A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

BACKGROUND: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. METHODS: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. RESULTS: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. CONCLUSION: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

Endoscopic diagnosis of gastric and oesophageal cancer in Lusaka, Zambia: a retrospective analysis.

INTRODUCTION: There are uncertainties surrounding the spectrum of upper gastrointestinal (UGI) diseases in sub-Saharan Africa. This is mainly due to the limitations of data collection and recording. We previously reported an audit of UGI endoscopic diagnoses in Zambia spanning from 1977 to 2014. We now have extended this analysis to include subsequent years, in order to provide a more comprehensive picture of how the diagnoses have evolved over 4 decades. METHODS: We combined data collected from the endoscopy unit at the University Teaching Hospital (UTH) in Lusaka during a previous review with that collected from the beginning of 2015 to the end of 2021. Since 2015, an electronic data base of endoscopy reports at the UTH was kept. The electronic data base was composed of drop-down menus that allowed for standardised reporting of findings. Collected data were coded by two experienced endoscopists and analysed. RESULTS: In total, the analysis included 25,849 endoscopic records covering 43 years. The number of endoscopic procedures performed per year increased drastically in 2010. With the exception of the last 2 years, the proportion of normal endoscopies also increased during the time under review. In total, the number of gastric cancer (GC) cases was 658 (3%) while that of oesophageal cancer (OC) was 1168 (5%). The number of GC and OC diagnoses increased significantly over the period under review, (p < 0.001 for both). For OC the increase remained significant when analysed as a percentage of all procedures performed (p < 0.001). Gastric ulcers (GU) were diagnosed in 2095 (8%) cases, duodenal ulcers (DU) in 2276 (9%) cases and 239 (1%) had both ulcer types. DU diagnosis showed a significantly decreasing trend over each decade (p < 0.001) while GU followed an increasing trend (p < 0.001). CONCLUSIONS: UGI endoscopic findings in Lusaka, Zambia, have evolved over the past four decades with a significant increase of OC and GU diagnoses. Reasons for these observations are yet to be established.

Krukenburg tumour: Role of 18F-FDG PET/CT in identifying the primary site.

Krukenberg tumours are a rare form of metastatic tumours of the ovary. They primary site is usually the gastro-intestinal system with the most common being gastric cancer. We present the case of a 35-year-old female coming in with a large pelvi-abdominal mass for investigation. This pelvic mass showed mild to moderate metabolic activity. 18F-FDG PET-CT was able to identify the primary gastric carcinoma. Subsequent histopathology confirmed this to be gastric adenocarcinoma with metastases to the ovary.

68Ga-FAPI-04 positron emission tomography/CT and laparoscopy for the diagnosis of occult peritoneal metastasis in newly diagnosed locally advanced gastric cancer: study protocol of a single-centre prospective cohort study.

INTRODUCTION: Accurate baseline clinical staging is critical to inform treatment decision-making for patients with gastric cancers. Peritoneal metastasis (PM) is the most common form of metastasis in gastric cancer and mainly diagnosed by diagnostic laparoscopy and peritoneal lavage evaluation. However, diagnostic laparoscopy is invasive and less cost-effective. It is urgent to develop a safe, fast and non-invasive functional imaging method to verify the peritoneal metastasis of gastric cancer. The aim of our study was to evaluate the proportion of patients in whom 68Ga-FAPI-04 positron emission tomography/CT (PET/CT) led to a change in treatment strategy and to assess the diagnostic accuracy of 68Ga-FAPI-04 PET/CT for the detection of occult peritoneal metastasis compared with laparoscopic exploration. METHODS AND ANALYSIS: In this single-centre, prospective diagnostic test accuracy study, a total of 48 patients with locally advanced gastric or gastro-oesophageal junction adenocarcinoma (cT4a-b, N0-3, M0, based on CT images) who are considering radical tumour surgery will be recruited. All participants will undergo 68Ga-FAPI-04 PET/CT before the initiation of laparoscopic exploration. The primary outcome is the proportion of patients with occult peritoneal metastatic lesions detected by 68Ga-FAPI-04 PET/CT, leading to a change in therapy strategy. The secondary outcomes include the diagnostic performance of 68Ga-FAPI-04 PET/CT for occult peritoneal metastasis, including sensitivity, specificity, accuracy, positive predictive value and negative predictive value. ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (2022-1484). Study results will be presented at public and scientific conferences and in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ChiCTR2300067591.

Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images.

Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a "Bi-directional feature extraction and fusion module" and a "Purification module for feature channel and space" based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.

Diagnostic efficacy of contrast-enhanced gastric ultrasonography in staging gastric cancer: a meta-analysis.

BACKGROUND: As comprehensive surgical management for gastric cancer becomes increasingly specialized and standardized, the precise differentiation between ≤T1 and ≥T2 gastric cancer before endoscopic intervention holds paramount clinical significance. OBJECTIVE: To evaluate the diagnostic efficacy of contrast-enhanced gastric ultrasonography in differentiating ≤T1 and ≥T2 gastric cancer. METHODS: PubMed, Web of Science, and Medline were searched to collect studies published from January 1, 2000 to March 16, 2023 on the efficacy of either double contrast-enhanced gastric ultrasonography (D-CEGUS) or oral contrast-enhanced gastric ultrasonography (O-CEGUS) in determining T-stage in gastric cancer. The articles were selected according to specified inclusion and exclusion criteria, and the quality of the included literature was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 scale. Meta-analysis was performed using Stata 12 software with data from the 2 × 2 crosslinked tables in the included literature. RESULTS: In total, 11 papers with 1124 patients were included in the O-CEGUS analysis, which revealed a combined sensitivity of 0.822 (95% confidence interval [CI] = 0.753-0.875), combined specificity of 0.964 (95% CI = 0.925-0.983), and area under the summary receiver operating characteristic (sROC) curve (AUC) of 0.92 (95% CI = 0.89-0.94). In addition, five studies involving 536 patients were included in the D-CEGUS analysis, which gave a combined sensitivity of 0.733 (95% CI = 0.550-0.860), combined specificity of 0.982 (95% CI = 0.936-0.995), and AUC of 0.93 (95% CI = 0.91-0.95). According to the I2 and P values ​​of the forest plot, there was obvious heterogeneity in the combined specificities of the included papers. Therefore, the two studies with the lowest specificities were excluded from the O-CEGUS and D-CEGUS analyses, which eliminated the heterogeneity among the remaining literature. Consequently, the combined sensitivity and specificity of the remaining studies were 0.794 (95% CI = 0.710-0.859) and 0.976 (95% CI = 0.962-0.985), respectively, for the O-CEDUS studies and 0.765 (95% CI = 0.543-0.899) and 0.986 (95% CI = 0.967-0.994), respectively, for the D-CEGUS studies. The AUCs were 0.98 and 0.99 for O-CEGUS and D-CEGUS studies, respectively. CONCLUSION: Both O-CEGUS and D-CEGUS can differentiate ≤T1 gastric cancer from ≥T2 gastric cancer, thus assisting the formulation of clinical treatment strategies for patients with very early gastric cancer. Given its simplicity and cost-effectiveness, O-CEGUS is often favored as a staging method for gastric cancer prior to endoscopic intervention.

Ovarian serous carcinoma with stomach metastasis: a rare case report and literature review.

