Multimode ultrasound imaging of an anal canal gastrointestinal stromal tumour (GIST).

Gastrointestinal stromal tumours (GISTs) can develop throughout the entire gastrointestinal tract, but these tumours are usually found in the stomach and small intestine. In this case, a rare GIST arising from the anal canal was investigated using high-frequency endoanal ultrasound and external three-dimensional ultrasound with tomographic ultrasound imaging. The endoanal approach revealed the inner structure of the tumour. External ultrasound was used to determine the relationship between the lesion and surrounding tissues. In the limited reports of anal canal GISTs, no other lesions have been correctly diagnosed preoperatively or displayed in detail on imaging. The multilayer structure of the anal sphincter and these lesions can be clearly displayed by a variety of ultrasound imaging methods, which are nonradiative, low-cost and easily accessible. Modern ultrasound has the potential for broad application in anal canal tumour diagnosis and surveillance.

Non-gastrointestinal stromal tumor, mesenchymal neoplasms of the gastrointestinal tract: a review of tumor genetics, pathology, and cross-sectional imaging findings.

There is a diverse group of non-gastrointestinal stromal tumor (GIST), mesenchymal neoplasms of the gastrointestinal (GI) tract that demonstrate characteristic pathology and histogenesis as well as variable imaging findings and biological behavior. Recent advancements in tumor genetics have unveiled specific abnormalities associated with certain tumors, influencing their molecular pathogenesis, biology, response to treatment, and prognosis. Notably, giant fibrovascular polyps of the esophagus, identified through MDM2 gene amplifications, are now classified as liposarcomas. Some tumors exhibit distinctive patterns of disease distribution. Glomus tumors and plexiform fibromyxomas exhibit a pronounced affinity for the gastric antrum. In contrast, smooth muscle tumors within the GI tract are predominantly found in the esophagus and colorectum, surpassing the incidence of GISTs in these locations. Surgical resection suffices for symptomatic benign tumors; multimodality treatment may be necessary for frank sarcomas. This article aims to elucidate the cross-sectional imaging findings associated with a wide spectrum of these tumors, providing insights that align with their histopathological features.

The correlation of sarcopenia and adverse events of imatinib therapy postoperatively in gastrointestinal stromal tumor through computed tomography quantitative body composition.

PURPOSE: This study aimed to investigate the correlation between sarcopenia and adverse events (AEs) of postoperative imatinib therapy through computed tomography (CT) quantitative body composition for intermediate- and high-risk gastrointestinal stromal tumors (GISTs). METHODS: The study retrospectively analyzed the clinical data of 208 patients with intermediate- and high-risk GIST treated surgically and treated with imatinib afterward at the First Affiliated Hospital of Wenzhou Medical University between October 2011 and October 2021. Images of preoperative CT scans within 1 month were used to determine the body composition of the patients. On the basis of the L3 skeletal muscle index, patients were classified into sarcopenia and nonsarcopenia groups. In 2 groups, AEs related to imatinib were analyzed. RESULTS: The proportion of AEs related to imatinib in the sarcopenia group was higher, and this disparity had a significant statistical significance (P = .013). Sarcopenia was significantly associated with hemoglobin reduction compared with nonsarcopenia (P = .015). There was a significant difference between the sarcopenia group and the nonsarcopenia group in the ratio of severe AEs (grades 3-4). Hemoglobin content (odds ratio [OR], 0.981; 95% CI, 0.963-1.000; P = .045), sex (OR, 0.416; 95% CI, 0.192-0.904; P = .027), and sarcopenia (OR, 5.631; 95% CI, 2.262-14.014; P < .001) were the influential factors of imatinib severe AEs in patients with intermediate- and high-risk GIST within 1 year after imatinib treatment. CONCLUSION: Patients with preoperative sarcopenia have a higher incidence and severity of AEs during adjuvant imatinib therapy.

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

OBJECTIVE: The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD: A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS: In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS: The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

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

PURPOSE: To assess the performance of radiomics-based analysis of contrast-enhanced computerized tomography (CE-CT) images for distinguishing GS from gastric GIST. METHODS: Forty-nine patients with GS and two hundred fifty-three with GIST were enrolled in this retrospective study. CT features were evaluated by two associate chief radiologists. Radiomics features were extracted from portal venous phase images using Pyradiomics software. A non-radiomics dataset (combination of clinical characteristics and radiologist-determined CT features) and a radiomics dataset were used to build stepwise logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models, respectively. Model performance was evaluated according to sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve, and Delong's test was applied to compare the area under the curve (AUC) between different models. RESULTS: A total of 1223 radiomics features were extracted from portal venous phase images. After reducing dimensions by calculating Pearson correlation coefficients (PCCs), 20 radiomics features, 20 clinical characteristics + CT features were used to build the models, respectively. The AUC values for the models using radiomics features and those using clinical features were more than 0.900 for both the training and validation groups. There were no significant differences in predictive performance between the radiomic and clinical data models according to Delong's test. CONCLUSION: A radiomics-based model applied to CE-CT images showed comparable predictive performance to senior physicians in the differentiation of GS from GIST.

Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor.

BACKGROUND: To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the model's performance. METHODS: Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One experienced radiologist manually delineated the volume of interest and then resampled the voxel size of the images to 0.5 × 0.5 × 0.5 mm3, 1 × 1 × 1 mm3, and 2 × 2 × 2 mm3, respectively. Radiomics features were extracted using PyRadiomics, resulting in 1218 features from each phase image. The datasets were randomly divided into training set (n = 66) and validation set (n = 28) at a 7:3 ratio. After applying multiple feature selection methods, the optimal features were screened. Radial basis kernel function-based support vector machine (RBF-SVM) was used as the classifier, and model performance was evaluated using the area under the receiver operating curve (AUC) analysis, as well as accuracy, sensitivity, and specificity. RESULTS: The combined phase model performed better than the other phase models, and the resampling method of 0.5 × 0.5 × 0.5 mm3 achieved the highest performance with an AUC of 0.953 (0.881-1), accuracy of 0.929, sensitivity of 0.938, and specificity of 0.917 in the validation set. The Delong test showed no significant difference in AUCs among the three resampling methods, with p > 0.05. CONCLUSIONS: Radiomics can effectively differentiate between HP and GISTs on CT images, and the diagnostic performance of radiomics is minimally affected by different resampling methods.

Transumbilical Endoscopic Resection of Intra-abdominal Mesenchymal Tumor.

Gastrointestinal stromal tumors (GISTs) typically occur in the stomach and proximal small intestine but can also be found in any other part of the digestive tract, including the abdominal cavity, albeit rarely. In the present case, the tumor was resected endoscopically through the anterior gastric wall. Computed tomography (CT) scan and gastroscopy of a 60-year-old woman revealed submucosal lesions in the gastric body. The possibility of a stromal tumor was considered more likely. The endoscopic surgery was performed under endotracheal anesthesia. After a solution had been injected at the lesion site in the stomach, the entire gastric wall was dissected to expose the tumor. As the lesion was in the abdominal cavity and its base was attached to the abdominal wall, it was accessed using a sterilized PCF colonoscope. A sodium chloride injection was administered at the base. The tumor was then peeled along its boundaries using the hooking and excision knife combined with the precutting knife. Subsequently, the tumor was pulled into the stomach through the incision made in the stomach and then extracted externally through the upper digestive tract using the ERCP spiral mesh basket. After confirming the absence of bleeding at the incision site, the endoscope was returned to the stomach, and the stomach opening was closed using purse-string sutures. The patient recovered satisfactorily following the surgery and was discharged on day 4. Histological examination revealed a low-risk stromal tumor (spindle cell type, <5 mitosis/50 high-power fields [HPF]). Immunohistochemistry revealed positive staining for CD34 and CD117, negative staining for SMA, positive staining for DOG1, and negative staining for S100. Additionally, the expression of ki67 was 3%.

The Value of Computed Tomography in Differentiating Heterotopic Pancreas from Small Gastrointestinal Stromal Tumor.

BACKGROUND: It is critical for an accurate preoperative diagnosis of heterotopic pancreas (HP) and small gastrointestinal stromal tumor (GIST), given the unique treatment and prognosis of the two tumors. This study aims to investigate HP's computed tomography (CT) features and identify the distinguishing characteristics between HP and small GIST. METHODS: From January 2016 to August 2020, our hospital database was searched for confirmed histopathological results and CT scans for HP and GIST for further analysis. The statistically significant variables were determined by using Fisher's exact test, the Mann-Whitney U test, the receiver operating characteristic (ROC) curve and the inverse probability weighting method. RESULTS: CT images and clinical data were reviewed for 24 participants with HP and 34 patients with small GIST. Contour, border, relative enhancement grade, surface dimple, duct-like structure, short diameter (SD), attenuation of each lesion in the unenhanced phase (Lp), and the enhancement ratio of tumor in the venous phase (ER) were significant for differentiating HP from small GIST. Threshold values for SD and Lp were 1.40 cm and 42.33 Hounsfield units, respectively. Ill-defined border, surface dimple, ductlike structure, and Lp were independent factors that differentiated HP from small GIST. Additionally, SD and ER were also found to be independent factors. CONCLUSIONS: Contour, relative enhancement grade, SD, and Lp could effectively differentiate HP from small GIST, demonstrating improved diagnostic performance compared to other parameters. The presence of ductlike structures and surface dimples could further characterize HP. These findings may help distinguish HP from small GIST and avoid unnecessary invasive examination and therapy in individuals with asymptomatic HP.

