ArticleFrontiers in medicine2022
Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines 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
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Diagnostic performance of CT scan-based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review.Frontiers in medicine · 2023Pooled it
- Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.Biomedical engineering online · 2024Trial
- Preoperative CT-based Radiomics for Predicting Response to Neoadjuvant Chemoimmunotherapy in Esophageal Squamous Cell Carcinoma.Radiology. Imaging cancer · 2026Article
- A novel combined model integrating collagen properties, radiomics and clinical data to predict gastric cancer prognosis.Frontiers in oncology · 2026Article
- Machine Learning for Lymph Node Metastasis Prediction in Early Gastric Cancer: A Comparative Analysis.International journal of medical sciences · 2026Article
- Machine Learning Algorithms for Adverse Drug Reactions Prediction and Identifying Its Determinants Among HIV Patients on Antiretroviral Therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia.Health science reports · 2025Article
- Multimodal ultrasound-based radiomics and deep learning for differential diagnosis of O-RADS 4-5 adnexal masses.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Habitat radiomics assists radiologists in accurately diagnosing lymph node metastasis of adenocarcinoma of the esophagogastric junction.Insights into imaging · 2025Article
- Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymph node metastasis in gastric cancer.BMC gastroenterology · 2025Article
- MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: a multicenter study.Insights into imaging · 2025Article
- Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.Frontiers in oncology · 2025Article
- Diagnostic Accuracy of Deep Learning for Predicting Lymph Node Metastasis Based on Computed Tomography in Gastric Cancer: A Systematic Review and Meta-analysis.Medical journal of the Islamic Republic of Iran · 2025Review
- CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.BMC medical imaging · 2024Article
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.BMC medical imaging · 2024Article
- A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients.Frontiers in oncology · 2024Article
- Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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Abstract
Background: This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods: This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, Results: We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively. Conclusion: We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.
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