Comparison of Effective Imaging Modalities for Detecting Gastric Neoplasms: A Randomized 3-Arm Phase II Trial.

INTRODUCTION: The early detection of gastric neoplasms (GNs) leads to favorable treatment outcomes. The latest endoscopic system, EVIS X1, includes third-generation narrow-band imaging (3G-NBI), texture and color enhancement imaging (TXI), and high-definition white-light imaging (WLI). Therefore, this randomized phase II trial aimed to identify the most promising imaging modality for GN detection using 3G-NBI and TXI. METHODS: Patients with scheduled surveillance endoscopy after a history of esophageal cancer or GN or preoperative endoscopy for known esophageal cancer or GN were randomly assigned to the 3G-NBI, TXI, or WLI groups. Endoscopic observations were performed to detect new GN lesions, and all suspected lesions were biopsied. The primary endpoint was the GN detection rate during primary observation. Secondary endpoints were the rate of missed GNs, early gastric cancer detection rate, and positive predictive value for a GN diagnosis. The decision rule had a higher GN detection rate between 3G-NBI and TXI, outperforming WLI by >1.0%. RESULTS: Finally, 901 patients were enrolled and assigned to the 3G-NBI, TXI, and WLI groups (300, 300, and 301 patients, respectively). GN detection rates in the 3G-NBI, TXI, and WLI groups were 7.3, 5.0, and 5.6%, respectively. The rates of missed GNs were 1.0, 0.7, and 1.0%, the detection rates of early gastric cancer were 5.7, 4.0, and 5.6%, and the positive predictive values for the diagnosis of GN were 36.5, 21.3, and 36.8% in the 3G-NBI, TXI, and WLI groups, respectively. DISCUSSION: Compared with TXI and WLI, 3G-NBI is a more promising modality for GN detection.

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Fibronectin-Targeting Dual-Modal MR/NIRF Imaging Contrast Agents for Diagnosis of Gastric Cancer and Peritoneal Metastasis.

The prevalence and fatality rates of gastric cancer (GC) remain elevated, with advanced stages presenting a grim prognosis. Noninvasive diagnosis of GC cancer often proves challenging until the disease has progressed to an advanced stage or metastasized. Initially, the level of fibronectin (FN) in cancer-associated fibroblasts (CAFs) of GC was at least 3.7 times higher than that in normal fibroblasts. Herein, two FN-targeting magnetic resonance/near-infrared fluorescence (MR/NIRF) imaging contrast agents were developed to detect GC and peritoneal metastasis noninvasively. The probes CREKA-Cy7-(Gd-DOTA) and CREKA-Cy7-(Gd-DOTA)3 demonstrated significant FN-targeting capability (with dissociation constants of 1.0 and 2.1 mM) and effective MR imaging performance (with proton relaxivity values of 9.66 and 27.44 mM-1 s-1 at 9.4 T, 37 °C). In vivo imaging revealed a high signal-to-noise ratio and successful visualization of GC metastasis using NIRF imaging as well as successful tumor detection in MR imaging. Therefore, this study highlights the potential of FN-targeting probes for GC diagnosis and aids in the advancement of new diagnostic strategies for the clinical detection of GC.

Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis.

BACKGROUND: Prognosis prediction of patients with gastric cancer after neoadjuvant chemotherapy is suboptimal. This study aims to develop and validate a dynamic radiomic model for prognosis prediction of patients with gastric cancer on the basis of baseline and posttreatment features. PATIENTS AND METHODS: This single-center cohort study included patients with gastric adenocarcinoma treated with neoadjuvant chemotherapy from June 2009 to July 2015 in the Gastrointestinal Cancer Center of Peking University Cancer Hospital. Their clinicopathological data, pre-treatment and post-treatment computed tomography (CT) images, and pathological reports were retrieved and analyzed. Four prediction models were developed and validated using tenfold cross-validation, with death within 3 years as the outcome. Model discrimination was compared by the area under the curve (AUC). The final radiomic model was evaluated for calibration and clinical utility using Hosmer-Lemeshow tests and decision curve analysis. RESULTS: The study included 205 patients with gastric adenocarcinoma [166 (81%) male; mean age 59.9 (SD 10.3) years], with 71 (34.6%) deaths occurring within 3 years. The radiomic model alone demonstrated better discrimination than the pathological T stage (ypT) stage model alone (cross-validated AUC 0.598 versus 0.516, P = 0.009). The final radiomic model, which incorporated both radiomic and clinicopathological characteristics, had a significantly higher cross-validated AUC (0.769) than the ypT stage model (0.516), the radiomics alone model (0.598), and the ypT plus other clinicopathological characteristics model (0.738; all P < 0.05). Decision curve analysis confirmed the clinical utility of the final radiomic model. CONCLUSIONS: The developed radiomic model had good accuracy and could be used as a decision aid tool in clinical practice to differentiate prognosis of patients with gastric cancer.

