ReviewEuropean radiology2022
Radiomics in precision medicine for gastric cancer: opportunities and challenges.
Review in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed, 96 citations in OpenAlex.
- A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer.Abdominal radiology (New York) · 2023Trial
- Clinical significance of intraoperative dissection time and machine learning-based prediction in laparoscopic distal gastrectomy for gastric cancer.Surgical endoscopy · 2026Article
- Multimodal radiopathomics model predicts postoperative metachronous liver metastasis in gastric cancer.Nature communications · 2026Article
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- The added value of intratumoral and peritumoral radiomic scores based on iodine density map for preoperative prediction of lymphovascular invasion in patients with gastric adenocarcinoma: a two-center study.Abdominal radiology (New York) · 2026Article
- Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition-Based Deep Learning.Cancer medicine · 2026Article
- Comparison of CT-based radiomics models for the prediction of human epidermal growth factor receptor 2 status in gastric cancer.BMC medical imaging · 2026Article
- Multiparametric MRI radiomics model incorporating ADC improves differentiation of benign and malignant sinonasal tumors.Neuroradiology · 2026Article
- A novel combined model integrating collagen properties, radiomics and clinical data to predict gastric cancer prognosis.Frontiers in oncology · 2026Article
- Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.Frontiers in oncology · 2026Article
- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
- Early identification of refractoryFrontiers in medicine · 2026Article
- Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study.Journal of imaging informatics in medicine · 2025Article
- Radiomics-based ensemble model predicts postoperative recurrence of gastric cancer.BMC medicine · 2025Article
- Radiomics and radiogenomics in ovarian cancer: a review with a focus on ultrasound applications.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Clinical significance of systemic inflammation response index and platelet-lymphocyte ratio in patients with stage I-III gastric cancer.World journal of gastrointestinal surgery · 2025Article
- Radiomics-based assessment of HER2 status and prognosis in gastric cancer: a retrospective dual-center CT study.Abdominal radiology (New York) · 2025Article
- Combination of prostate cancer antigen 3 (PCA3), sarcosine, glypican-1 (GPC1), urokinase plasminogen activator receptor (uPAR), and thymidine kinase 1 (TK1), and T2WI and DWI radiomics model for distinguishing benign prostatic hyperplasia, prostate cancer, and prostatitis.Journal of medical biochemistry · 2025Article
- CT-based quantification of spatiotemporal heterogeneity for predicting response to neoadjuvant chemotherapy in locally advanced gastric cancer.BMC medical imaging · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesRadiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC.
methodsWe performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality.
resultsTwenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies.
conclusionsAlthough radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. KEY POINTS: • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.
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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.