HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS: In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS: Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS: Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

Feasibility and efficacy of endoscopy with blue laser imaging for the detection and diagnosis of cardia polyps: A single-center randomized controlled study.

OBJECTIVE: To compare the detection rate and diagnostic accuracy of cardia polyps using endoscopy with blue laser imaging (BLI) and white-light imaging (WLI). METHODS: Patients were randomly divided into the BLI group and WLI group according to the endoscopic procedures. BLI followed by WLI was conducted in the BLI group, whereas WLI followed by BLI examination was conducted in the WLI group. The number, size, microstructure, and microvascular patterns of cardia polyps detected were recorded. Biopsy of the polyps was then performed. RESULTS: The detection rate of cardia polyps in the BLI group was higher than that in the WLI group (7.87% vs 4.22%, P = 0.018). The rate of overlooked lesions in the BLI group was lower than in the WLI group (0.64% vs 3.38%, P = 0.003). The diagnostic coincidence rate between magnifying BLI and histopathology was 88.16%. The sensitivity, specificity, positive predictive value and negative predictive value for the diagnosis of neoplastic lesions by magnifying endoscopy with BLI were 90.91%, 87.69%, 55.56%, and 98.28%, respectively. The most remarkable patterns for predicting inflammatory polyps were the prolonged and fine network patterns (sensitivity 71.43%, specificity 93.75%). Small round combined with honeycomb patterns were the most common among fundic gland polyps (sensitivity 80.00%, specificity 98.48%). Neoplastic lesions presented as villous or ridge-like combined with core vascular or unclear pattern for both microvascular and microstructure patterns. CONCLUSION: BLI is more effective than WLI in the detection and diagnosis of cardia polyps, and magnifying endoscopy with BLI may help diagnose such lesions.

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.

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.

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.

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.

Head to head comparison of 68Ga-FAPI PET/CT with 18F-FDG PET/CT in primary and metastatic lesions of gastric tumor: A systematic review and meta-analysis.

OBJECTIVE: Our study aims to head to head compare the application of gallium-68-fibroblast activation protein inhibitor (68Ga-FAPI) positron emission tomography/computed tomography (PET/CT) and fluorine-18-fluorodeoxyglucose (18F-FDG) PET/CT in primary and metastatic lesions of gastric tumor to determine the superior diagnostic tool. MATERIALS AND METHODS: A systematic search, up to March 31, 2023, across PubMed, Embase, and Cochrane Library databases utilized a data-specific Boolean logic strategy. Sensitivity (SEN) and specificity (SPE) evaluations of 68Ga-FAPI and 18F-FDG PET/CT in gastric cancer lesions were conducted. The quality of the studies was assessed using QUADAS-2, and publication bias was examined through Begg and Egger tests. RESULTS: Analysis involved 141 gastric tumor patients and 2753 metastatic lesions in five studies, with overall satisfactory study quality and no apparent publication bias. Patient-level data showed a combined SEN of 0.95 (95% CI: 0.90-0.98) for 68Ga-FAPI and 0.84 (95% CI: 0.77-0.89) for 18F-FDG. At the lesion level, combined SEN were 0.91 (95% CI: 0.84-0.96) for 68Ga-FAPI and 0.72 (95% CI: 0.63-0.80) for 18F-FDG. The pooled SEN for detecting lymph node metastases was 0.78 (95% CI: 0.74-0.82) for 68Ga-FAPI and 0.35 (95% CI: 0.30-0.39) for 18F-FDG, with pooled SPE values of 0.99 (95% CI: 0.98-0.99) and 0.97 (95% CI: 0.96-0.98), respectively. For detecting distant metastases, pooled SEN values were 0.97 (95% CI: 0.96-0.98) and 0.69 (95% CI: 0.66-0.72) for 68Ga-FAPI and 18F-FDG, with pooled SPE values of 0.86 (95% CI: 0.82-0.89) and 0.64 (95% CI: 0.59-0.68), respectively. CONCLUSION: This meta-analysis concluded that 68Ga-FAPI PET/CT was significantly more sensitive than 18F-FDG PET/CT in assessing primary gastric tumors, lymph nodes, and distant metastases, but the difference in the specificity of lymph node metastasis was not significant.

