The aim of this study was to investigate the value of computed tomography (CT) radiomics for the differentiation between T2 and T3/4 stage lesions in gastric cancer.A total of 244 consecutive patients with pathologically proven gastric cancer were retrospectively included and split into a training cohort (171 patients) and a test cohort (73 patients). Preoperative arterial phase and portal phase contrast enhanced CT images were retrieved for tumor segmentation and feature extraction by using a dedicated postprocessing software. The random forest method was used to build the classifier models.The performance of single phase radiomics models were favorable in the differentiation between T2 and T3/4 stage tumors. Arterial phase-based radiomics model exhibited areas under the curve of 0.899 (95% CI: 0.812-0.955) in the training cohort and 0.825 (95% CI: 0.718-0.904) in the test cohort. Portal phase-based radiomics model showed areas under the curve of 0.843 (95% CI: 0.746-0.914) and 0.818 (95% CI: 0.711-0.899) in the training and test cohort, respectively.CT radiomics approach has a potential role in differentiation between T2 and T3/4 stage tumors in gastric cancer.Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Differentiation of colorectal cancers with deep submucosal invasion (T1b) from colorectal cancers with superficial invasion (T1a) or no invasion (Tis) is not straightforward. This study aimed to develop a computer aided diagnosis system (CADx) to establish the diagnosis of early-stage cancers using non-magnified endoscopic white light images alone.A total of 1513 lesions (Tis 1074, T1a 145, T1b 294) in 5108 images were collected from 1470 patients at ten academic hospitals and assigned to training and testing datasets (3:1). The ResNet-50 network was used as the backbone to extract features from images. Over sampling and focal loss were used to compensate class imbalance of invasive stage. Diagnostic performance was assessed using the testing dataset including 403 CRCs with 1392 images. Two experts and two trainees read the identical testing dataset.At 90% cutoff for per lesion score, CADx showed the highest specificity of 94.4% [95% confidence interval: 91.3 - 96.6], with 59.8% [48.3 - 70.4] sensitivity and 87.3% [83.7 - 90.4] accuracy. The area under the characteristic curve was 85.1% [79.9 - 90.4] for CADx, 88.2% [83.7 - 92.8] for expert 1, 85.9% [80.9 - 90.9] for expert 2, 77.0% [71.5 - 82.4] for trainee 1 (vs. CADx: p=0.0076), and 66.2% [60.6 - 71.9] for trainee 2 (p<0.0001). The function was also confirmed on nine short videos.CADx developed with endoscopic white light images showed excellent per lesion specificity and accuracy for T1b lesion diagnosis, equivalent to experts and superior to trainees. (UMIN000037053) (249 =<250 words).Copyright © 2023 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough.We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis.The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785-0.858) in the primary cohort, 0.797 (0.771-0.823) in the external validation cohorts, and 0.822 (0.756-0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n = 271).A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer receiving neoadjuvant chemotherapy, providing timely guidance for clinical decision-making. However, current approaches to evaluate LNM and OS have limited accuracy. In this study, we used longitudinal CT images from 1,021 patients with locally advanced gastric cancer to develop and validate a multitask deep learning model, named co-attention tri-oriented spatial Mamba (CTSMamba), to simultaneously predict LNM and OS. CTSMamba was trained and validated on 398 patients, and the performance was further validated on 623 patients at two additional centers. Notably, CTSMamba exhibited significantly more robust performance than a clinical model in predicting LNM across all of the cohorts. Additionally, integrating CTSMamba survival scores with clinical predictors further improved personalized OS prediction. These results support the potential of CTSMamba to accurately predict LNM and OS from longitudinal images, potentially providing clinicians with a tool to inform individualized treatment approaches and optimized prognostic strategies.

Lymph node metastasis (LNM) in gastric cancer is a prognostic factor and has implications for the extent of lymph node dissection. The lymphatic drainage of the stomach involves multiple nodal stations with different risks of metastases. The aim of this study was to develop a deep learning system for predicting LNMs in multiple nodal stations based on preoperative CT images in patients with gastric cancer.

Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients.

Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients.A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients' PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability.RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923-0.993], 0.941 (95% CI 0.904-0.977), 0.928 (95% CI 0.886-0.971), and 0.920 (95% CI 0.862-0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability.CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Accurate prediction of preoperative occult peritoneal metastasis (OPM) is critical to selecting appropriate therapeutic regimen for gastric cancer (GC). Considering the clinical practicability, we develop and validate a visible nomogram that integrates the CT images and clinicopathological parameters for the individual preoperative prediction of OPM in GC.This retrospective study included 520 patients who underwent staged laparoscopic exploration or peritoneal lavage cytology (PLC) examination. Univariate and multivariate logistic regression results were used to screen model predictors and construct nomograms of OPM risk. The performance of the model was detected by using ROC, accuracy, and C-index. The bootstrap resampling method was considered internal validation of the model. The Delong test was used to evaluate the difference in AUC between the two models.Grade 2 mural stratification, tumor thickness, and the Lauren classification diffuse were significant predictors of OPM (p < 0.05). The nomogram of these three factors (compared with the original model) showed a higher predictive effect (p < 0.001). The area under the curve (AUC) of the model was 0.830 (95% CI 0.788-0.873), and the internally validated AUC of 1000 bootstrap samples was 0.826 (95% CI 0.756-0.870). The sensitivity, specificity, and accuracy were 76.0%, 78.8%, and 78.3%, respectively.CT phenotype-based nomogram demonstrates favorable discrimination and calibration, and it can be conveniently used for preoperative individual risk rating of OPM in GC.In this study, the preoperative OPM prediction model based on CT images (mural stratification, tumor thickness) combined with pathological parameters (the Lauren classification) showed excellent predictive ability in GC, and it is also suitable for clinicians to use rather than limited to professional radiologists.• Nomogram based on CT image analysis can effectively predict occult peritoneal metastasis in gastric cancer (training area under the curve (AUC) = 0.830 and bootstrap AUC = 0.826). • Nomogram model combined with CT features performed better than the original model (established using only clinicopathological parameters) in differentiating occult peritoneal metastasis of gastric cancer.© 2023. The Author(s), under exclusive licence to European Society of Radiology.

The aim of this study was to build a SVM classifier using ResNet-3D algorithm by artificial intelligence for prediction of synchronous PC.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.© 2024. The Author(s).

Accurate delineation of cancer margins is critical for endoscopic curative resection. This study aimed to train and validate real-time fully convolutional networks for delineating the resection margin of early gastric cancer (EGC) under indigo carmine chromoendoscopy (CE) or white light endoscopy (WLE), and evaluated its performance and that of magnifying endoscopy with narrow-band imaging (ME-NBI).We collected CE and WLE images of EGC lesions to train fully convolutional networks ENDOANGEL. ENDOANGEL was tested both on stationary images and endoscopic submucosal dissection (ESD) videos. The accuracy and reliability of ENDOANGEL and NBI-dependent delineation were further evaluated by a novel endoscopy-pathology point-to-point marking.ENDOANGEL had an accuracy of 85.7% in the CE images and 88.9% in the WLE images under an overlap ratio threshold of 0.60 in comparison with the manual markers labeled by the experts. In the ESD videos, the resection margins predicted by ENDOANGEL covered all areas of high-grade intraepithelial neoplasia and cancers. The minimum distance between the margins predicted by ENDOANGEL and the histological cancer boundary was 3.44 ± 1.45 mm which outperformed the resection margin based on ME-NBI.ENDOANGEL has the potential to assist endoscopists in delineating the resection extent of EGC under CE or WLE during ESD.

Laparoscopic exploration (LE) is crucial for diagnosing intra-abdominal metastasis (IAM) in advanced gastric cancer (GC). However, overlooking single, tiny, and occult IAM lesions during LE can severely affect the treatment and prognosis due to surgeons' visual misinterpretations. To address this, we developed the artificial intelligence laparoscopic exploration system (AiLES) to recognize IAM lesions with various metastatic extents and locations. The AiLES was developed based on a dataset consisting of 5111 frames from 100 videos, using 4130 frames for model development and 981 frames for evaluation. The AiLES achieved a Dice score of 0.76 and a recognition speed of 11 frames per second, demonstrating robust performance in different metastatic extents (0.74-0.76) and locations (0.63-0.90). Furthermore, AiLES performed comparably to novice surgeons in IAM recognition and excelled in recognizing tiny and occult lesions. Our results demonstrate that the implementation of AiLES could enhance accurate tumor staging and assist individualized treatment decisions.© 2025. The Author(s).

The efficacy of neoadjuvant chemotherapy (NACT) in patients with advanced gastric cancer (GC) varies greatly. Thus, we aimed to verify the predictive value of tumor‐infiltrating immune cells (TIICs) on the treatment response to NACT and the prognosis of patients with advanced GC, and to explore the impact of NACT on the tumor immune microenvironment (TIME). Paired tumor tissues (pre‐ and post‐NACT) from patients with advanced GC were collected for this study. TIICs were assessed using immunohistochemistry staining and analyzed using logistic regression to establish an immune microenvironment score for GC (ISGC score) and predict NACT efficacy. Kaplan–Meier curves were used to evaluate the survival outcome of patients. The results showed that TIME was dramatically heterogeneous between NACT response and nonresponse patients. In the validation cohort, the ISGC score demonstrated good predictive performance for treatment response to NACT. Moreover, high ISGC indicated better long‐term survival in patients with advanced GC. Furthermore, tumor‐infiltrated T cells (CD3+ and CD8+) and CD11c+ macrophages were significantly increased in the response group, while CD163+ macrophages and FOXP3+ Treg cells were decreased after NACT. However, opposite results were exhibited in the nonresponse group. Finally, we found that the percentage of programmed cell death ligand 1 (PD‐L1)‐positive tumors was 31% (32/104) pre‐NACT and 49% (51/104) post‐NACT, and almost all patients with elevated PD‐L1 were in the NACT response group. The ISGC model accurately predicted NACT efficacy and classified patients with GC into different survival groups. NACT regulates the TIME in GC, which may provide strategies for personalized immunotherapy.