18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). METHODS: A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. RESULTS: The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). CONCLUSIONS: The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS: We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS: The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS: In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.

Targeting Patient-Derived Orthotopic Gastric Cancers with a Fluorescent Humanized Anti-CEA Antibody.

BACKGROUND: Gastric cancer poses a major diagnostic and therapeutic challenge as surgical resection provides the only opportunity for a cure. Specific labeling of gastric cancer could distinguish resectable and nonresectable disease and facilitate an R0 resection, which could improve survival. METHODS: Two patient-derived gastric cancer lines, KG8 and KG10, were established from surgical specimens of two patients who underwent gastrectomy for gastric adenocarcinoma. Harvested tumor fragments were implanted into the greater curvature of the stomach to establish patient-derived orthotopic xenograft (PDOX) models. M5A (humanized anti-CEA antibody) or IgG control antibodies were conjugated with the near-infrared dye IRDye800CW. Mice received 50 µg of M5A-IR800 or 50 µg of IgG-IR800 intravenously and were imaged after 72 hr. Fluorescence imaging was performed by using the LI-COR Pearl Imaging System. A tumor-to-background ratio (TBR) was calculated by dividing the mean fluorescence intensity of the tumor versus adjacent stomach tissue. RESULTS: M5A-IR800 administration resulted in bright labeling of both KG8 and K10 tumors. In the KG8 PDOX models, the TBR for M5A-IR800 was 5.85 (SE ± 1.64) compared with IgG-IR800 at 0.70 (SE ± 0.17). The K10 PDOX models had a TBR of 3.71 (SE ± 0.73) for M5A-IR800 compared with 0.66 (SE ± 0.12) for IgG-IR800. CONCLUSIONS: Humanized anti-CEA (M5A) antibodies conjugated to fluorescent dyes provide bright and specific labeling of gastric cancer PDOX models. This tumor-specific fluorescent antibody is a promising potential clinical tool to detect the extent of disease for the determination of resectability as well as to visualize tumor margins during gastric cancer resection.

Impact of 18FFDG-PET/CT and Laparoscopy in Staging of Locally Advanced Gastric Cancer: A Cost Analysis in the Prospective Multicenter PLASTIC-Study.

BACKGROUND: Unnecessary D2-gastrectomy and associated costs can be prevented after detecting non-curable gastric cancer, but impact of staging on treatment costs is unclear. This study determined the cost impact of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18FFDG-PET/CT) and staging laparoscopy (SL) in gastric cancer staging. MATERIALS AND METHODS: In this cost analysis, four staging strategies were modeled in a decision tree: (1) 18FFDG-PET/CT first, then SL, (2) SL only, (3) 18FFDG-PET/CT only, and (4) neither SL nor 18FFDG-PET/CT. Costs were assessed on the basis of the prospective PLASTIC-study, which evaluated adding 18FFDG-PET/CT and SL to staging advanced gastric cancer (cT3-4 and/or cN+) in 18 Dutch hospitals. The Dutch Healthcare Authority provided 18FFDG-PET/CT unit costs. SL unit costs were calculated bottom-up. Gastrectomy-associated costs were collected with hospital claim data until 30 days postoperatively. Uncertainty was assessed in a probabilistic sensitivity analysis (1000 iterations). RESULTS: 18FFDG-PET/CT costs were €1104 including biopsy/cytology. Bottom-up calculations totaled €1537 per SL. D2-gastrectomy costs were €19,308. Total costs per patient were €18,137 for strategy 1, €17,079 for strategy 2, and €19,805 for strategy 3. If all patients undergo gastrectomy, total costs were €18,959 per patient (strategy 4). Performing SL only reduced costs by €1880 per patient. Adding 18FFDG-PET/CT to SL increased costs by €1058 per patient; IQR €870-1253 in the sensitivity analysis. CONCLUSIONS: For advanced gastric cancer, performing SL resulted in substantial cost savings by reducing unnecessary gastrectomies. In contrast, routine 18FFDG-PET/CT increased costs without substantially reducing unnecessary gastrectomies, and is not recommended due to limited impact with major costs. TRIAL REGISTRATION: NCT03208621. This trial was registered prospectively on 30-06-2017.

