Evaluation of the Relationships Between Computed Tomography Features, Pathological Findings, and Prognostic Risk Assessment in Gastrointestinal Stromal Tumors

Small Bowel Gastrointestinal Stromal Tumors: The Value of CT Enterography in Assessing Pathological Aggressiveness

OBJECTIVE

This study aimed to characterize the computed tomography (CT) enterography features of the small bowel gastrointestinal stromal tumors (GIST) and to determine the association with pathological aggressiveness.

METHODS

Computed tomography enterography images of 30 patients with the histologically confirmed small bowel GIST were retrospectively enrolled. Tumor size, location, border, growth pattern, enhancement pattern, necrosis, calcification, ulceration, internal air, nodal metastasis, liver metastasis, peritoneal metastasis, and draining vein were evaluated. Relationships between imaging features and pathological aggressiveness were analyzed using χ2 test or Fisher exact test. Correlations among CT features were analyzed using Spearman correlation analysis.

RESULTS

There were significant differences in tumor size between different risk levels (F = 8.388, P < 0.001). There were statistically significant differences in the 5 imaging manifestations of necrosis, ulcer, tumor boundary, drainage vein, and intratumoral gas (P < 0.05). There was a significant negative correlation between tumor size and enhancement type as well as clear tumor boundary. There was a significant positive correlation between tumor size and necrosis, ulcer, drainage vein, intratumoral gas, liver metastasis, and peritoneal metastasis.

CONCLUSIONS

Some CT enterography imaging features might be useful in the determination of the pathological aggressiveness in the patients with small bowel GIST.

Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements

PURPOSE

To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs).

METHODS

Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared.

RESULTS

The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618).

CONCLUSIONS

Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

CT features of calcified micro-gastric gastrointestinal stromal tumors: a case series

BACKGROUND

Due to the lack of corresponding clinical symptoms, small calcified gastric gastrointestinal stromal tumors (GISTs) are often overlooked in clinical practice. Therefore, there is an unmet need to define the imaging features of calcified micro-gastric GISTs to facilitate diagnosis. This study retrospectively analyzed the computed tomography (CT) features of pathologically confirmed calcified micro-gastric GISTs.

METHODS

The medical records (gastroscopy, pre-treatment gastric CT imaging [pre- and post-contrast scans], pathology) of patients with calcified gastric GISTs < 1 cm in diameter confirmed pathologically after endoscopic submucosal dissection, endoscopic submucosal excavation, or endoscopic full-thickness resection were retrospectively reviewed.

RESULTS

Seven patients had 8 calcified gastric GISTs < 1 cm in diameter. Six patients hadsingle lesions, and 1patients had multiple lesions. Six patients had lesions in the gastric fundus, 1 patient had a lesion in the body of the stomach. Lesions had a mean diameter of 5.2 mm (range, 1.3 mm ~ 7 mm). Unenhanced CT scans showed spots and high-density nodular calcifications in 3 submucosal lesions, 2 lesions in the muscularis propria, and 3 subserosal lesions that protruded outside the stomach. Among the 8 lesions, only two had solid soft tissue components surrounding the calcification, with one of these two showing post contrast enhancement of the solid soft tissue component.

CONCLUSIONS

Novel CT features of gastric GISTs included: commonly found in the gastric antrum, small size (< 1 cm in diameter), calcification, few solid soft tissue components, and no abnormal enhancement in most cases.

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.

Machine learning for predicting the risk stratification of 1-5 cm gastric gastrointestinal stromal tumors based on CT

BACKGROUD

To predict the malignancy of 1-5 cm gastric gastrointestinal stromal tumors (GISTs) by machine learning (ML) on CT images using three models - Logistic Regression (LR), Decision Tree (DT) and Gradient Boosting Decision Tree (GBDT).

METHODS

231 patients from Center 1 were randomly assigned into the training cohort (n = 161) and the internal validation cohort (n = 70) in a 7:3 ratio. The other 78 patients from Center 2 served as the external test cohort. Scikit-learn software was used to build three classifiers. The performance of the three models were evaluated by sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). Diagnostic differences between ML models and radiologists were compared in the external test cohort. Important features of LR and GBDT were analyzed and compared.

RESULTS

GBDT outperformed LR and DT with the largest AUC values (0.981 and 0.815) in the training and internal validation cohorts and the greatest accuracy (0.923, 0.833 and 0.844) across all three cohorts. However, LR was found to have the largest AUC value (0.910) in the external test cohort. DT yielded the worst accuracy (0.790 and 0.727) and AUC values (0.803 and 0.700) in both the internal validation cohort and the external test cohort. GBDT and LR performed better than radiologists. Long diameter was demonstrated to be the same and most important CT feature for GBDT and LR.

