ArticleBritish journal of cancer2025
Intratumoral and peritumoral PET/CT-based radiomics for non-invasively and dynamically predicting immunotherapy response in NSCLC.
Article in British journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study.iScience · 2026Article
- Integrating Lymph Node Metastasis and Programmed Death-Ligand 1 Prediction in Non-Small Cell Lung Cancer From a Single PET/CT Scan: Multicenter Radiomics Study.JMIR medical informatics · 2026Article
- A radiomics-based machine learning model for the preoperative differentiation of lung adenocarcinoma subtypes.Quantitative imaging in medicine and surgery · 2026Article
- PET/CT imaging for assessment of tumor immunotherapy response: a new perspective on tumor metabolic reprogramming and immune escape.La Radiologia medica · 2026Review
- ΔAverage of long and short axis diameter on non-contrast CT is associated with major pathological response to neoadjuvant immunotherapy in resectable non-small cell lung cancer.European radiology · 2026Article
- Specific PET Imaging for Precision Management of Lung Cancer: Advances, Clinical Translation and Future Directions.Chemical & biomedical imaging · 2026Review
- Association between intra- and peritumoral heterogeneity on B-mode ultrasound and overall survival in patients with liver metastases treated with immunotherapy.European radiology experimental · 2026Article
- Article
- PET/CT-based quantification of intratumoral heterogeneity for predicting pathological complete response in breast cancer patients treated with neoadjuvant chemotherapy: A multicentre study.European journal of nuclear medicine and molecular imaging · 2026Article
- Advances in translational lung cancer research in 2025: a narrative review.Translational lung cancer research · 2026Review
- Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.Multidisciplinary respiratory medicine · 2026Article
- Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics.Cancers · 2026Review
- Non-invasive assessment of PD-L1 status and histology in non-small cell lung cancer usingJournal of translational medicine · 2026Article
- Metabolic reprogramming in cancer: dysregulation of glucose, lipid, and amino acid pathways and therapeutic opportunities.Molecular biomedicine · 2026Review
- Prognostic Significance of Pretreatment [¹⁸F]ALF-NOTA-FAPI-04 PET/CT in Patients With Recurrent Gastric Cancer Undergoing Combined PD-1 Inhibitor and Chemotherapy.Korean journal of radiology · 2026Article
- Prediction of tumor-infiltrating lymphocytes through habitat radiomics and exploration of response mechanisms in neoadjuvant immunochemotherapy-treated lung cancer.Cancer immunology, immunotherapy : CII · 2026Article
- Ensemble learning strategy-based 18 F-FDG PET/CT metabolic habitats radiomics for predicting EGFR mutation and prognosis in LA-NSCLC: a multi-center study.BMC medical imaging · 2026Article
- Tumor Characterization Using [Pharmaceuticals (Basel, Switzerland) · 2026Article
- Predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma after third-generation TKI resistance using CT radiomics-based habitat imaging.Frontiers in immunology · 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
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Authors and funding
16 authors.
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Abstract
backgroundWe aimed to develop a machine learning model based on intratumoral and peritumoral
methodsThis retrospective study included 296 NSCLC patients, including a training cohort (N = 183), a testing cohort (N = 78), and a TCIA radiogenomic cohort (N = 35). The extreme gradient boosting algorithm was employed to develop the radiomic models.
resultsThe COMB-Radscore, which was developed by combining radiomic features from PET, CT, and PET/CT images, had the most satisfactory predictive performance with AUC (ROC) 0.894 and 0.819 in the training and testing cohorts, respectively. Survival analysis has demonstrated that COMB-Radscore is an independent prognostic factor for progression-free survival and overall survival. Moreover, COMB-Radscore demonstrates excellent dynamic predictive performance, with an AUC (ROC) of 0.857, enabling the earlier detection of potential disease progression in patients compared to radiological evaluation solely relying on tumor size. Further radiogenomic analysis showed that the COMB-Radscore was associated with infiltration abundance and functional status of CD8 + T cells.
conclusionsThe radiomic model holds promise as a precise, personalized, and dynamic decision support tool for the treatment of NSCLC patients.
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