Larger Tumor Size and Elevated Serum Chromogranin A Levels Predict Metastatic Disease on DOTATATE Imaging in Patients with Gastroenteropancreatic Neuroendocrine Tumors.

PURPOSE: DOTATATE PET/CT (DOTATATE) is superior to conventional imaging in detecting metastasis for gastroenteropancreatic neuroendocrine tumors (GEP-NETs). However, limited availability, high-cost, and additive radiation exposure necessitate guidelines for its use. This study seeks to investigate the relationship between clinical characteristics and metastasis on DOTATATE. METHODS: This was a retrospective analysis of 815 patients who underwent DOTATATE at UCLA from 2014 to 2022. After applying inclusion and exclusion criteria, the study cohort consisted of 163 patients with pathologically diagnosed GEP-NETs, who either underwent primary tumor resection within 1-year prior, or had not undergone resection at the time of DOTATATE imaging. The presence of metastasis was determined using DOTATATE. Fisher's exact test, chi-squared test, and Mann-Whitney test were conducted to compare intergroup difference. Multivariate analysis was performed to identify clinical characteristics associated with metastasis on DOTATATE. RESULTS: Of patients with GEP-NETs, 40.5% (n = 66) were diagnosed with metastases by using DOTATATE. Those with metastatic disease were more likely to exhibit a larger primary tumor size (median 3.4 vs. 1.2, cm, P < 0.001), elevated serum chromogranin A level (CgA, median 208 vs. 97, mg/ml, P = 0.005), and higher tumor grade (P < 0.001). Primary tumor size ≥2 cm and serum CgA level ≥150 ng/mL for metastatic disease had a sensitivity and specificity of 64% and 89%, and 72% and 59%, respectively. Multivariate analysis demonstrated that primary tumor size (≥2/<2, cm, odds ratio [OR] 47.90, P < 0.001), tumor functionality (functional/nonfunctional, adjusted OR 10.17 P = 0.008), serum CgA level (≥150/<150, ng/ml, OR 6.25, P = 0.005), and tumor grade G2 (G2/G1, OR 9.6, P < 0.001) were independently associated with metastases on DOTATATE. CONCLUSIONS: Among patients with GEP-NETs, primary tumor size ≥2 cm, serum CgA level ≥150 ng/mL, and tumor grade G2 are associated with an increased risk of metastases on DOTATATE, and these predictors may be helpful to identify patients where DOTATATE is indicated for complete staging.

CXCR4 PET/MRI for follow-up of gastric mucosa-associated lymphoid tissue lymphoma after first-line Helicobacter pylori eradication.

