Explore the vast range of titles available.

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.

Objectives To establish and validate a radiomics nomogram model for preoperative prediction of KIT exon 9 mutation status in patients with gastrointestinal stromal tumors (GISTs). Materials and methods Eighty-seven patients with pathologically confirmed GISTs were retrospectively enrolled in this study. Imaging and clinicopathological data were collected and randomly assigned to the training set (n = 60) and test set (n = 27) at a ratio of 7:3. Based on contrast-enhanced CT (CE-CT) arterial and venous phase images, the region of interest (ROI) of the tumors were manually drawn layer by layer, and the radiomics features were extracted. The intra-class correlation coefficient (ICC) was used to test the consistency between observers. Least absolute shrinkage and selection operator regression (LASSO) were used to further screen the features. The nomogram of integrated radiomics score (Rad-Score) and clinical risk factors (extra-gastric location and distant metastasis) was drawn on the basis of multivariate logistic regression. The area under the receiver operating characteristic (AUC) curve and decision curve analysis were used to evaluate the predictive efficiency of the nomogram, and the clinical benefits that the decision curve evaluation model may bring to patients. Results The selected radiomics features (arterial phase and venous phase features) were significantly correlated with the KIT exon 9 mutation status of GISTs. The AUC, sensitivity, specificity, and accuracy in the radiomics model were 0.863, 85.7%, 80.4%, and 85.0% for the training group (95% confidence interval [CI]: 0.750-0.938), and 0.883, 88.9%, 83.3%, and 81.5% for the test group (95% CI: 0.701-0.974), respectively. The AUC, sensitivity, specificity, and accuracy in the nomogram model were 0.902 (95% confidence interval [CI]: 0.798-0.964), 85.7%, 86.9%, and 91.7% for the training group, and 0.907 (95% CI: 0.732-0.984), 77.8%, 94.4%, and 88.9% for the test group, respectively. The decision curve showed the clinical application value of the radiomic nomogram. Conclusion The radiomics nomogram model based on CE-CT can effectively predict the KIT exon 9 mutation status of GISTs and may be used for selective gene analysis in the future, which is of great significance for the accurate treatment of GISTs.

Introduction Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal soft tissue tumor. Clinical diagnosis mainly relies on enhanced CT, endoscopy and endoscopic ultrasound (EUS), but the misdiagnosis rate is still high without fine needle aspiration biopsy. We aim to develop a novel diagnostic model by analyzing the preoperative data of the patients. Methods We used the data of patients who were initially diagnosed as gastric GIST and underwent partial gastrectomy. The patients were randomly divided into training dataset and test dataset at a ratio of 3 to 1. After pre-experimental screening, max depth = 2, eta = 0.1, gamma = 0.5, and nrounds = 200 were defined as the best parameters, and in this way we developed the initial extreme gradient-boosting (XGBoost) model. Based on the importance of the features in the initial model, we improved the model by excluding the hematological features. In this way we obtained the final XGBoost model and underwent validation using the test dataset. Results In the initial XGBoost model, we found that the hematological indicators (including inflammation and nutritional indicators) examined before the surgery had little effect on the outcome, so we subsequently excluded the hematological indicators. Similarly, we also screened the features from enhanced CT and ultrasound gastroscopy, and finally determined the 6 most important predictors for GIST diagnosis, including the ratio of long and short diameter under CT, the CT value of the tumor, the enhancement of the tumor in arterial period and venous period, existence of liquid area and calcific area inside the tumor under EUS. Round or round-like tumors with a CT value of around 30 (25–37) and delayed enhancement, as well as liquid but not calcific area inside the tumor best indicate the diagnosis of GIST. Conclusions We developed a model to further differential diagnose GIST from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results of clinical examinations that most patients should have completed before surgical resection.

Gastrointestinal stromal tumor (GIST) is the most common sarcoma arising in the gastrointestinal (GI) tract. Imatinib mesylate (imatinib) is efficacious in treating advanced and metastatic GIST. Patients undergoing resection of GIST realize a highly variable median disease-free survival (DFS). In the absence of prospective data, we conducted a randomized, phase II study to assess the safety and efficacy of preoperative and postoperative imatinib for the treatment of GIST. Nineteen GIST patients undergoing surgical resection were randomized to receive 3, 5, or 7 days of preoperative imatinib (600 mg daily). Patients received postoperative imatinib for 2 years. Perioperative adverse events were compared with those in an imatinib-naïve historical control. The efficacy of imatinib was assessed by 18 fluorodeoxyglucose positron emission tomography ( 18 FDG-PET), dynamic computed tomography (dCT), terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, and DFS. Imatinib did not affect surgical morbidity as compared with an imatinib-naïve cohort ( p  ≥ 0.1). Most patients responded to preoperative imatinib by 18 FDG-PET and dCT (69% and 71%, respectively). Tumor cell apoptosis increased by an average of 12% (range 0–33%) and correlated with the duration of preoperative imatinib ( p  = 0.04). Median DFS of patients treated with surgery and imatinib was 46 months (range 10–46 months). Tumor size was a predictor of recurrence after postoperative imatinib ( p  = 0.02). Imatinib appears to be safe and may be considered for patients undergoing surgical resection of their GIST. Radiographic response and tumor cell apoptosis occur within the first week of imatinib therapy.

