based on accordant regression coefficients, 3 risk factors were weighted with point values: 1 point for mucosal ulceration, 2 points for an irregular tumor shape, and 3 points for tumor size ≥2 cm. In the validation stage, 186 patients were included. The area under the curve of the prediction model was .80 (95% confidence interval, .73-.85), which was significantly higher than that of tumor size alone (P = .034).

CONCLUSIONS

The independent risk factors for high malignancy potential gastric GISTs smaller than 5 cm were tumor size larger than 2 cm, an irregular tumor shape, and mucosal ulceration. These factors could be used to predict malignancy potential of gastric GISTs in a simple combination.

Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors

Background

Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are treated varyingly according to the malignancy. The purpose of this study is to develop and validate a nomogram for preoperative prediction of the malignant potential in patients with GIST.

Methods

A total of 440 patients with pathologically confirmed GIST after surgery in our hospital from January 2011 to July 2019 were retrospectively analyzed. They were randomly divided into the training set (n = 308) and validation set (n = 132). CT signs and texture features of each patient were analyzed and predictive model were developed using the least absolute shrinkage and selection operator (lasso) regression. Then a nomogram based on selected parameters was developed. The predictive effectiveness of nomogram was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). Concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram by bootstrapping based on internal (training set) and external (validation set) validity. The clinical application value of the nomogram was determined through the decision curve analysis (DCA).

Results

Totally 156 GIST patients with low-malignant (very low and low risk) and 284 ones with high-malignant potential (intermediate and high risk) are enrolled in this study. The prediction nomogram consisting of size, cystoid variation and meanValue had an excellent discrimination both in training and validation sets (AUCs (95% confidence interval(CI)): 0.935 (0.908, 0.961), 0.933 (0.892, 0.974); C-indices (95% CI): 0.941 (0.912, 0.956), 0.935 (0.901, 0.982); sensitivity: 81.4, 90.6%; specificity: 75.0, 75.7%; accuracy: 88.0, 88.6%, respectively). The calibration curves indicated a good consistency between the actual observation and nomogram prediction for differentiating GIST malignancy. Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusion

This study presents a prediction nomogram that incorporates the CT signs and texture parameter, which can be conveniently used to facilitate the preoperative individualized prediction of malignancy in GIST patients.

Risk stratification in GIST: shape quantification with CT is a predictive factor

Background

Tumor shape is strongly associated with some tumor’s genomic subtypes and patient outcomes. Our purpose is to find the relationship between risk stratification and the shape of GISTs.

Methods

A total of 101 patients with primary GISTs were confirmed by pathology and immunohistochemistry and underwent enhanced CT examination. All lesions’ pathologic sizes were 1 to 10 cm. Points A and B were the extremities of the longest diameter (LD) of the tumor and points C and D the extremities of the small axis, which was the longest diameter perpendicular to AB. The four angles of the quadrangle ABCD were measured and each angle named by its summit (A, B, C, D). For regular lesions, we took angles A and B as big angle (BiA) and small angle (SmA). For irregular lesions, we compared A/B ratio and D/C ratio and selected the larger ratio for analysis. The chi-square test, t test, ROC analysis, and hierarchical or binary logistic regression analysis were used to analyze the data.

Results

The BiA/SmA ratio was an independent predictor for risk level of GISTs (p = 0.019). With threshold of BiA at 90.5°, BiA/SmA ratio at 1.35 and LD at 6.15 cm, the sensitivities for high-risk GISTs were 82.4%, 85.3%, and 83.8%, respectively; the specificities were 87.1%, 71%, and 77.4%, respectively; and the AUCs were 0.852, 0.818, and 0.844, respectively. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could (p < 0.05). Shape and Ki-67 were independent predictors of the mitotic value (p = 0.036 and p < 0.001, respectively), and the accuracy was 87.8%.

Conclusions

Quantifying tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs, especially for high-risk grading and mitotic value > 5/50HPF.

Key Points

• The BiA/SmA ratio was an independent predictor affecting the risk level of GISTs. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could.

• Shape and Ki-67 were independent predictors of the mitotic value.

• The method for quantifying the tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs.

Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging

The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.

Gastric gastrointestinal stromal tumours (2-5 cm): Correlation of CT features with malignancy and differential diagnosis

PURPOSE

The purpose of this study was to investigate the computed tomography (CT) features of 2-5 cm gastric gastrointestinal stromal tumors (GISTs), schwannomas and leimyomas which have close relationship with malignancy evaluation and differential diagnosis.

METHOD

Seventy-six patients with pathologically confirmed gastric submucosal tumors (SMTs) between 2-5 cm were included in this study, including 60 GISTs, 10 schwannomas and 6 leiomyomas. CT imaging features were reviewed and quantitative parameters including CT values during nonenhanced phase (CTV-N), portal phase (CTV-P) and delayed phase (CTV-D) were recorded. The association of CT features with mitotic counts of GISTs and the significantly different CT features between GISTs and benign SMTs were analyzed.