CONCLUSIONS

ML classifiers, especially GBDT and LR with high accuracy and strong robustness, were considered to be promising in risk classification of 1-5 cm gastric GISTs based on CT. Long diameter was found the most important feature for risk stratification.

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.

Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors

BACKGROUND

preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST's prognosis as determined by the Miettinen classification.

METHODS

patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated.

RESULTS

52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations.

CONCLUSIONS

the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.

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.

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

Introduction

Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction.

Methods

A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier.

Results

The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively.

Discussion

The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.

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.

Angiogenesis in gastrointestinal stromal tumors: From bench to bedside

Gastrointestinal stromal tumors (GISTs) are rare neoplasms with an estimated incidence from 0.78 to 1-1.5 patients per 100000. They most commonly occur in the elderly during the eighth decade of life affecting predominantly the stomach, but also the small intestine, the omentum, mesentery and rectosigmoid. The available treatments for GIST are associated with a significant rate of recurrent disease and adverse events. Thorough understanding of GIST’s pathophysiology and translation of this knowledge into novel regimens or drug repurposing is essential to counter this challenge. The present review summarizes the existing evidence about the role of angiogenesis in GIST’s development and progression and discusses its clinical underpinnings.

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.

Gastrointestinal stromal tumors: relationship between preoperative CT features and pathologic risk stratification

OBJECTIVE

To investigate a relationship between contrast-enhanced computed tomography (CECT) features of gastrointestinal stromal tumors (GISTs) and risk of relapse according to Miettinen stratified risk classifications.

METHODS

After ethical committee approval, a retrospective analysis was conducted on the preoperative CECT of patients with pathologically proven GIST undergoing surgery between June 2009 and December 2019. Chi-square analysis was used to evaluate the correlation between Miettinen stratified risk categories and the following imaging features: tumor size and location, growth pattern, margins, type and degree of contrast enhancement, presence of calcifications, necrosis, signs of ulceration/fistulation, internal hemorrhagic foci, enlarged feeding or draining vessels (EFDV), ascites, peritoneal implants, lymphadenopathy, or metastasis.

RESULTS

A total of 54 patients (mean age 65 ± 11, 29 men) were included in the study with a total of 56 GISTs. Necrosis, ulceration/fistulation, hemorrhage, margins, enlarged vessels, type of contrast enhancement, and metastasis turned out to be associated with Miettinen risk categories (p < 0.005). Logistic regression analysis identified the presence of necrosis and EFDV as predictors of pathologic risk of relapse (overall accuracy of 89.3%).

CONCLUSION

Preoperative CECT may be helpful in predicting pathologic risk categories of GISTs, as determined by the Miettinen classification system.

Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors

Objective

The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm.

Materials and Methods

Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria.

Results

At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943).

Conclusion

We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

MRI Texture-Based Models for Predicting Mitotic Index and Risk Classification of Gastrointestinal Stromal Tumors

BACKGROUND

Treatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under- or overtreatment.

PURPOSE

To develop and validate an MRI texture-based model to predict the mitotic index and its risk classification.

STUDY TYPE

Retrospective.

POPULATION

Ninety-one patients with histologically-confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort.

FIELD STRENGTH/SEQUENCE

T₂ -weighted imaging (T₂ WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) at 1.5T.

ASSESSMENT

GIST images were manually segmented by two independent radiologists using ITK-SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk.

RESULTS

The texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922).

DATA CONCLUSION

The texture-based model can be used to predict GIST mitotic index and risk classification preoperatively.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

3.

Differentiation of gastric glomus tumor from small gastric stromal tumor by computed tomography

OBJECTIVE

This study was performed to investigate the value of computed tomography (CT) in the differentiation of gastric glomus tumors (GGTs) and small gastric stromal tumors (GSTs).

METHODS

Fifty-nine patients with pathologically confirmed GGTs (n = 11) and GSTs (n = 48) from 2006 to 2019 were retrospectively evaluated. All patients' preoperative CT imaging features were analyzed.