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.

Clinicopathologic and endoscopic characteristics of ten patients with gastric hamartomatous inverted polyp: a single center case series.

BACKGROUND: Gastric hamartomatous inverted polyps (GHIPs) are not well characterized and remain diagnostically challenging due to rarity. Therefore, this study aims to investigate the clinicopathologic and endoscopic characteristics of patients with GHIP. METHODS: We retrospectively reviewed clinicopathologic and endoscopic features of ten patients with GHIP who were admitted to Beijing Friendship Hospital from March 2013 to July 2022. All patients were treated successfully by endoscopic resection. RESULTS: GHIPs were usually asymptomatic and found incidentally during gastroscopic examination. They may be sessile or pedunculated, with diffuse or local surface redness or erosion. On endoscopic ultrasonography, the sessile submucosal tumor-type GHIP demonstrated a heterogeneous lesion with cystic areas in the third layer of the gastric wall. Histologically, GHIPs were characterized by a submucosal inverted proliferation of cystically dilated hyperplastic gastric glands accompanied by a branching proliferation of smooth muscle bundles. Inflammatory cells infiltration was observed in the stroma, whereas only one patient was complicated with glandular low-grade dysplasia. Assessment of the surrounding mucosa demonstrated that six patients (60%) had atrophic gastritis or Helicobacter pylori-associated gastritis, and four patients (40%) had non-specific gastritis. Endoscopic resection was safe and effective. CONCLUSIONS: GHIPs often arise from the background of abnormal mucosa, such as atrophic or H.pylori-associated gastritis. We make the hypothesis that acquired inflammation might lead to the development of GHIPs. We recommend to make a full assessment of the background mucosa and H. pylori infection status for evaluation of underlying gastric mucosal abnormalities, which may be the preneoplastic condition of the stomach.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis. METHODS: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features. RESULTS: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs. CONCLUSIONS: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.

A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement.

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %.

Advances in the Imaging of Esophageal and Gastroesophageal Junction Malignancies.

Accurate imaging is key for the diagnosis and treatment of esophageal and gastroesophageal junction cancers . Current imaging modalities, such as computed tomography (CT) and 18F-FDG (2-deoxy-2-[18F]fluoro-D-glucose) positron emission tomography (PET)/CT, have limitations in accurately staging these cancers. MRI shows promise for T staging and residual disease assessment. Novel PET tracers, like FAPI, FLT, and hypoxia markers, offer potential improvements in diagnostic accuracy. 18F-FDG PET/MRI combines metabolic and anatomic information, enhancing disease evaluation. Radiomics and artificial intelligence hold promise for early detection, treatment planning, and response assessment. Theranostic nanoparticles and personalized medicine approaches offer new avenues for cancer therapy.

CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis.

PURPOSE: To construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM). METHODS AND METHODS: 293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score. RESULTS: AUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor. CONCLUSIONS: Radiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

Diagnostic performance of linked color imaging for gastric cancer by Helicobacter pylori infection status: A subanalysis of the large-scale, multicenter randomized controlled trial LCI-FIND.