RESULTS

The lobulated contour was significantly more common in GISTs with high mitoses (P < 0.05). The value of CTV-D/CTV-P was significantly lower in GISTs with high mitoses (P < 0.05) and it was an independent predictor for high-mitosis GISTs (P = 0.049, odds ratio [OR] = 186.445) with an area under the curve (AUC) of 0.722. CT features including heterogeneous enhancement and presence of necrosis or cystic degeneration were significantly suggestive of GISTs instead of benign SMTs (P < 0.05). The value of CTV-D/CTV-P was significantly higher in schwannomas than that in GISTs (P < 0.05) with an AUC of 0.853. The value of CTV-P/CTV-N was significantly lower in leiomyomas than that in GISTs (P < 0.05) with an AUC of 0.883.

CONCLUSIONS

Some qualitative and quantitative parameters on contrast-enhanced CT can be helpful in preoperative diagnosis and risk stratification of 2-5 cm gastric GISTs.

Multiparametric MRI and 18F-FDG PET features for differentiating gastrointestinal stromal tumors from benign gastric subepithelial lesions

OBJECTIVES

To investigate whether multiparametric magnetic resonance imaging (MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography (PET) can be helpful in differentiating gastrointestinal stromal tumors (GISTs) from non-GISTs and high-risk GISTs from low-risk GISTs.

METHODS

This retrospective study included 56 patients with pathologically confirmed GISTs (n = 39), leiomyoma (n = 8), schwannoma (n = 5), heterotopic pancreas (n = 3), and glomus tumor (n = 1) who underwent MRI and/or PET examinations. Two radiologists reviewed MRI regarding location, shape, contour, growth pattern, margin, signal intensity (SI) on T1- (T1WI) and T2-weighted images (T2WI), degree and pattern of enhancement, hemorrhage, and necrosis. Mean apparent diffusion coefficient (ADC) and maximum standardized uptake value (SUV_max) were measured. Imaging features were compared among non-GISTs, low-risk GISTs, and high-risk GISTs using uni- and multivariate statistical analyses.

RESULTS

Size, longitudinal location, shape, contour, growth pattern, SI on T1- and T2WI, enhancement pattern, hemorrhage, necrosis, ADC, and SUV_max were significantly different among non-GISTs, low-risk GISTs, and high-risk GISTs (p < 0.05). On multivariate analysis, SI on T2WI (hazard ratio [HR], 66.0; p = 0.002) was the only independent variable for differentiating GISTs from non-GISTs whereas enhancement pattern (HR, 56.0; p = 0.041), ADC (HR, 0.997; p = 0.01), and SUV_max (HR, 2.08; p = 0.027) were significant features for differentiating between high-risk and low-risk GISTs.

CONCLUSIONS

Several qualitative and quantitative MRI and PET features including ADC and SUV_max values are significantly different among non-GISTs, low-risk GISTs, and high-risk GISTs. Multiparametric information obtained from MRI with or without PET can be useful for differentiation of gastric subepithelial tumors as well as for determining patients' management and prognosis.

KEY POINTS

• Several qualitative MRI features are helpful in distinguishing gastrointestinal stromal tumors (GISTs) from non-GISTs as well as high-risk GISTs from low-risk GISTs. • Apparent diffusion coefficient value on diffusion-weighted imaging can be useful in distinguishing GISTs from non-GISTs as well as high-risk GISTs from low-risk GISTs. • PET has the potential to distinguish between high-risk and low-risk GISTs.

Endoscopic Removal of Gastrointestinal Stromal Tumors in the Stomach: A Single-Center Experience

BACKGROUND AND AIMS

Endoscopic removal of GISTs (gastrointestinal stromal tumors) is recently recognized, but less is known about its efficacy and safety. This study is aimed at assessing the feasibility, clinical efficacy, and safety of the endoscopic removal of gastric GISTs.

PATIENTS AND METHODS

Endoscopic removal (ER) of GISTs was performed in 134 patients at our hospital between January 2015 and January 2019. The clinical features, surgical outcomes, complications, pathological diagnosis, and risk classification were evaluated retrospectively.

RESULTS

ER was successful in 131 cases (98%), including 58 by ESD (endoscopic submucosal dissection), 43 by ESE (endoscopic submucosal excavation), 25 by EFTR (endoscopic full-thickness resection), and 5 by STER (submucosal tunneling endoscopic resection). In addition, GISTs of two cases were resected using LECS (laparoscopic and luminal endoscopic cooperative surgery) for the extraluminal and intraluminal growth pattern. The average tumor size was 1.89 ± 1.25 cm (range: 0.5-6.0 cm). Of these patients, 26 cases had a large tumor size (range: 2.0-6.0 cm), and endoscopic removal was successful in all of them. During the procedure, endoclips were used to close the perforation in all cases, without conversion to open surgery. The average length of hospital stay was 5.50 ± 2.15 days (range: 3-10 days). In the risk classification, 106 (79.7%) were of a very low risk, 25 (18.8%) of a low risk, and 2 (1.5%) of a moderate risk. The moderate-risk cases were treated with imatinib mesylate after ER. No recurrence or metastasis was observed during the follow-up period of 23 ± 8 months (range: 3-48 months).

CONCLUSIONS

The endoscopic treatment is feasible, effective, and safe for gastric GISTs, and individualized choice of approaches is recommended for GISTs.