Posttreatment evaluation of gastric mucosa-associated lymphoid tissue (MALT) lymphoma currently relies on esophagogastroduodenoscopy with histological assessment of biopsies. Overexpression of the G protein-coupled C-X-C chemokine receptor type 4 (CXCR4) has been previously observed in MALT lymphoma. The aim of this prospective study was to evaluate positron emission tomography (PET) with the novel CXCR4 tracer [68Ga]Pentixafor as a potential alternative to follow up biopsies for assessment of residual disease (noncomplete remission [CR]) after first-line Helicobacter pylori eradication. Forty-six post-H pylori eradication [68Ga]Pentixafor-PET/magnetic resonance imaging (MRI) examinations of 26 gastric MALT lymphoma patients, and 20 [68Ga]Pentixafor-PET/MRI examinations of 20 control group patients without lymphoma, were analyzed. In the MALT lymphoma group, time-matched gastric biopsies were used as reference standard and showed CR in 6 cases. Pooled examination-based accuracy, sensitivity, specificity, and positive and negative predictive values of [68Ga]Pentixafor-PET for detection of residual gastric MALT lymphoma at follow-up were 97.0%, 95.0%, 100.0%, 100.0%, and 92.9%, respectively. Maximum and mean PET standardized uptake values showed moderate correlation with immunohistochemistry-based CXCR4+ cell counts, with correlation coefficients of r = 0.51 and r = 0.52 (P = .008 and P = .006). In summary, CXCR4 imaging with [68Ga]Pentixafor-PET may represent a promising test for assessment of residual gastric MALT lymphomas after H pylori eradication.

[68Ga]Ga-FAPI-04 PET/MR imaging strategy in management of Krukenberg tumors (KTs) from gastric signet-ring-cell carcinoma: to overcome limitation of [68Ga]Ga-FAPI-04 PET imaging in KTs.

PURPOSE: To compare performance of whole-body [68Ga]Ga-FAPI-04 and [18F]FDG PET imaging in the detection of Krukenberg tumors (KTs), primary site and extra-ovarian metastases of gastric signet-ring-cell carcinoma (GSRCC), and evaluate the value of [68Ga]Ga-FAPI-04 PET/MR imaging strategy and its potential impact on the management of KTs from GSRCC. METHODS: Twelve patients with twenty-three KTs from GSRCC, who underwent both [68Ga]Ga-FAPI-04 pelvic PET/MR and whole-body [68Ga]Ga-FAPI-04 and [18F]FDG PET imaging were retrospectively analyzed. [68Ga]Ga-FAPI-04 and [18F]FDG uptakes were compared by using Wilcoxon signed-rank test or paired t test. McNemar's test was used to compare lesion detectability between two modalities. Two-tailed P<0.05 was considered statistically significant. Immunohistochemistry staining was utilized to analyze the fibroblast activation protein (FAP) expression in KTs. RESULTS: A total of 12 patients with 23 KTs from GSRCC (8 synchronous and 4 metachronous) were evaluated. [68Ga]Ga-FAPI-04 was superior to [18F]FDG PET in detecting primary sites of GSRCC (100% [11/11] vs. 18.2% [2/11], p = 0.002), involved lymph nodes (90.9% [10/11] vs. 54.5% [6/11], p = 0.046) and peritoneal metastases (100% [12/12] vs. 41.7% [5/12], p = 0.008), with higher SUVmax and TBR (all p < 0.005). Both tracers had limited value in identifying KTs, with 100% false negative rate on [68Ga]Ga-FAPI-04 PET and a low detection rate of 8.7% on [18F]FDG PET. Fap immunohistochemistry showed negative or slight FAP expression in neoplastic signet ring cells and ovarian stroma. [68Ga]Ga-FAPI-04 PET/MR imaging strategy greatly improved the detection rate of Krukenberg tumors (87%, 20/23). After adding diffusion-weighted imaging (DWI), the detection rate was further improved (87.5% vs. 100%, p = 0.083). [68Ga]Ga-FAPI-04 PET/MR imaging strategy either upgraded TNM staging or changed treatment management in twelve patients. CONCLUSIONS: [68Ga]Ga-FAPI-04 PET outperformed [18F]FDG PET in detecting primary site and most extra-ovarian metastases of GSRCC, but both tracers had limited value in identifying Krukenberg tumors. Pelvis MRI should be applied to compensate the limitation of [68Ga]Ga-FAPI-04 PET imaging to identify Krukenberg tumours. The [68Ga]Ga-FAPI-04 PET/MR imaging strategy has the potential to impact treatment decisions for GSRCC patients with KTs.

