ArticleFrontiers in nutrition2026
Multidimensional modeling and stratification of nutritional risk in non-small cell lung cancer based on artificial intelligence-derived CT body composition phenotypes.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop and temporally validate a multidimensional nutritional risk model integrating artificial intelligence (AI)-derived CT body composition phenotypes for patients with non-small cell lung cancer (NSCLC). Methods: This single-center retrospective cohort included 656 patients with pathologically confirmed NSCLC, including 432 in the development cohort and 224 in the temporal validation cohort. Baseline chest CT, clinical-nutritional variables, and immune-inflammatory markers were collected before first-line treatment. AI-based automated body composition analysis was used to extract CT phenotypes. The primary outcome was a 90-day nutrition-related adverse clinical trajectory. Clinical-nutritional, imaging, and integrated models were developed and compared. Results: The incidence of short-term nutrition-related adverse clinical trajectories was 34.3% in the development cohort and 32.6% in the validation cohort. Percentage weight loss, prognostic nutritional index, systemic immune-inflammation index, skeletal muscle index, and intermuscular adipose tissue volume were independent predictors. The integrated model achieved the best performance, with AUCs of 0.842 and 0.816 in the development and validation cohorts, respectively, and showed superior calibration and clinical utility. Higher risk scores were also associated with worse overall survival and progression-free survival. Conclusion: Integrating AI-derived CT body composition phenotypes with nutritional and inflammatory indicators improved nutritional risk stratification in NSCLC and may support earlier risk-adapted nutritional management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.