Background Gastrointestinal subepithelial lesions (SELs) range from benign to malignant. Endoscopic ultrasound (EUS)-guided fine-needle biopsy (EUS-FNB) is used widely for pathological diagnosis of SELs. Early diagnosis and treatment are important because all Gastrointestinal stromal tumors (GISTs) have some degree of malignant potential. Diagnosing SELs with EUS-FNB is more difficult than diagnosing other tumors because an accurate diagnosis of GIST requires a sufficient tissue sample for immunostaining, which is part of the diagnostic protocol. Moreover, EUS-FNB is less accurate for diagnosis based on samples from SELs measuring ≤ 2 cm. However, our retrospective study showed that more than 50% of patients with SELs ≤ 2 cm were diagnosed as GIST. Therefore, EUS-FNB needles are required with adequate sampling in SELs measuring ≤ 2 cm. Previously, we conducted a retrospective single-center study of SELs measuring ≤ 2 cm, and reported that EUS-FNB with a Fork-tip needle was superior to that with a Franseen needle in that the former acquires sufficient sample. This multicenter comparative open-label superiority study is designed to verify whether a 22G Fork-tip needle is superior to a 22G Franseen needle with respect to sample acquisition. Methods/design Present study will randomly assign for 110 patients (55 in the Fork-tip needle group and 55 in the Franseen needle group) with SELs measuring ≤ 2 cm, all of whom are managed at one of the 10 participating endoscopic centers. The primary endpoint evaluates the superiority of a 22G Fork-tip needle over a 22G Franseen needle for collection of an adequate tissue specimen at the first puncture. The secondary endpoints compare successful puncture rate, procedure completion rate, number of adverse events, diagnostic suitability of the first puncture specimen for GIST, and the number of punctures required until adequate specimen collection. Discussion The outcomes may provide insight into the optimal needle choice for diagnosis of SELs ≤ 2 cm, thereby aiding development of practice guidelines. Present study is expected to promote early definitive diagnosis of GISTs, thereby increasing the number of cases that can receive curative treatment and improving prognosis. Trial registration Japan Registry of Clinical Trials (JRCT; trial registration: jRCTs052230144). Registered December 13, 2023. (URL; https://jrct.niph.go.jp/re/reports/detail/76858 ).

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.

Subepithelial lesions (SEL) of the GI tract represent a mix of benign and potentially malignant entities including tumors, cysts, or extraluminal structures causing extrinsic compression of the gastrointestinal wall. SEL can occur anywhere along the GI tract and are frequently incidental findings encountered during endoscopy or cross-sectional imaging. This clinical guideline of the American College of Gastroenterology was developed using the Grading of Recommendations Assessment, Development, and Evaluation process and is intended to suggest preferable approaches to a typical patient with a SEL based on the currently available published literature. Among the recommendations, we suggest endoscopic ultrasound (EUS) with tissue acquisition to improve diagnostic accuracy in the identification of solid nonlipomatous SEL and EUS fine-needle biopsy alone or EUS fine-needle aspiration with rapid on-site evaluation sampling of solid SEL. There is insufficient evidence to recommend surveillance vs resection of gastric gastrointestinal stromal tumors (GIST) <2 cm in size. Owing to their malignant potential, we suggest resection of gastric GIST >2 cm and all nongastric GIST. When exercising clinical judgment, particularly when statements are conditional suggestions and/or treatments pose significant risks, health-care providers should incorporate this guideline with patient-specific preferences, medical comorbidities, and overall health status to arrive at a patient-centered approach.

Background Indocyanine green (ICG)-fluorescence imaging has been developed for real-time identification of hepatic tumors and segmental boundaries during hepatectomy. Fusion ICG-fluorescence imaging (real-time visualization of pseudocolor-fluorescence signals on white-light color images) may serve as a reliable navigation tool especially in laparoscopic hepatectomy, in which gross inspection and palpation are limited. Methods The study population consisted of 41 patients undergoing laparoscopic hepatectomy. Hepatic tumors were identified by fluorescence imaging following the preoperative intravenous administration of ICG (0.5 mg/kg body weight). To visualize hepatic perfusion and segmental boundaries, ICG (1.25 mg) was injected intravenously during surgery, following closure of the proximal portal pedicle. A laparoscopic imaging system, which enabled superimposition of the pseudocolor-fluorescence images on white color images, was used for the fusion ICG-fluorescence imaging. Results Among the 53 malignant tumors resected, fusion ICG-fluorescence imaging revealed 45 nodules (85%), including three nodules of colorectal liver metastasis unidentifiable by white-light color images or intraoperative ultrasonography. It also delineated the segmental boundaries on the hepatic raw surfaces as well as on the phrenic/visceral surfaces in all 12 patients evaluated using this technique. Conclusions Fusion imaging enhances the feasibility of intraoperative ICG-fluorescence imaging in the identification of hepatic tumors and segmental boundaries. It may therefore help surgeons in the safe and accurate completion of laparoscopic hepatectomies.

Background and AimsEndoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images.MethodsPubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy.ResultsOverall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89–0.95), 0.82 (95% CI 0.75–0.87), 4.55 (95% CI 2.64–7.84), and 0.12 (95% CI 0.07–0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83–175.69) and 0.950 (Q* = 0.891).ConclusionsAI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future.

BackgroundTumor 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.MethodsA 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.ResultsThe 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%.ConclusionsQuantifying 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.