Assessment of metastatic risk of gastric GIST based on treatment-naïve CT features

Prediction of Ki-67 expression and malignant potential in gastrointestinal stromal tumors: novel models based on CE-CT and serological indicators

PURPOSE

To identify more reliable imaging and serological indicators for predicting Ki-67 expression and malignant potential in gastrointestinal stromal tumors, as well as to develop a preoperative prediction model with clinical utility.

PATIENTS AND METHODS

This study retrospectively analyzed patients with gastrointestinal stromal tumors (GIST) diagnosed at the First Affiliated Hospital of Jinzhou Medical University between May 2018 and May 2024. Univariate logistic analyses, two-way stepwise regression, P-value stepwise regression, and LASSO regression were employed to screen for Ki-67 high expression and high malignant potential risk factors associated with GIST. Models were established using various regression methods; Nomograms, calibration curves, and clinical decision curves were generated for the two best prediction models.

RESULTS

Two-way stepwise regression analysis revealed that diameter (P=0.037; OR=1.22; 95% CI: 1.01 - 1.46), growth pattern (extraluminal type: P=0.028; OR=3.54; 95% CI: 1.14 - 10.94), enhancement model (P=0.099; OR=0.39; 95% CI: 0.12 - 1.20), EVFDM (P=0.069; OR=0.43; 95% CI: 0.17 - 1.07), PLR (P=0.099; OR=3.06; 95% CI: 0.81 - 11.59), and OPNI (P=0.058; OR=2.38; 95% CI: 0.97 - 5.84) are identified as independent risk factors for Ki-67 expression. Utilizing the two-way stepwise regression model to predict Ki-67 expression, the area under the curve (AUC) for the training group was 0.865 (95% CI: 0.807-0.922), while for the validation group it was 0.784 (95% CI: 0.631-0.937). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for the training group were 153.360 and 174.619, respectively. Two-way stepwise regression analysis revealed that volume (P < .001, OR = 1.06; 95% CI: 1.03 - 1.09), contour (P = 0.066; OR = 0.17; 95% CI: 0.05 - 0.62), ulcer (P = 0.094; OR = 0.16; 95% CI: 0.03 - 0.98), IBSC (P = 0.008; OR = 5.27; 95% CI: 1.57 - 17.69), and OPNI (P = 0.045; OR = 0.22; 95% CI: 0.05 - 0.96) are independent risk factors for malignant potential. Utilizing the two-way stepwise regression model to predict malignant potential, the AUC for the training group was 0.950 (95% CI: 0.920 - 0.980), while for the validation group it was 0.936 (95% CI: 0.867 - 1.000). The AIC and BIC values for the training group were 96.330 and 114.552, respectively.

CONCLUSION

Diameter, growth pattern, enhancement pattern, EVFDM, PLR, and OPNI are independent risk factors for GIST with high Ki-67 expression. Additionally, volume, contour, ulceration, IBSC, and OPNI serve as independent risk factors for GIST with high malignant potential. The preoperative models developed using CT images can predict the malignant potential and Ki-67 expression status of GIST to a certain extent. When combined with serological indicators, these models' predictive performance can be further enhanced.

CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

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.

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.

Early Prediction and Monitoring of Treatment Response in Gastrointestinal Stromal Tumors by Means of Imaging: A Systematic Review

Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic disease. It is important to assess the efficacy of TKI treatment at an early stage to optimize therapy strategies and eliminate futile ineffective treatment, side effects and unnecessary costs. This systematic review provides an overview of the imaging features obtained from contrast-enhanced (CE)-CT and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT to predict and monitor TKI treatment response in GIST patients. PubMed, Web of Science, the Cochrane Library and Embase were systematically screened. Articles were considered eligible if quantitative outcome measures (area under the curve (AUC), correlations, sensitivity, specificity, accuracy) were used to evaluate the efficacy of imaging features for predicting and monitoring treatment response to various TKI treatments. The methodological quality of all articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies, v2 (QUADAS-2) tool and modified versions of the Radiomics Quality Score (RQS). A total of 90 articles were included, of which 66 articles used baseline [18F]FDG-PET and CE-CT imaging features for response prediction. Generally, the presence of heterogeneous enhancement on baseline CE-CT imaging was considered predictive for high-risk GISTs, related to underlying neovascularization and necrosis of the tumor. The remaining articles discussed therapy monitoring. Clinically established imaging features, including changes in tumor size and density, were considered unfavorable monitoring criteria, leading to under- and overestimation of response. Furthermore, changes in glucose metabolism, as reflected by [18F]FDG-PET imaging features, preceded changes in tumor size and were more strongly correlated with tumor response. Although CE-CT and [18F]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended.

Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors

Objective

To develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).

Methods

Preoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.

Results

In the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.

Conclusion

The DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.

Utility of preoperative computed tomography features in predicting the Ki-67 labeling index of gastric gastrointestinal stromal tumors

PURPOSE

To evaluate the value of preoperative computed tomography (CT) features including morphologic and quantitative features for predicting the Ki-67 labeling index (Ki-67LI) of gastric gastrointestinal stromal tumors (GISTs).

METHODS

We retrospectively included 167 patients with gastric GISTs who underwent preoperative contrast-enhanced CT. We assessed the morphologic features of preoperative CT images and the quantitative features including the maximum diameter of tumor, total tumor volume, mean total tumor CT value, necrosis volume, necrosis volume ratio, enhanced tissue volume, and mean CT value of enhanced tissue. Potential predictive parameters to distinguish the high-level Ki-67LI group (>4%, n = 125) from the low-level Ki-67LI group (≤4%, n = 42) were compared and subsequently determined in multivariable logistic regression analysis.

RESULTS

Growth pattern (p = 0.036), shape (p = 0.000), maximum diameter (p = 0.018), total tumor volume (p = 0.021), mean total tumor CT value (p = 0.009), necrosis volume (p = 0.006), necrosis volume ratio (p = 0.000), enhanced tissue volume (p = 0.027), and mean CT value of enhanced tissue (p = 0.004) were significantly different between the two groups. Multivariate logistic regression analysis indicated that lobulated/irregular shape (odds ratio [OR] = 3.817; p = 0.000) and high necrosis volume ratio (OR = 1.935; p = 0.024) were independent factors of high-level Ki-67LI.

CONCLUSIONS

Higher necrosis volume ratio in combination with lobulated/irregular shape could potentially predict high expression of Ki-67LI for gastric GISTs.

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.

Role of Multidetector CT Imaging in the Risk Stratification of Gastrointestinal Stromal Tumors (GISTs)–A Retrospective Analysis

Background Gastrointestinal stromal tumors (GISTs) are the most common gastrointestinal mesenchymal neoplasms which can arise from any part of the gastrointestinal tract (GIT) or an extraintestinal location. Size and the organ of origin are the major imaging inputs expected from the radiologist. However, it is worthwhile to find out which imaging characteristics on MDCT correlate with risk stratification. This knowledge would help the clinician in treatment planning and prognostication. The aim of this retrospective study is to evaluate the various MDCT imaging characteristics of GISTs and find out which parameters have significant association with risk and subsequent development of metastasis on follow-up whenever it was possible.

Materials and Methods This is a retrospective study conducted on 45 histopathologically proven cases of GIST from two institutions by searching from the digital archives. The following imaging parameters were analyzed: maximum size in any plane, organ of origin, shape (round, ovoid or irregular), margin (well-defined or ill-defined), surface (smooth or lobulated), percentage of necrosis, growth pattern, enhancement characteristics–both intensity (mild, moderate or significant) and pattern (homogenous vs. heterogenous), calcification, infiltration into adjacent organs, and presence of metastasis at presentation or on follow-up.

Results CT morphological parameters of significance in risk stratification as per our study include tumor necrosis, predominant cystic change, irregular and lobulated shape/surface characteristics, and adjacent organ infiltration.

The parameters which were associated with development of metastasis were size > 5 cm, necrosis > 30%, and the presence of adjacent organ infiltration.

Conclusion The radiologist has an important role in ascertaining the size of tumor as well as the organ of origin accurately to guide the clinician in risk calculation and subsequent prognostication. In addition, certain CT characteristics mentioned above, namely, tumor size, significant necrosis/cystic changes, irregular/lobulated contour, and invasion of adjacent organs, help in risk stratification and in predicting metastasis/poor prognosis.

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.

New advances in radiomics of gastrointestinal stromal tumors

Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.

Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors

PURPOSE

The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification.

METHODS

Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated.

RESULTS

The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870-1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777-0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs.

CONCLUSION

Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.

Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters

PURPOSE

To examine the value of spectral CT quantitative parameters in gastrointestinal stromal tumor (GIST) risk classification.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board. The requirement for informed consent was signed. The authors evaluated 86 patients (30 high risk, 22 medium risk, 28 low risk, and 6 very low risk; mean age: 59 years [range 19-83 years]) with pathologically confirmed GIST who underwent plain and triple-phase contrast-enhanced CT with spectral CT imaging mode from March 2015 through September 2017, with manual follow-up. Quantitative parameters including the CT value of 70 keV monochromatic images, the slope of spectral curves, and the normalized iodine concentration (NIC) and water (iodine) concentrations were measured and calculated, and conducted a power analysis of the above data.