Ovarian cancer is a common tumor among women. It is often asymptomatic in the early stages, with most cases already at stage III to IVE at the time of diagnosis. Direct spread and lymphatic metastasis are the primary modes of metastasis, whereas hematogenous spread is rare. An initial diagnosis of ovarian cancer that has metastasized to the stomach is also uncommon. Therefore, clear treatment methods and prognostic data for such metastasis are lacking. In our hospital, we encountered a patient with an initial imaging diagnosis of a gastric tumor and a history of an ovarian tumor with endoscopic abdominal metastasis. Based on the characteristics of the case, the two tumors were considered to be the same. After chemotherapy, a partial response was observed in the stomach and pelvic lesions, suggesting the effectiveness of the treatment. Through three treatments of recurrence, gastroscopy confirmed the stomach to be a metastatic site. Therefore, determining the primary source of advanced tumors is crucial in guiding treatment decisions. Clinicians must approach this comprehensively, relying on thorough evaluation and personal experience.

Exploring non-curative endoscopic submucosal dissection: Current treatment optimization and future indication expansion.

The increasing popularity of endoscopic submucosal dissection (ESD) as a treatment for early gastric cancer has highlighted the importance of quality assessment in achieving curative resections. This article emphasizes the significance of evaluating ESD quality, not only for curative cases but also for non-curative ones. Postoperative assessment relies on the endoscopic curability (eCura) classification, but management strategies for eCuraC-1 tumour with a positive horizontal margin are unclear. Current research primarily focuses on comparing additional surgical procedures in high-risk patients, while studies specifically targeting eCuraC-1 patients are limited. Exploring management strategies and follow-up outcomes for such cases could provide valuable insights. Furthermore, the application of molecular imaging using near-infrared fluorescent tracers holds promise for precise tumour diagnosis and navigation, potentially impacting the management of early-stage gastric cancer patients. Advancing research in these areas is essential for improving the overall efficacy of endoscopic techniques and refining treatment indications.

Endoscopic features and treatments of gastric cystica profunda.

BACKGROUND: Gastric cystica profunda (GCP) represents a rare condition characterized by cystic dilation of gastric glands within the mucosal and/or submucosal layers. GCP is often linked to, or may progress into, early gastric cancer (EGC). AIM: To provide a comprehensive evaluation of the endoscopic features of GCP while assessing the efficacy of endoscopic treatment, thereby offering guidance for diagnosis and treatment. METHODS: This retrospective study involved 104 patients with GCP who underwent endoscopic resection. Alongside demographic and clinical data, regular patient follow-ups were conducted to assess local recurrence. RESULTS: Among the 104 patients diagnosed with GCP who underwent endoscopic resection, 12.5% had a history of previous gastric procedures. The primary site predominantly affected was the cardia (38.5%, n = 40). GCP commonly exhibited intraluminal growth (99%), regular presentation (74.0%), and ulcerative mucosa (61.5%). The leading endoscopic feature was the mucosal lesion type (59.6%, n = 62). The average maximum diameter was 20.9 ± 15.3 mm, with mucosal involvement in 60.6% (n = 63). Procedures lasted 73.9 ± 57.5 min, achieving complete resection in 91.3% (n = 95). Recurrence (4.8%) was managed via either surgical intervention (n = 1) or through endoscopic resection (n = 4). Final pathology confirmed that 59.6% of GCP cases were associated with EGC. Univariate analysis indicated that elderly males were more susceptible to GCP associated with EGC. Conversely, multivariate analysis identified lesion morphology and endoscopic features as significant risk factors. Survival analysis demonstrated no statistically significant difference in recurrence between GCP with and without EGC (P = 0.72). CONCLUSION: The findings suggested that endoscopic resection might serve as an effective and minimally invasive treatment for GCP with or without EGC.

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Superior lymph node harvest by fluorescent lymphography during minimally invasive gastrectomy for gastric cancer patients with high body mass index.

