Trial reportAging2022
A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment.
Trial report in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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
14 citing papers in PubMed, 17 citations in OpenAlex.
- A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy.BMC gastroenterology · 2023Trial
- Immuno-cytotoxic convergence: integrating antibody‒drug conjugates and immunofusion proteins to overcome resistance in gastrointestinal cancers.Journal of hematology & oncology · 2026Review
- CT-based radiomics for predicting the treatment response to PD-1/PD-L1 inhibitors combined with chemotherapy in unresectable gastric cancer.Insights into imaging · 2026Article
- Multimodal CT radiomics predicts PD-1 inhibitor efficacy in advanced gastric cancer: a two-center validation study.Insights into imaging · 2025Article
- Nomogram based on CT imaging and clinical data to predict the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer.Frontiers in immunology · 2025Article
- Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology.Frontiers in oncology · 2025Review
- Article
- A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?NPJ precision oncology · 2024Review
- Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study.Journal for immunotherapy of cancer · 2023Article
- Artificial intelligence applications in computed tomography in gastric cancer: a narrative review.Translational cancer research · 2023Review
- Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study.Frontiers in oncology · 2023Article
- Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians.Frontiers in immunology · 2023Review
- Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.Journal for immunotherapy of cancer · 2022Review
- CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors.Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell's c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732-0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors (
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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.