ArticleFrontiers in oncology2022
CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- 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
- A commentary on "Multimodal radiopathomics approach for predictions of prognosis and immunotherapy response in patients with gastric cancer: a multicohort retrospective study".International journal of surgery (London, England) · 2026Article
- Deep learning-based radiomics and pathomics for decoding tumor microenvironment and predicting immunotherapy outcomes in gastric cancer.Frontiers in immunology · 2026Review
- A novel combined model integrating collagen properties, radiomics and clinical data to predict gastric cancer prognosis.Frontiers in oncology · 2026Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
- Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma.NPJ digital medicine · 2025Article
- Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology.Frontiers in oncology · 2025Review
- Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer.World journal of gastrointestinal oncology · 2024Article
- Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data.Signal transduction and targeted therapy · 2024Article
- CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients.Journal of translational medicine · 2024Article
- Delta-radiomics in cancer immunotherapy response prediction: A systematic review.European journal of radiology open · 2023Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: To explore the prognostic value of CT-based delta radiomics in predicting the prognosis of patients with stage IV gastric cancer treated with immune checkpoint inhibitors (ICI). Materials and methods: Forty-two patients with stage IV gastric cancer, who had received ICI monotherapy, were enrolled in this retrospective study. Baseline and first follow-up CT scans were analyzed. Intratumoral and peritumoral regions of interest (ROI) were contoured, enabling the extraction of 192 features from each ROI. The intraclass correlation coefficients were used to select features with high stability. The least absolute shrinkage and selection operator was used to select features with high weights for predicting patient prognosis. Kaplan-Meier analysis and log-rank test were performed to explore the association between features and progression free survival (PFS). Cox regression analyses were used to identify predictors for PFS. The C-index was used to assess the prediction performance of features. Results: Two radiomics features of ΔVintra_ZV and postVperi_Sphericity were identified from intratumoral and peritumoral regions, respectively. The Kaplan-Meier analysis revealed significant differences in PFS between patients with low and high feature value (ΔVintra_ZV: Conclusions: Based on CT scans at baseline and first follow-up, the delta radiomics features could efficiently predict the PFS of gastric cancer patients treated with ICI therapy.
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