RESULTS

(1) The CT values at 70 keV of the high-risk group were higher than the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), no significant differences in the intermediate-risk and low-risk groups were noted (P = 0.874, 0.871, 0.831, respectively). (2) The slope of the spectral curve of the high-risk group was higher than those of the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), and there were no significant differences between the intermediate- and low-risk groups (P = 0.069, 0.466, 0.840, respectively). (3) The NIC of the high-risk group significantly differed from the lower risk groups (P ≤ 0.001). There was also no significant difference observed between the intermediate- and low-risk groups (P = 0.671, 0.457, 0.833, respectively). (4) The power analysis results show that only the low-risk group with delay period is 0.530, the rest groups are all greater than 0.999.

CONCLUSION

Dual-energy spectral CT with quantitative analysis may help to increase the accuracy in differentiating the pathological risk classification of GIST between high risk and non-high risk, preoperatively. There were limitations for distinguishing the intermediate- and low-risk groups.

Value of contrast-enhanced harmonic endoscopic ultrasonography in differentiating between gastrointestinal stromal tumors: A meta-analysis

OBJECTIVE

Contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) is a valuable device to diagnose and determine the malignant potential of gastrointestinal stromal tumors (GIST) as early as possible when making clinical therapeutic decisions. This study aimed to estimate the ability of CH-EUS to discriminate between GIST and benign submucosal lesions (SML) and to predict their malignant potential.

METHODS

PubMed, MEDLINE, EMBASE, the Web of Science, and Cochrane Central Register of Controlled Trials databases were screened. Using the data provided in the literatures, 2 × 2 tables were constructed to obtain the pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve (AUROC) was calculated.

RESULTS

Four studies with a total of 187 patients were identified to evaluate the value of CH-EUS in discriminating between GIST and benign SML. The pooled sensitivity, specificity, and AUROC were 89% (95% CI 0.82-0.93), 82% (95% CI 0.66-0.92), and 0.89, respectively. Five studies including 143 patients were analyzed to assess the accuracy of CH-EUS in determining the malignant potential of GIST. The pooled sensitivity, specificity, and AUROC curve of CH-EUS were 96% (95% CI 0.90-0.99), 53% (95% CI 0.40-0.66), and 0.92, respectively.

CONCLUSIONS

CH-EUS is a safe, noninvasive method that can distinguish between GIST and benign subepithelial lesions and to predict their malignant potential to a certain extent. Large-scale, multicenter prospective studies are needed in the future.

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

OBJECTIVE

This study aimed to investigate the endoscopic ultrasound (EUS) and computed tomography (CT) features of gastric gastrointestinal stromal tumors (GISTs) for assessing potential malignancy and prognosis.

METHODS

Fifty consecutive patients with primary gastric GISTs larger than 2 cm were retrospectively enrolled in this study. The association of CT and EUS features with malignancy was analyzed using univariate and stepwise logistic regression method. The agreement between EUS/CT lesion size and pathologic tumor size was analyzed by calculating the intraclass correlation coefficient (ICC) value, and the association of imaging features with mitotic counts was further analyzed using univariate analysis. The Kaplan-Meier method and Cox proportional hazards models were used to assess the value of imaging features for predicting the prognosis of GIST patients.

RESULTS

Tumor size > 5 cm and an exophytic/mixed growth pattern on CT as well as tumor size > 5 cm and the presence of cystic spaces on EUS were independent predictors of highly malignant GISTs (all p < 0.05). The ICC values of CT/EUS lesion size relative to pathologic tumor size showed very good reliability (0.853 for EUS and 0.831 for CT). Only tumor shape and growth pattern on CT were significant for predicting mitotic index (both p < 0.05). Direct organ invasion on CT (p = 0.036; hazard ratio [HR] = 11.891) and serosal invasion on EUS (p = 0.015; HR = 8.223) were independent adverse prognostic factors.

CONCLUSIONS

CT features may be more useful than EUS features for predicting tumor mitotic index. In addition, preoperative imaging features can help predict the prognosis of gastric GISTs.

KEY POINTS

• Both CT and EUS features can be used for risk stratification of gastric GISTs larger than 2 cm. • CT features performed better than EUS features for predicting tumor mitotic index. • Preoperative imaging features can help predict the prognosis of gastric GISTs.