BACKGROUND: Fluorescent lymphography (FL) using indocyanine green (ICG) allows for the visualization of all draining lymph nodes (LNs), thereby increasing LN retrieval. However, no studies have assessed the efficacy of FL in high body mass index (BMI) gastric cancer patients, even as LN yield decreases with increasing BMI in gastrectomy. This study aimed to investigate the influence of FL on LN retrieval in high BMI gastric cancer patients. METHODS: Gastric cancer patients who underwent laparoscopic or robotic gastrectomies from 2013 to 2021 were included. Patients were classified into two groups, with FL (FL group) or without FL (non-FL group). The effect of FL on LN retrieval was assessed by BMI. Inverse probability of treatment weighting (IPTW) was used to ensure comparability between groups. RESULTS: Retrieved LN number decreased as BMI increased regardless of FL application (P < 0.001). According to the IPTW analysis, the mean retrieved LN number was significantly higher in the FL group (48.4 ± 18.5) than in the non-FL group (39.8 ± 16.3, P < 0.001), irrespective of BMI. The FL group exhibited a significantly higher proportion of patients with 16 or more LNs (99.5%) than the non-FL group (98.1%, P < 0.001). The FL group also had a significantly higher proportion of patients with 30 or more LNs (86.6%) than the non-FL group (72.2%, P < 0.001). In both the normal and high-BMI patients, the FL group had a significantly larger percentage of patients with a higher nodal classification than the non-FL group. CONCLUSION: FL resulted in more LN retrieval, even in high BMI patients. FL ensures accurate staging by maintaining the appropriate retrieved LN number in high BMI gastric cancer patients.

Correlation of CT parameters and PD-L1 expression status in gastric cancer.

OBJECTIVE: We aimed to explore the correlation between routine computed tomography (CT) imaging features and programmed cell death ligand-1(PD-L1) expression status in gastric cancer and evaluate the predictive value of imaging parameters for this immunotherapy biomarker. MATERIALS AND METHODS: Patients with gastric adenocarcinoma who underwent abdominal CT three-stage enhanced scan and PD-L1 immunohistochemical testing before treatment were retrospectively examined. All diagnoses were confirmed through pathology. According to the expression status of PD-L1, they were divided into the positive (CPS ≥ 5) or negative group (CPS < 5). Baseline CT imaging features were collected. Diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve. RESULTS: In total, 67 patients (17 women and 50 men; mean age: 59.55 ± 10.22 years) with gastric adenocarcinoma were included in the study. The overall stages, probability of maximum lymph node short diameter > 1 cm and peak of lesion enhancement occurring in the arterial phase were statistically significant between the two groups (p < 0.05). Moreover, the arterial enhancement fraction (AEF) was significantly higher in the positive group than that in the negative group (p < 0.05), and ROC curve analysis showed that the AEF exhibited a high evaluation efficacy (area under the curve [AUC] = 0.724 [95% confidence interval (CI): 0.602-0.826]). The combined parameters had the best diagnostic efficacy (AUC = 0.825 [95%CI: 0.716-0.933]), sensitivity (75.00%), and specificity (81.40%). CONCLUSIONS: These findings confirm a correlation between CT imaging features and PD-L1 expression status in gastric cancer, and AEF may help evaluate high PD-L1 expression and select patients suitable for immunotherapy.

Curative criteria for endoscopic treatment of gastric cancer.

Endoscopic treatment, particularly endoscopic submucosal dissection, has become the primary treatment for early gastric cancer. A comprehensive optical assessment, including white light endoscopy, image-enhanced endoscopy, and magnification, are the cornerstones for clinical staging and determining the resectability of lesions. This paper discusses factors that influence the indication for endoscopic resection and the likelihood of achieving a curative resection. Our review stresses the critical need for interpreting the histopathological report in accordance with clinical guidelines and the imperative of tailoring decisions based on the patients' and lesions' characteristics and preferences. Moreover, we offer guidance on managing complex scenarios, such as those involving non-curative resection. Finally, we identify future research avenues, including the role of artificial intelligence in estimating the depth of invasion and the urgent need to refine predictive scores for lymph node metastasis and metachronous lesions.

Multimodal management of foregut neuroendocrine neoplasms.

