ArticleFrontiers in oncology2022
CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03636893 (Dragon III- Phase 2), which is not on this map. Cited by 18 papers, 3 of them syntheses that pooled it.
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.
Dragon III- Phase 2: The Preliminary Efficacy Study of FLOT Versus SOX Regimen as Neoadjuvant Chemotherapy for Patients With Locally Advanced Gastric Cancer: Open-label, Randomized Controlled Trial
Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Pooled it
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review.Frontiers in oncology · 2024Pooled it
- The study of nomogram model based on CT radiomics and clinical features for histological classification of parotid gland tumors.Scientific reports · 2026Article
- CT-based delta-radiomics nomogram to assess tumor regression grade in locally advanced gastric cancer patients following neoadjuvant chemotherapy.Abdominal radiology (New York) · 2026Article
- Preoperative prediction of tumor deposits in advanced gastric cancer using intratumoral and peritumoral CT radiomics: development and validation of an ensemble model.Frontiers in oncology · 2026Article
- Deep-learning-based radiomics of intratumoral and peritumoral CT images to differentiate benign from malignant parotid tumors.American journal of cancer research · 2026Article
- Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis from COVID-19 Pneumonia Using a CT-based Radiomics Nomogram.Current medical imaging · 2025Article
- CT-based quantification of spatiotemporal heterogeneity for predicting response to neoadjuvant chemotherapy in locally advanced gastric cancer.BMC medical imaging · 2025Article
- Prediction of tumor regression grade in far-advanced gastric cancer after preoperative immuno-chemotherapy using dual-energy CT-derived extracellular volume fraction.European radiology · 2025Article
- Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology.Frontiers in oncology · 2025Review
- Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.Abdominal radiology (New York) · 2024Article
- Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer.Insights into imaging · 2024Article
- Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning.Frontiers in medicine · 2024Article
- Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy.Abdominal radiology (New York) · 2023Review
- Neoadjuvant Chemotherapy in Asian Patients With Locally Advanced Gastric Cancer.Journal of gastric cancer · 2023Review
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years.Frontiers in pediatrics · 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
14 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy. Materials and Methods: A total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated. Results: There were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.76~0.90), 0.77 (0.63~0.91), 0.78 (0.66~0.89), and 0.72 (0.66~0.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010). Conclusion: Radiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.
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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.