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.

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.

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.

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 in predicting vascular invasion in gastric cancer based on enhanced CT: a preliminary study.

BACKGROUND: Vascular invasion (VI) is closely related to the metastasis, recurrence, prognosis, and treatment of gastric cancer. Currently, predicting VI preoperatively using traditional clinical examinations alone remains challenging. This study aims to explore the value of radiomics analysis based on preoperative enhanced CT images in predicting VI in gastric cancer. METHODS: We retrospectively analyzed 194 patients with gastric adenocarcinoma who underwent enhanced CT examination. Based on pathology analysis, patients were divided into the VI group (n = 43) and the non-VI group (n = 151). Radiomics features were extracted from arterial phase (AP) and portal venous phase (PP) CT images. The radiomics score (Rad-score) was then calculated. Prediction models based on image features, clinical factors, and a combination of both were constructed. The diagnostic efficiency and clinical usefulness of the models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: The combined prediction model included the Rad-score of AP, the Rad-score of PP, Ki-67, and Lauren classification. In the training group, the area under the curve (AUC) of the combined prediction model was 0.83 (95% CI 0.76-0.89), with a sensitivity of 64.52% and a specificity of 92.45%. In the validation group, the AUC was 0.80 (95% CI 0.67-0.89), with a sensitivity of 66.67% and a specificity of 88.89%. DCA indicated that the combined prediction model might have a greater net clinical benefit than the clinical model alone. CONCLUSION: The integrated models, incorporating enhanced CT radiomics features, Ki-67, and clinical factors, demonstrate significant predictive capability for VI. Moreover, the radiomics model has the potential to optimize personalized clinical treatment selection and patient prognosis assessment.

The staining results of early gastric cancer by indigo carmine chromoendoscopy associated with histological structure: a retrospective study.

BACKGROUND: At present, conventional endoscopy and chromoendoscopy using indigo carmine (IC) is a very useful method to determine the demarcation line (DL) of early gastric cancer lesions, but it is not suitable for all lesions. AIMS: This study aimed to determine the applicable conditions for IC chromoendoscopy. METHODS: We retrospectively evaluated 187 lesions in 181 patients who had an endoscopic diagnosis of EGC and were treated with endoscopic submucosal dissection (ESD). According to the existence of the DL between the lesion mucosa and normal mucosa with IC chromoendoscopy, the lesions were divided into two groups: clear group and unclear group. Clinicopathological characteristics were evaluated in each group. From January 2022 to March 2023, the postoperative pathological sections of 19 lesions (81 slices) in the clear group and 19 lesions (80 slices) in unclear group were scanned with high definition, and the crypt structure between the two groups was evaluated. RESULTS: There was no significant difference in clinical factors between the clear group and unclear group. There were significant differences in crypt area, crypt length, and crypt opening diameter between the two groups. In the clear group, there were significant differences in crypt area, crypt length, and crypt opening diameter between the normal area and cancer area, but there was no significant difference in the unclear group. CONCLUSIONS: The margins of lesions with fused or absent crypt structures, a small crypt area, a short crypt length, and a short crypt opening diameter can be easily determined with IC chromoendoscopy.

Molecular SPECT/CT Profiling of Claudin18.2 Expression In Vivo: Implication for Patients with Gastric Cancer.