The foregut, which includes the esophagus, stomach and duodenum, represents one of the most common sites for neuroendocrine neoplasms. These are highly heterogenous with different risk of progression depending on location, cell-type of origin, size, grade and other factors. Various endoscopic and imaging modalities exist to inform therapeutic decision-making, which may be in the form of surgical or endoscopic resection and medical therapy depending on the extent of the disease after diagnostic evaluation. This narrative review aims to explore the literature on the multimodal management of such foregut neuroendocrine neoplasms.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning.

BACKGROUND: Lymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients. AIM: To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately. METHODS: This study used a retrospective dataset involving 257 GC patients (training cohort, n = 172; validation cohort, n = 85). First, several clinical indicators, including serum tumor markers, CT-TN stages and CT-detected extramural vein invasion (CT-EMVI), were extracted, as were quantitative spectral CT parameters from the delineated tumor regions. Next, a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters. A logistic regression (LR)-based nomogram model was subsequently constructed to predict LVI/PNI status, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: In both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant (P < 0.05). LR analysis of the training group showed preoperative CT-T stage, CT-EMVI, single-energy CT values of 70 keV of venous phase (VP-70 keV), and the ratio of standardized iodine concentration of equilibrium phase (EP-NIC) were independent influencing factors. The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824, respectively, which were slightly greater than those of CT-T and CT-EMVI (AUC = 0.793, 0.762). The nomogram combining CT-T stage, CT-EMVI, VP-70 keV and EP-NIC yielded AUCs of 0.918 (0.866-0.954) and 0.874 (0.784-0.936) in the training and validation cohorts, which are significantly higher than using each of single independent factors (P < 0.05). CONCLUSION: The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC, with accuracy boosted by integrating clinical markers.

Gastric point-of-care ultrasonography in patients undergoing radical gastrointestinal surgery before anesthetic induction: an observational cohort study.

BACKGROUND: Pulmonary aspiration of gastric contents is a serious perioperative complication. Patients with gastric cancer may experience delayed gastric emptying. However, the role of qualitative and quantitative gastric ultrasound assessments in this patient population before anesthesia induction has not yet been determined. METHODS: Adult patients with gastrointestinal cancer were recruited and examined using gastric point-of-care ultrasound (POCUS) before anesthetic induction from March 2023 to August 2023 in a tertiary cancer center. Three hundred patients with gastric cancer were conducted with POCUS prior to induction, and three hundred patients with colorectal cancer were included as controls. The cross-sectional area (CSA) of the gastric antrum and gastric volumes (GV) were measured and calculated. We determined the nature of the gastric contents and classified the antrum using a 3-point grading system. A ratio of GV to body weight > 1.5mL/Kg was defined as a high risk of aspiration. RESULTS: In patients with gastric cancer, 70 patients were classified as grade 2 (23%, including 6 patients with solid gastric contents) and 63 patients (21%) were identified as having a high risk of aspiration. Whereas in patients with colorectal cancer, only 11 patients were classified as grade 2 (3.7%), and 27 patients (9.7%) were identified as having a high risk of aspiration. A larger tumor size (OR:1.169, 95% CI 1.045-1.307, P = 0.006), tumor located in antrum (OR:2.304, 95% CI 1.169-4.539,P = 0.016), gastrointestinal obstruction (OR:21.633, 95% CI 4.199-111.443, P < 0.0001) and more lymph node metastasis (OR:2.261, 95% CI 1.062-4.812, P = 0.034) were found to be positively while tumor site at cardia (OR:0.096, 95% CI 0.019-0.464, P = 0.004) was negatively associated with high aspiration risk in patients with gastric cancer. CONCLUSION: The Gastric POCUS prior to induction provides an assessment of the status of gastric emptying and can identify the patients at high risk of aspiration, especially those with gastric cancer. TRIAL REGISTRATION: Chinese Clinical Trial Registry ( www.chictr.org.cn ) identifier: ChiCTR2300069242; registered 10 March 2023.

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.