Endoscopic Characteristics of Small-Bowel Gastrointestinal Stromal Tumors Detected During Single-Balloon Enteroscopy: A Retrospective Analysis.

BACKGROUND/AIMS:  The endoscopic features of small-bowel gastrointestinal stromal tumors (GISTs) are not well defined. The objective of this study was to describe the endoscopic features of GISTs of the small intestine detected via single-balloon enteroscopy (SBE). MATERIALS AND METHODS:  Patients with surgically confirmed small intestinal GISTs from January 2014 to September 2022 were retrospectively analyzed. The hospital's electronic medical record system was used to retrieve the patients' data, including their demographics, clinical symptoms, hemoglobin on admission, endoscopic and computerized tomography findings, clinicopathological findings, and surgical management data. RESULTS:  In total, 46 GIST patients (23 men and 23 women) with overt bleeding were included, with a mean age of 52 years (23-80 years). The typical duration of the symptoms was 48 hours. Four patients (8.70%) had lesions in the duodenum, 32 (69.56%) had lesions in the jejunum, 8 (17.39%) had lesions in the ileum, and 2 (4.35%) had lesions around the junction of the jejunum and ileum. Out of the 46 patients, 27 underwent SBE, and GISTs were visualized in 25, while the lesions could not be visualized in the remaining 2. Submucosal round (n = 13), submucosal sessile (n = 8), and invasive/penetrating (n = 4) were among the endoscopic tumor features. Twenty patients exhibited submucosal protuberant lesions, with ulceration, vascular nodules/congestion, or erosion on the surface, and 5 patients presented ulcerative infiltrative lesions. The multiple logistic regression analysis indicated that the invasive/penetrating characteristics of GISTs under SBE evaluation are significantly correlated with the risk level of GIST malignancy (P < .05). CONCLUSION:  A variety of endoscopic characteristics could be observed during the preoperative SBE evaluation of small-intestine GISTs.

Application of Computer-Assisted Endoscopic Ultrasonography Based on Texture Features in Differentiating Gastrointestinal Stromal Tumors from Benign Gastric Mesenchymal Tumors.

BACKGROUND/AIMS:  Gastrointestinal stromal tumors are common gastric mesenchymal tumors that are potentially malignant. However, endoscopic ultrasonography is poor in diagnosing gastrointestinal stromal tumors. The study investigated the efficacy of texture features extracted from endoscopic ultrasonography images to differentiate gastrointestinal stromal tumors from gastric mesenchymal tumors. MATERIALS AND METHODS:  The endoscopic ultrasonography examinations of 120 patients with confirmed gastric gastrointestinal stromal tumors, leiomyoma, or schwannoma were evaluated. Histology was considered the gold standard. Three feature combinations were extracted from endoscopic ultrasonography images of each lesion: 48 gray-level co-occurrence matrix-based features, 48 gray-level co-occurrence matrix-based features plus 3 global gray features, and 15 gray-gradient co-occurrence matrix-based features. Support vector machine classifiers were constructed by using feature combinations to diagnose gastric gastrointestinal stromal tumors. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were used to evaluate the diagnostic performance. The support vector machine model's diagnostic performance was compared with the endoscopists. RESULTS:  The 3 feature combinations had better performance in differentiating gastrointestinal stromal tumors: gray-gradient cooccurrence matrix-based features yielded an area under the receiver operating characteristic curve of 0.90, which was significantly greater than an area under the receiver operating characteristic curve of 0.83 in gray-level co-occurrence matrix-based features and an area under the receiver operating characteristic curve of 0.84 in the texture features plus 3 global features. The support vector machine model (81.67% accuracy, 81.36% sensitivity, and 81.97% specificity) was also better than endoscopists (an average of 69.31% accuracy, 65.54% sensitivity, and 72.95% specificity) Conclusion: Texture features in computer-assisted endoscopic ultrasonography diagnosis are useful to differentiate gastrointestinal stromal tumors from benign gastric mesenchymal tumors and compare favorably with endoscopists. Support vector machine model using gray-gradient co-occurrence matrix-based texture features revealed the best diagnostic performance in diagnosing gastric gastrointestinal stromal tumors.

Ruptured Duodenal GIST in a Young Female - A Rare Presentation and Comprehensive Review.