Zolbetuximab (IMAB362), a monoclonal antibody targeting Claudin18.2 (CLDN 18.2), demonstrates a significant clinical benefit in patients with advanced gastroesophageal cancers. The noninvasive assessment of CLDN18.2 expression through molecular imaging offers a potential avenue for expedited monitoring and the stratification of patients into risk groups. This study elucidates that CLDN18.2 is expressed at a noteworthy frequency in primary gastric cancers and their metastases. The iodogen method was employed to label IMAB362 with 123I/131I. The results demonstrated the efficient and reproducible synthesis of 123I-IMAB362, with a specific binding affinity to CLDN18.2. Immuno-single-photon emission computed tomography (SPECT) imaging revealed the rapid accumulation of 123I-IMAB362 in gastric cancer xenografts at 12 h, remaining stable for 3 days in patient-derived tumor xenograft models. Additionally, tracer uptake of 123I-IMAB362 in MKN45 cells surpassed that in MKN28 cells at each time point, with tumor uptake correlating significantly with CLDN18.2 expression levels. Positron emission tomography/computed tomography imaging indicated that tumor uptake of 18F-FDG and the functional/viable tumor volume in the 131I-IMAB362 group were significantly lower than those in the 123I-IMAB362 group on day 7. In conclusion, 123I-IMAB362 immuno-SPECT imaging offers an effective method for direct, noninvasive, and whole-body quantitative assessment of tumor CLDN18.2 expression in vivo. This approach holds promise for accelerating the monitoring and stratification of patients with gastric cancer.

Preparation of Radiolabeled Zolbetuximab Targeting CLDN18.2 and Its Preliminary Evaluation for Potential Clinical Applications.

Claudin18.2 (CLDN18.2), due to its high expression in various gastric cancer tissues, is considered an optimal target for antitumor drug molecules. In this study, we obtained the labeled compounds of [125I]I-zolbetuximab using the Iodogen method. Under the optimum labeling conditions, the molar activity of [125I]I-zolbetuximab was 1.75 × 102 GBq/µmol, and the labeling efficiency was more than 99%. The labeled compounds exhibited excellent in vitro stability in both phosphate buffer saline (PBS, pH = 7.4) and fetal bovine serum systems (FBS) (radiochemical purity >90% at 72 h). The uptake percentage of [125I]I-zolbetuximab in MKN45-CLDN18.2 cells is 24.69 ± 0.84% after 6 h. The saturation binding assay and specificity assay further demonstrated the high specificity of [125I]I-zolbetuximab for CLDN18.2. The long retention at the tumor site and rapid metabolic clearance at other organ sites of [125I]I-zolbetuximab were observed in small-animal SPECT-CT imaging. The same trend was also observed in the biodistribution study. Due to the excellent targeting ability of zolbetuximab for CLDN18.2, [125I]I-zolbetuximab exhibits strong specific binding and retention with cells and tumors highly expressing CLDN18.2. However, the balance between mAb's longer cycle time in vivo and targeting binding and retention ability should be intensively considered for using this kind of radiopharmaceutical in the diagnosis and treatment of CLDN18.2-positive gastric cancer.

HER2 in Gastric Cancer: A Comprehensive Analysis Combining Meta-Analysis and DCE-MRI Radiomics.

OBJECTIVE: Advanced gastric cancer (AGC) is a severe malignant tumor, and overexpression of HER2/ERBB2 may play a crucial role in its development. The purpose of this study is to investigate the overexpression of HER2/ERBB2 in gastric cancer through a meta-analysis and examine its relationship with perfusion parameters using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technology. METHODS: We conducted an extensive literature search and collected relevant studies for the meta-analysis. We used a random-effects model and assigned weights using the "inverse variance" method. Additionally, we included 95 AGC patients diagnosed pathologically between April 2018 and October 2021. They all underwent DCE-MRI scans, and the data were subsequently analyzed using the Omni kinetic software. HER2 expression was assessed using immunohistochemistry. RESULTS: The meta-analysis revealed an overall odds ratio (OR) of .21 for HER2/ERBB2 overexpression in gastric cancer, with a 95% confidence interval of [.14, .30]. DCE-MRI results showed a significant association between high HER2 expression and poor tumor differentiation (P < .005). The extracellular volume fraction (Ecv) quantile, mean, relative deviation, median intensity, and difference entropy were significantly higher in the low HER2 expression group compared to the high HER2 expression group. Receiver operating characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) values of DCE-MRI radiomic parameters with significant differences were close to .7. CONCLUSION: Overexpression of HER2/ERBB2 in gastric cancer is significantly associated with certain radiomic parameters of DCE-MRI, providing a valuable diagnostic tool for clinical practice. Furthermore, the meta-analysis further confirmed the critical role of HER2/ERBB2 in the development of gastric cancer.