RESULTS

The following features were significantly different between GGTs and small GSTs: location in the antrum, endophytic growth, heterogeneous enhancement in the arterial phase, CT value in the arterial phase of ≥60.7 Hounsfield units (HU), CT value in the portal phase of ≥87.6 HU, degree of enhancement in the arterial phase of ≥29.9 HU, and degree of enhancement in the portal phase of ≥49.0 HU. A model including four randomly selected features among these seven criteria was built to differentiate GGTs from small GSTs with a sensitivity and specificity of 90.9% (10/11) and 100% (48/48), respectively.

CONCLUSION

We identified seven features that are useful for differentiating GGTs from small GSTs. A combination of four of these seven criteria may increase the diagnostic accuracy.

Identification of gastrointestinal stromal tumors from leiomyomas in the esophagogastric junction: A single-center review of 136 cases

To identify significant clinical and CT features for the differentiation of gastrointestinal stromal tumors (GISTs) from leiomyomas in the esophagogastric junction (EGJ).One hundred thirty six patients with pathologically proven GISTs (n = 87) and leiomyomas (n = 49) in the EGJ were enrolled. And preoperative CT images were available in 73 GISTs cases and 34 leiomyoma cases. Two radiologists reviewed the CT images by consensus with regard to tumor size, shape, growth pattern, surface, enhancement pattern, enhancement degree, attention at each phasic image and the presence of surface ulcer, calcification, and intralesional low attention.Eight significant clinical and CT features were identified for differentiating GISTs from leiomyomas: older age (>46.5 years), tumor long diameter >4.5 cm, heterogeneous enhancement, high degree enhancement, mean CT attenuation >69.2 HU, presences of intralesional low attenuation and surface ulcer, absences of calcification (P < .05). On the receiver operating characteristic curve analysis, an optimal cutoff score of 3.5 was achieved for differentiating GISTs from leiomyomas with an AUC of 0.844 (sensitivity: 76.7%, specificity: 76.5%).older age (>46.5 years), tumor long diameter >4.5 cm, heterogeneous enhancement, high degree enhancement, mean CT attenuation >69.2 HU, presences of intralesional low attenuation and surface ulcer, absence of calcification are significant features highly suggestive of GISTs in differentiation from leiomyomas in the EGJ.

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.

Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors

PURPOSE: To build radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict malignant potential and mitotic count of gastrointestinal stromal tumors (GISTs). PATIENTS AND METHODS: A total of 333 GISTs patients were retrospectively included in our study. Radiomic features were extracted from the preoperative CE-CT images. According to postoperative pathology, patients were categorized by malignant potential and mitotic count, respectively. The most valuable radiomic features were chosen to build a logistic regression model to predict the malignant potential and a random forest classifier model to predict the mitotic count. The performance of radiomic models was assessed with the receiver operating characteristics curve. Our study further developed a radiomic nomogram to preoperatively predict malignant potential in a personalized way for patients with GISTs. RESULTS: The predictive model was built to discriminate high– from low–malignant potential GISTs with an area under the curve (AUC) of 0.882 (95% CI 0.823-0.942) in the training set and 0.920 (95% CI 0.870-0.971) in the validation set. Moreover, the other radiomic model was built to differentiate high– from low–mitotic count GISTs with an AUC of 0.820 (95% CI 0.753-0.887) in the training set and 0.769 (95% CI 0.654-0.883) in the validation set. CONCLUSION: The radiomic models using CE-CT showed a good predictive performance for preoperative risk stratification of GISTs and hold great potential for personalized clinical decision making.

Evaluation of Clinical Plus Imaging Features and Multidetector Computed Tomography Texture Analysis in Preoperative Risk Grade Prediction of Small Bowel Gastrointestinal Stromal Tumors

OBJECTIVE

This study aimed to evaluate the prediction roles of clinical plus imaging features and multidetector computed tomography (MDCT) texture analysis in preoperative risk grade classification of small bowel (SB) gastrointestinal stromal tumors (GISTs).

METHODS

This study included 213 SB GIST patients. Clinical features and MDCT imaging findings were reviewed. Tumor risk stratifications were determined according to modified National Institutes of Health criteria. Random forest models were performed to evaluate the correlation of risk stratification.

RESULTS

The model of clinical plus imaging findings showed an area under receiver operating characteristic curve (AUC) of 92.0%. The AUC of texture analysis based on MDCT portal phase was 93.3%, without statistical difference from that of clinical plus imaging model (P = 0.378). The AUC of the model combined clinical plus imaging features and MDCT texture analysis was 94.3%, which was significantly higher than the AUC of clinical imaging model (P = 0.042).

CONCLUSION

Texture analysis may become an important comprehensive tool for preoperative risk stratification of SB GISTs.