Gastrointestinal stromal tumors (GISTs) are uncommon mesenchymal neoplasms primarily found in the gastrointestinal (GI) tract. While they typically occur in older adults, GISTs can manifest in individuals of any age. This publication paper presents a case study of a rare occurrence: a spontaneously ruptured duodenal GIST in a young female. Through an in-depth review of the literature, we aim to provide insights into the clinical presentation, diagnostic challenges, management strategies, and long-term outcomes associated with this unique clinical scenario.

RésuméLes tumeurs stromales gastro-intestinales (GIST) sont des néoplasmes mésenchymateux peu communs principalement trouvés dans le tractus gastro-intestinal (GI). Bien qu'elles surviennent généralement chez les adultes plus âgés, les GIST peuvent se manifester chez des individus de tout âge. Cet article présente une étude de cas d'une occurrence rare: une GIST duodénale spontanément rompue chez une jeune femme. À travers une revue approfondie de la littérature, nous visons à fournir des éclairages sur la présentation clinique, les défis diagnostiques, les stratégies de prise en charge et les résultats à long terme associés à ce scénario clinique unique.

Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors.

OBJECTIVES: Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this study, we used a computer-assisted diagnosis (CAD) system with deep learning analysis of EUS images (EUS-CAD) and assessed its ability to differentiate GI stromal tumors (GISTs) from other mesenchymal tumors and their risk classification performance. MATERIALS AND METHODS: A total of 101 pathologically confirmed cases of subepithelial lesions (SELs) arising from the muscularis propria layer, including 69 GISTs, 17 leiomyomas and 15 schwannomas, were examined. A total of 3283 EUS images were used for training and five-fold-cross-validation, and 827 images were independently tested for diagnosing GISTs. For the risk classification of 69 GISTs, including very-low-, low-, intermediate- and high-risk GISTs, 2,784 EUS images were used for training and three-fold-cross-validation. RESULTS: For the differential diagnostic performance of GIST among all SELs, the accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve were 80.4%, 82.9%, 75.3% and 0.865, respectively, whereas those for intermediate- and high-risk GISTs were 71.8%, 70.2%, 72.0% and 0.771, respectively. CONCLUSIONS: The EUS-CAD system showed a good diagnostic yield in differentiating GISTs from other mesenchymal tumors and successfully demonstrated the GIST risk classification feasibility. This system can determine whether treatment is necessary based on EUS imaging alone without the need for additional invasive examinations.

Clinical course of asymptomatic duodenal subepithelial lesions.

BACKGROUND/AIMS: There is limited knowledge regarding the management of duodenal subepithelial lesions (SELs) owing to a lack of understanding of their natural course. This study aimed to assess the natural course of asymptomatic duodenal SELs and provide management recommendations. METHODS: Patients diagnosed with duodenal SELs and followed up for a minimum of 6 months were retrospectively investigated. RESULTS: Among the 443,533 patients who underwent esophagogastroduodenoscopy between 2008 and 2020, duodenal SELs were identified in 0.39% (1,713 patients). Among them, 396 duodenal SELs were monitored for a median period of 72.5 months (interquartile range, 37.7-111.3 mo). Of them, 16 SELs (4.0%) showed substantial changes in size or morphology at a median follow-up of 35.1 months (interquartile range, 21.7-51.4 mo). Of these SELs with substantial changes, tissues of two SELs were acquired using endoscopic ultrasound-guided fine needle aspiration biopsy: one was a lipoma and the other was non-diagnostic. Three SELs were surgically or endoscopically removed; two were diagnosed as gastrointestinal stromal tumors, and one was a lipoma. An initial size of 20 mm or larger was associated with substantial changes during follow-up (p = 0.016). CONCLUSION: While the majority of duodenal SELs may not exhibit substantial interval changes, regular follow-up with endoscopy may be necessary for cases with an initial size of 20 mm or larger, considering a possibility of malignancy.

Excision of a gastrointestinal stromal tumour in a dog using short-wave infrared fluorescence imaging and indocyanine green.

