{"doi":"10.1007/s00259-025-07354-4","title":"Development and validation of a radiogenomics prognostic model integrating PET/CT radiomics and glucose metabolism-related gene signatures for non-small cell lung cancer","abstract":null,"journal":"European Journal of Nuclear Medicine and Molecular Imaging","year":2025,"id":634275,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1644947,"name":"Congjie Wang","orcid":null,"position":1,"is_corresponding":false},{"id":1270567,"name":"Jianguo Zhang","orcid":"0000-0003-2307-9666","position":2,"is_corresponding":false},{"id":1644949,"name":"Mingjun Ding","orcid":null,"position":3,"is_corresponding":false},{"id":1644951,"name":"Yizhi Ge","orcid":null,"position":4,"is_corresponding":false},{"id":1644953,"name":"Xia He","orcid":null,"position":5,"is_corresponding":false},{"id":592325,"name":"Chunsheng Wang","orcid":"0000-0001-7501-2282","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Development and validation of a radiogenomics prognostic model integrating PET/CT radiomics and glucose metabolism-related gene signatures for non-small cell lung cancer","abstract":"<h4>Background</h4>Non-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy characterized by altered glucose metabolism. Integration of PET/CT radiomics with glucose metabolism-related genomic signatures could provide a more comprehensive approach for prognosis and treatment guidance.<h4>Methods</h4>Radiomics features were extracted from PET/CT images of 156 NSCLC patients from The Cancer Imaging Archive (TCIA) database, and glucose metabolism-related gene signatures were obtained from TCGA and GEO databases. We developed a multimodal radiogenomics prognostic model (RGC-score) using least absolute shrinkage and selection operator (LASSO) regression, combining PET/CT radiomics, glucose metabolism-related genes (GMR-genes). Functional enrichment analysis, immune infiltration assessment, and drug sensitivity analysis were performed to investigate the biological significance of glucose metabolism-related genes (GMR-genes).<h4>Results</h4>The RGC-score model effectively stratified NSCLC patients into distinct high- and low-risk groups with significant differences in survival outcomes (P < 0.001), demonstrating excellent predictive performance (1-year AUC = 0.907, 5-year AUC = 0.968).GMR-genes are mainly involved in the process of metabolic remodeling of tumors, which is closely related to the immune microenvironment (especially CD8<sup>+</sup> T cell infiltration) and immune checkpoint molecule expression. Additionally, significant differences in drug sensitivity were identified between glucose metabolism subtypes.<h4>Conclusion</h4>The RGC-score robustly predicts NSCLC prognosis and informs metabolic-immune interactions for personalized therapy. Limitations include the retrospective design and modest validation cohort size, necessitating prospective multicenter trials.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40423774","pmcid":null,"openalex_id":"https://openalex.org/W4410794343","authors":[],"funders":[{"funder_name":"Spark Youth Cultivation Program of Jiangsu Cancer Hospital","grant_id":"XHQN202408","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82172804","title":null},{"funder_name":"Nanjing Science and Technology Plan Project","grant_id":"2022SX00001663","title":null}],"total_grants":3,"fwci":4.2158,"citation_percentile":0.94533497,"influential_citations":0,"citation_trend":[{"year":2025,"count":3},{"year":2026,"count":3}],"oa_status":"closed","license":"https://www.springernature.com/gp/researchers/text-and-data-mining","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/s00259-025-07354-4.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1007/s00259-025-07354-4/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1007/s00259-025-07354-4","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40423774","host_type":"repository"}],"fields_of_study":["Radiomics and Machine Learning in Medical Imaging","Cancer Immunotherapy and Biomarkers","Ferroptosis and cancer prognosis","Humans","Carcinoma, Non-Small-Cell Lung","Positron Emission Tomography Computed Tomography","Lung Neoplasms","Glucose","Prognosis","Male","Female","Middle Aged","Aged","Radiomics"],"mesh_terms":["Positron Emission Tomography Computed Tomography","Radiomics","Aged","Carcinoma, Non-Small-Cell Lung","Female","Glucose","Humans","Lung Neoplasms","Male","Middle Aged","Prognosis"],"keywords":["Radiogenomics","Radiomics","Medicine","Lung cancer","Oncology","Internal medicine","Radiology","NSCLC","glucose metabolism","Pet/ct","Immune Infiltration"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T13:25:10.190492Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}