A correlation research of Ki67 index, CT features, and risk stratification in gastrointestinal stromal tumor

Background and Objectives

Recurrence and metastasis are the most important factors affecting the quality of life and survival rate of patients with gastrointestinal stromal tumors (GISTs). Accurate preoperative determination of the malignant degree of GISTs and the development of a reasonable treatment plan can effectively reduce the recurrence rate. CT is currently considered the preferred imaging modality for initial assessment. Until now, there have only been a few studies investigating the relationship between CT features and recurrence of GISTs. However, the value of CT features in prognostic assessment is still unclear. In this study, we attempted to investigate the prognostic significance of CT features and the Ki67 index in GISTs.

Methods

We retrospectively analyzed the clinicopathological and imaging data for 151 patients with a histopathological diagnosis of GIST who had received contrast‐enhanced CT examination and surgical resection at XinHua Hospital from October 2008 to December 2015 or Sir Run Run Shaw Hospital in 2017. Then, we explored the correlation among CT features, the Ki67 index, and risk stratification of GISTs. The correlation among CT features, the Ki67 index, and risk stratification was mainly analyzed using the Spearman rank correlation.

Results

The incidence of high‐risk disease or metastasis was clearly higher in the group with Ki67 > 5% than that in the group with Ki67 ≤ 5% (P < 0.001). The Ki67 index was positively correlated with risk stratification (r = 0.558) or mitotic index (r = 0.619). CT imaging features including size, contour, and margin of the tumor were associated with the Ki67 index (r = 0.332, 0.333, and 0.302, respectively). The multivariate logistic regression analysis revealed that the tumor size [P = 0.043 Exp (B) = 1.150] and the presence of ulceration [P = 0.011, Exp (B) = 3.669] were effective variables in distinguishing between the groups with Ki67 ≤ 5% and >5%. The presence of necrosis or cystic degeneration, tumor contour, tumor margin, and pattern of enhancement were associated with risk stratification (r = 0.530, 0.501, 0.419, and 0.447, respectively).

Conclusions

Our findings suggest that the Ki67 index is an effective complementation in predicting the prognosis of GISTs, and CT features including size, contour, and margin of the tumor, presence of necrosis or cystic degeneration, and pattern of enhancement provide evidence to support the importance of preoperative assessment.

Evaluation of computed tomography vascular reconstruction for the localization diagnosis of perigastric mass

BACKGROUND

The aim of this study was to evaluate the utility of computed tomography (CT) vascular reconstruction in the localization diagnosis of perigastric mass.

METHODS

Fifty-eight patients with pathologically detected perigastric mass underwent abdominal dynamic contrast-enhanced CT. CT vascular reconstructions were produced from arterial phase data using volume rendering (VR), multiplanar reconstruction (MPR), and maximal intensity projection (MIP). Image analysis was focused on the relationship between the mass, perigastric arteries, and the gastric wall. Localization diagnosis values were compared between CT vascular reconstruction and dynamic-enhanced CT images.

RESULTS

Among the 58 cases of perigastric mass, 41 cases originated from the stomach, 7 cases from the left liver lobe, 6 from the pancreas, 2 from lessor omental bursa, 1 from transverse mesocolon, and 1 from left adrenal gland. The accuracy of CT vascular reconstruction images in the localization diagnosis of perigastric mass was higher than that of dynamic-enhanced CT images (98.3% and 86.2%, respectively, P = .04). On the reference level, 35 (35/41) patients with stomach-originated masses showed the mass adjacent perigastric arteries pushed away from the stomach (arterial displacement sign), and 15 (15/17) patients with nonstomach-originated masses showed perigastric arteries between the mass and the stomach (arterial entrapment sign). The sensitivity, specificity, positive predictive value, and negative predictive value of the localization diagnosis of perigastric mass with arterial displacement sign were 85.4%, 100%, 100%, and 73.9%, respectively, and with arterial entrapment sign, 88.2%, 100%, 100%, and 95.3%, respectively.

CONCLUSION

CT vascular reconstruction can clearly depict the relationship between perigastric mass and adjacent perigastric arteries, which may help us more accurately differentiate between stomach-originated and nonstomach-originated masses compared with original dynamic-enhanced CT images.

Update on Gastrointestinal Stromal Tumors for Radiologists

The management of gastrointestinal stromal tumors (GISTs) has evolved significantly in the last two decades due to better understanding of their biologic behavior as well as development of molecular targeted therapies. GISTs with exon 11 mutation respond to imatinib whereas GISTs with exon 9 or succinate dehydrogenase subunit mutations do not. Risk stratification models have enabled stratifying GISTs according to risk of recurrence and choosing patients who may benefit from adjuvant therapy. Assessing response to targeted therapies in GIST using conventional response criteria has several potential pitfalls leading to search for alternate response criteria based on changes in tumor attenuation, volume, metabolic and functional parameters. Surveillance of patients with GIST in the adjuvant setting is important for timely detection of recurrences.