Incidental Bleeding Gastrointestinal Stromal Tumor Identified on 99m Tc-RBC Scintigraphy.

ABSTRACT: Various pathologies could lead to occult gastrointestinal (GI) bleeding. Here we report the case of a 73-year-old woman who presented with hematochezia and syncope, and was found to have a large bleeding GI stromal tumor incidentally from 99m Tc-RBC scintigraphy. This study was done after negative workup with CT angiography, colonoscopy, and capsule endoscopy for the source of GI bleeding. Final pathology confirmed the mass being a low-grade GI stromal tumor after exploratory laparotomy.

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.

Predicting the progression-free survival of gastrointestinal stromal tumors after imatinib therapy through multi-sequence magnetic resonance imaging.

PURPOSE: Identify radiomics features associated with progression-free survival (PFS) and develop a predictive model for accurate PFS prediction in liver metastatic gastrointestinal stromal tumor patients (GIST). METHODS: This multi-center retrospective study involved a comprehensive review of clinical and imaging data pertaining to 211 patients with gastrointestinal stromal tumors (GIST) from Center A and B. A total of 147 patients with hepatic metastatic GIST were included, with 102 cases as the training set and 45 cases as the external validation set. Radiomics features were extracted from non-enhanced MR images, specifically T2WI, DWI, and ADC, and relevant features were selected through LASSO-Cox regression. A radiomics nomogram model was then constructed using multivariable Cox regression analysis to effectively predict PFS. The models performance were evaluated with the concordance index (C-index). RESULTS: The median age of the patients was 53 years, with 82 males and 65 females. A total of 21 radiomics features were selected to generate the radiomics signature. Radiomics signature slightly outperformed the clinical model but without significant difference (P > 0.05). Integrated radiomics signature with clinical features to build a nomogram, which exhibited high predictive performance in both training (C-index 0.757, 95% CI 0.692-0.822) and validation cohorts (C-index 0.718, 95% CI 0.618-0.818). Nomogram significantly outperformed the clinical model (P = 0.002 for training cohort, P < 0.001 for validation cohort). Stable long-term predictions shown by time-dependent ROC analysis (AUC 0.765-0.919 in training, 0.766-0.893 in validation). Multivariable Cox regression confirmed radiomics signature as an independent prognostic factor for preoperative survival prediction in hepatic metastatic GIST patients (HR = 3.973). CONCLUSION: Radiomics signature is valuable for predicting PFS in metastatic GIST patients. Integrating imaging features and clinical factors into a comprehensive nomogram improves accuracy and effectiveness of survival prognosis, guiding personalized treatment strategies.

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.

Percutaneous ultrasound-guided radiofrequency ablation for patients with liver metastases from gastrointestinal stromal tumors.

OBJECTIVES: This study aimed to analyze the survival outcomes and prognostic factors of radiofrequency ablation (RFA) for liver metastases from gastrointestinal stromal tumors (GISTs). METHODS: Between March 2011 and November 2022, 34 patients (16 males; age range, 25-72 [median age, 52.5] years) who underwent RFA for liver metastasis from GISTs were included. The mean maximum diameter of metastatic lesions was 2.4 ± 1.0 (range, 1.1-5.2) cm. Survival curves were constructed using the Kaplan-Meier method and compared using the log-rank test. Multivariate analyses were performed using a Cox proportional hazards model. RESULTS: For 79 lesions among 34 patients, all targeted lesions were completely ablated. The mean hepatic progression-free survival (HPFS) period was 28.4 ± 3.8 (range, 1.0-45.7) months. The 1-, 3-, and 5-year HPFS rates were 67.2%, 60.5%, and 20.2%, respectively. Based on the univariate analysis, the number of metastatic tumors and tyrosine kinase inhibitors(TKI) therapy before RFA were prognostic factors for HPFS. Multivariate analysis showed that pre-RFA TKI therapy was associated with a better HPFS(p = 0.030). The mean overall survival (OS) period was 100.5 ± 14.1 (range, 3.8-159.5) months and the 1-, 3-, and 5-year survival rates were 96.9%, 77.1%, and 58.7%, respectively. Both univariate and multivariate analysis indicated that extrahepatic metastasis before RFA (p = 0.044) was a significant prognostic factor for OS. CONCLUSIONS: Liver metastases from GIST exhibit relatively mild biological behavior. RFA is safe and effective, particularly in patients without pre-RFA extrahepatic metastases. Patients received targeted therapy before RFA can obtain an extended HPFS.

Multiple GISTand pheochromocytoma -A rare associationin neurofibromatosis type 1 / GIST múltipley feocromocitoma -Una asociación raraen neurofibromatosis tipo 1

We describe a case of coexistence of multiple gastrointestinal stromal tumors (GIST) and pheochromocytoma in a 32-year-old patient with neurofibromatosis type 1. The coexistence of these two conditions in patients with this syndrome is extremely rare.(AU)