A 7-year-old castrated male Golden Retriever weighing 36.8 kg presented to the Veterinary Teaching Hospital with vomiting, anorexia and depression. After blood tests, radiographic, ultrasound and computed tomography examinations, a 7.85 × 5.90 × 8.75 cm mass was identified in the caecum. To visualise the tumour margin and improve the accuracy of tumour resection, intraoperative short-wave infrared imaging using indocyanine green was performed during surgery. An indocyanine green solution was injected intravenously as a bolus of 5 mg/kg 24 h before surgery. Tumour resection was performed with a 0.5 cm margin from the fluorescent-marked tissues. Histopathological examination revealed a diagnosis of a gastrointestinal stromal tumour (GIST) and the absence of neoplastic cells in the surgical margin, indicating a successful surgery. To our knowledge, this is the first case of a GIST resection in a dog using intraoperative short-wave infrared imaging.

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.

Endoscopic Ultrasound-Guided Tissue Acquisition Using Fork-Tip Needle for Subepithelial Lesions: A Single-Center Validation Study.

BACKGROUND: The cutoff value for stereomicroscopic on-site evaluation (SOSE) in endoscopic ultrasound-guided tissue acquisition (EUS-TA) has high diagnostic sensitivity when a Franseen needle is employed for upper gastrointestinal subepithelial lesions (SELs) (stereomicroscopically visible white core [SVWC] ≥ 4 mm). AIM: We aimed to determine whether high diagnostic sensitivity could be obtained when EUS-TA was performed using a Fork-tip needle. METHODS: Twenty-one patients were prospectively registered. Patients underwent EUS-TA using a Fork-tip needle for upper gastrointestinal SELs at Kitasato University Hospital between January and November 2022. Punctures were made twice using the needle, and SOSE was conducted for each specimen. Blood and physical examination were performed to assess adverse events. Pathological diagnosis was made using hematoxylin and eosin-stained sections and immunohistochemical staining. Statistical comparisons were completed using Fisher's exact tests. RESULTS: The diagnostic rate of EUS-TA was 100% (21/21 cases). The final diagnosis was gastrointestinal stromal tumor in 17 (81.0%) and leiomyoma in 4 (19.0%) patients. SOSE was conducted on all 42 punctures, and the tissue sampling rate was 100% (42/42 punctures). Specimens with SVWC ≥ 4 mm were collected in 97.6% punctures (41/42 punctures) and the diagnostic sensitivity for these specimens was 100% (41/41 punctures), which is significantly higher (p < 0.0238) compared to the absence of cutoff value (diagnostic sensitivity of 0%). No EUS-TA-related adverse events occurred. CONCLUSIONS: EUS-TA combined with SOSE for upper gastrointestinal SEL using a fork-tip needle had a high diagnostic rate, and the cutoff value of SVWC ≥ 4 mm had high diagnostic sensitivity.

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.

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). METHODS: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning-based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the compared models, SegNeXt-ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. CONCLUSION: This two-stage SegNeXt-ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.

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.

Clinical course of small gastric subepithelial lesion less than 20 mm diagnosed by endoscopic ultrasound-guided fine-needle aspiration.

BACKGROUND AND AIM: Gastrointestinal stromal tumors (GISTs) are treated as malignant gastric subepithelial lesions (SELs), and resection is recommended. However, small gastric SELs < 20 mm with no malignant features are monitored without histopathological examination, and the frequency of malignancy is unknown. This study aimed to clarify the clinicopathological findings and clinical course of gastric SELs < 20 mm measured by endoscopic ultrasound (EUS). METHODS: This retrospective cohort study included consecutive patients with small gastric SELs < 20 mm diagnosed using EUS at a tertiary referral center between 2009 and 2021. The clinical course after diagnosis using EUS-guided fine-needle aspiration (EUS-FNA) was reviewed. RESULTS: Among 333 patients with small gastric SELs, 104 patients with 105 lesions underwent EUS-FNA. The pathological diagnosis was confirmed in 87 patients. GISTs were the most common pathology (47%). Among the 87 patients, 43 underwent therapeutic interventions, including tumor resection and chemotherapy. In groups of tumor resection, the pathological tumor size on the resected specimen was significantly larger than the size measured by EUS (19.5 mm vs 15.0 mm, P < 0.001), and 37% of resected SELs were 20 mm or over. No recurrence was observed after tumor resection during a mean follow-up period of 40 months. CONCLUSIONS: Approximately 40% of small gastric SELs were malignant tumors, such as GIST, with most of them requiring treatment. Additionally, considering that the EUS measurement is 5 mm smaller than the pathological tumor diameter, further examinations, such as systematic EUS-FNA, may be required for SEL, including those smaller than 20 mm.