ArticleNPJ precision oncology2025
Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence models in predicting lymph node metastasis in early gastric cancer: a systematic review and meta -analysis.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2026Pooled it
- Multichannel deep learning-based MRI model for predicting breast cancer axillary lymph node invasion: a comparative study to Node-RADS.Quantitative imaging in medicine and surgery · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- Multi-scale deep learning models based on MRI for predicting pathological differentiation and evaluating its association with recurrence-free survival in hepatocellular carcinoma: an explainable machine learning study.Journal of gastrointestinal oncology · 2026Article
- Development and validation of a multimodal AI-agent system for prognosis analysis of bladder urothelial carcinoma.NPJ precision oncology · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data.Frontiers in immunology · 2026Article
- Gastrointestinal cancer: molecular pathogenesis and targeted therapy.Molecular biomedicine · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefore, this study aims to develop a deep learning model using portal venous phase CT images to accurately distinguish EGC without lymph node metastasis. This study included 3164 patients with gastric cancer (GC) who underwent radical surgery at two medical centers in China from 2006 to 2019. Moreover, 2.5D radiomic data and multi-instance learning (MIL) were novel approaches applied in this study. By basing the selection of features on 2.5D radiomic data and MIL, the ResNet101 model combined with the XGBoost model represented a satisfactory performance for diagnosing pT1N0 GC. Furthermore, the 2.5D MIL-based model demonstrated a markedly superior predictive performance compared to traditional radiomics models and clinical models. We first constructed a deep learning prediction model based on 2.5D radiomics and MIL for effectively diagnosing pT1N0 GC patients, which provides valuable information for the individualized treatment selection.
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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.