ArticleEClinicalMedicine2024
A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images.
Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global hotspots and trends in the application of neoadjuvant therapy for gastric cancer: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas.CNS neuroscience & therapeutics · 2026Article
- Development and validation of an interpretable MRI-based multimodal fusion model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter study.Abdominal radiology (New York) · 2026Article
- Review
- A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.BMC medical imaging · 2026Article
- CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.Journal of translational medicine · 2026Article
- Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.BMC medical imaging · 2026Article
- Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.BMC cancer · 2026Article
- The result prediction of fluorescence in situ hybridization for breast cancer patients based on machine learning and deep learning models: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Multi-parameter magnetic resonance imaging (MRI) deep learning radiomics predicts complete response after induction immunochemotherapy in locally advanced nasopharyngeal carcinoma.Translational cancer research · 2026Article
- Integration of clinical, pathological, and endoscopic data improves prediction for lymph node metastasis in early gastric cancer: a retrospective multicenter study.International journal of surgery (London, England) · 2026Article
- Hyperthermia enhances the efficacy of PD-1 inhibitor plus chemotherapy in PD-L1 CPS-negative advanced gastric cancer.Frontiers in immunology · 2026Article
- Transformer-Based Habitat Fusion Model Using DCE-MRI Predicts Microvascular Invasion and Recurrence in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Optimizing patient selection for neoadjuvant immunotherapy in gastric cancer via pre-treatment imaging analysis.Frontiers in immunology · 2026Article
- Impact of total gastrectomy plus perioperative PD-1 inhibitors on survival in locally advanced gastric cancer.Frontiers in oncology · 2026Article
- A Transformer-Based Deep Learning Model for predicting Early Recurrence in Hepatocellular Carcinoma After Hepatectomy Using Intravoxel Incoherent Motion Images.Journal of hepatocellular carcinoma · 2026Article
- Predictive value of multivariate models combining CT-based extracellular volume fraction with clinicopathological parameters for preoperative detection of occult lymph node metastasis in gastric cancer.Insights into imaging · 2025Article
- Prediction of the Ki-67 proliferation index in lung adenocarcinoma using an interpretable CT-based deep learning radiomics model: a two-center study.BMC pulmonary medicine · 2025Article
- Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma.BMC cancer · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC). Methods: A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan-Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers. Findings: The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752-0.849), 0.748 (95% CI, 0.660-0.830), 0.788 (95% CI, 0.735-0.835), and 0.766 (95% CI, 0.717-0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P < 0.001; Center 2:HR, 1.776, P = 0.013]. Interpretation: The transformer-based DLN provides early and effective prediction of LNM and survival outcomes in LAGC patients receiving NAC, with promise to guide individualized therapy. Future prospective multicenter studies are warranted to further validate our model. Funding: National Natural Science Foundation of China (NO. 82373432, 82171923, 82202142), Project Funded by China Postdoctoral Science Foundation (NO. 2022M720857), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (NO. U22A20345), National Science Fund for Distinguished Young Scholars of China (NO. 81925023), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (NO. 2022B1212010011), High-level Hospital Construction Project (NO. DFJHBF202105), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (NO. 2024B1515020091).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.