{"doi":"10.1093/bjr/tqae234","title":"A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy","abstract":"OBJECTIVE: To identify image biomarkers associated with overall life expectancy from low-dose CT and integrate them as an index for assessing an individual's health. METHODS: Two categories of CT image features, body composition tissues and cardiopulmonary vasculature characteristics, were quantified from LDCT scans in the Pittsburgh Lung Screening Study cohort (n = 3635). Cox proportional-hazards models identified significant image features which were integrated with subject demographics to predict the subject's overall hazard. Subjects were stratified using composite model predictions and feature-specific risk stratification thresholds. The model's performance was validated extensively, including 5-fold cross-validation on PLuSS baseline, PLuSS follow-up examinations, and the National Lung Screening Trial (NLST). RESULTS: The composite model had significantly improved prognostic ability compared to the baseline model (P < .01) with AUCs of 0.774 (95% CI: 0.757-0.792) on PLuSS, 0.723 (95% CI: 0.703-0.744) on PLuSS follow-up, and 0.681 (95% CI: 0.651-0.710) on the NLST cohort. The identified high-risk stratum were several times more likely to die, with mortality rates of 79.34% on PLuSS, 76.47% on PLuSS follow-up, and 46.74% on NLST. Two cardiopulmonary structures (intrapulmonary artery-vein ratio, intrapulmonary vein density) and two body composition tissues (SM density, bone density) identified high-risk patients. CONCLUSIONS: Body composition and pulmonary vasculatures are predictive of an individual's health risk; their integrations with subject demographics facilitate the assessment of an individual's overall health status or susceptibility to disease. ADVANCES IN KNOWLEDGE: CT-computed body composition and vasculature biomarkers provide improved prognostic value. The integration of CT biomarkers and patient demographic information improves subject risk stratification.","journal":"British Journal of Radiology","year":2024,"id":482197,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8284,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1321577,"name":"Tong Yu","orcid":"0009-0003-4914-8380","position":1,"is_corresponding":false},{"id":448655,"name":"Jing Wang","orcid":"0000-0001-7199-4784","position":2,"is_corresponding":false},{"id":288546,"name":"David O. Wilson","orcid":"0000-0002-2635-7468","position":3,"is_corresponding":false},{"id":1322479,"name":"Pengyu Chen","orcid":"0000-0002-6519-9079","position":4,"is_corresponding":false},{"id":1322480,"name":"Emrah Duman","orcid":"0000-0002-5927-2153","position":5,"is_corresponding":false},{"id":288540,"name":"Jiantao Pu","orcid":"0000-0003-2127-5313","position":6,"is_corresponding":false},{"id":802074,"name":"Cameron Beeche","orcid":"0000-0002-5781-8810","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":null,"created_at":"2026-07-19T02:07:14.349221Z","pmid":"39535867","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":[]}