BACKGROUND: Linked color imaging (LCI) is a new image enhancement technology that facilitates the recognition of subtle differences in mucosal color. In the large-scale, multicenter randomized controlled trial LCI-FIND, LCI demonstrated good diagnostic performance for the detection of tumor lesions in the upper gastrointestinal tract. The aim of the present study was to exploratively evaluate the diagnostic performance of LCI according to H. pylori infection status as a subanalysis of LCI-FIND trial. METHODS: The patients were randomly allocated to receive white light imaging (WLI) first, followed by LCI (WLI group), or vice versa (LCI group), and the two groups were compared for the detection of tumors. Data from this trial were analyzed by the presence/absence of H. pylori infection and further analyzed by successful or unsuccessful eradication in the H. pylori infection group. RESULTS: The 752 patients in the WLI group and 750 patients in the LCI group who had participated in the LCI-FIND trial were included. In the successful eradication group, more gastric lesions were detected by primary mode in the LCI group than in the WLI group, indicating that more lesions were missed by WLI. Fisher's exact probability test for the comparison of the WLI and LCI groups yielded a p-value of 0.0068, with missed gastric lesions being detected 0.136 times (95% confidence interval: 0.020-0.923), significantly less with LCI than with WLI. CONCLUSION: The current study suggests that LCI should be used for gastric cancer screening, particularly in patients with successful H. pylori eradication.

Prediction of Post-Gastrectomy Pancreatic Complications: A Preoperative Imaging Study Based on Computed Tomography.

BACKGROUND: Postoperative pancreas-related complications (PPRCs) are common after laparoscopic gastrectomy (LG) in patients with gastric cancer. We estimated the anatomical location of the pancreas on a computed tomography (CT) image and investigated its impact on the incidence of PPRCs after LG. METHODS: We retrospectively reviewed the preoperative CT images of 203 patients who underwent LG for gastric cancer between January 2010 and December 2017. From these images, we measured the gap between the upper edge of the pancreatic body and the root of the common hepatic artery. We evaluated the potential relationship between PPRCs and the gap between pancreas and common hepatic artery (GPC) status using an analysis based on the median cutoff value and assessed the impact of GPC status on PPRC incidence. We performed univariate and multivariate analyses to identify predictive factors for PPRC. RESULT: Postoperative pancreas-related complications occurred in 11 patients (5.4%). The median of the optimal cutoff GPC value for predicting PPRC was 0 mm; therefore, we classified the GPC status into two groups: GPC plus group and GPC minus group. Univariate analysis revealed that sex (male), C-reactive protein (CRP) > .07 mg/dl, GPC plus, and visceral fat area (VFA) > 99 cm2 were associated with the development of PPRC. Multivariate analysis identified only GPC plus as independent predictor of PPRC (hazard ratio: 4.60 [95% confidence interval 1.11-31.15], P = .034). CONCLUSION: The GPC is a simple and reliable predictor of PPRC after LG. Surgeons should evaluate GPC status on preoperative CT images before proceeding with laparoscopic gastric cancer surgery.

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.

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.

Dynamic change in the peritoneal cancer index based on CT after chemotherapy in the overall survival prediction of gastric cancer patients with peritoneal metastasis.

PURPOSE: The purpose of this research was to investigate the efficacy of the CT-based peritoneal cancer index (PCI) to predict the overall survival of patients with peritoneal metastasis in gastric cancer (GCPM) after two cycles of chemotherapy. METHODS: This retrospective study registered 112 individuals with peritoneal metastasis in gastric cancer in our hospital. Abdominal and pelvic enhanced CT before and after chemotherapy was independently analyzed by two radiologists. The PCI of peritoneal metastasis in gastric cancer was evaluated according to the Sugarbaker classification, considering the size and distribution of the lesions using CT. Then we evaluated the prognostic performance of PCI based on CT, clinical characteristics, and imaging findings for survival analysis using multivariate Cox proportional hazard regression. RESULTS: The PCI change ratio based on CT after treatment (ΔPCI), therapy lines, and change in grade of ascites were independent factors that were associated with overall survival (OS). The area under the curve (AUC) value of ΔPCI for predicting OS with 0.773 was higher than that of RECIST 1.1 with 0.661 (P < 0.05). Patients with ΔPCI less than -15% had significantly longer OS. CONCLUSION: CT analysis after chemotherapy could predict OS in patients with GCPM. The CT-PCI change ratio could contribute to the determination of an appropriate strategy for gastric cancer patients with peritoneal metastasis.