ReviewJapanese journal of radiology2023
Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.
Review in Japanese journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
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Who cites it
17 citing papers in PubMed, 17 citations in OpenAlex.
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- Predictive imaging in abdominal oncology: current trends and future directions.Japanese journal of radiology · 2026Review
- 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
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Circular RNA hsa_circRNA_101996 modulates gastric cancer cell proliferation and apoptosis through the miR-577/HMGN5 axis.World journal of gastrointestinal oncology · 2025Article
- Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging.Translational cancer research · 2025Article
- Review
- A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.Tomography (Ann Arbor, Mich.) · 2024Article
- AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.Journal of imaging · 2024Article
- The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma.Discover oncology · 2024Article
- Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- OLIGOMETASTASIS IN GASTRIC CANCER TREATMENT: IS THERE A PLACE FOR THE SURGEON?Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2023Article
- A prognostic nomogram based on log odds of positive lymph nodes to predict the overall survival in biliary neuroendocrine neoplasms (NENs) patients after surgery.Journal of endocrinological investigation · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Gastric cancer is one of the most common malignant tumors. Although some progress has been made in chemotherapy and surgery, it is still one of the highest mortalities in the world. Therefore, early detection, diagnosis and treatment are very important to improve the prognosis of patients. In recent years, with the proposal of the concept of radiomics, it has been gradually applied to histopathological grading, differential diagnosis, therapeutic efficacy and prognosis evaluation of gastric cancer, whose advantage is to comprehensively quantify the tumor phenotype using a large number of quantitative image features, so as to predict and diagnose the lesion area of gastric cancer early. The purpose of this review is to evaluate the research status and progress of radiomics in gastric cancer, and reviewed the workflow and clinical application of radiomics. The 27 original studies on the application of radiomics in gastric cancer were included from web of science database search results from 2017 to 2021, the number of patients included ranged from 30 to 1680, and the models used were based on the combination of radiomics signature and clinical factors. Most of these studies showed positive results, the median radiomics quality score (RQS) for all studies was 36.1%, and the development prospect and challenges of radiomics development were prospected. In general, radiomics has great potential in improving the early prediction and diagnosis of gastric cancer, and provides an unprecedented opportunity for clinical practice to improve the decision support of gastric cancer treatment at a low cost.
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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.