ArticleBMC cancer2025
Tumor metabolic heterogeneity based on
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A pretreatmentTranslational cancer research · 2026Article
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
Abstract
backgroundTumor metabolic heterogeneity (MH) assessed by 18-fluorine fluorodeoxyglucose positron emission tomography computed tomography (18F-FDG PET/CT) is established as a marker of drug resistance in solid tumors. Its prognostic significance in diffuse large B-cell lymphoma (DLBCL) remains inadequately explored. This study aims to investigate the prognostic value of tumor MH assessed by 18F-FDG PET/CT in DLBCL.
methodsThis study retrospectively included 297 patients diagnosed with DLBCL at the Third Affiliated Hospital of Soochow University from August 2012 to December 2022. We evaluated tumor MH using the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH). In addition to obtaining conventional PET metabolic metrics, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG), we also collected baseline clinical data. This study developed a survival prediction model based on Cox regression analysis and compared its prognostic performance with the National Comprehensive Cancer Network-International prognostic index (NCCN-IPI). We evaluated the model’s calibration, discrimination, and clinical utility, and conducted an internal validation.
resultsMultivariable Cox regression analysis indicated age (hazard ratio [HR], HR,1.62, 95% confidence interval [CI], 1.13 ~ 2.34, P = 0.01; HR, 2.15, 95% CI, 1.35 ~ 3.42, P = 0.001), TMTV (HR, 3.24, 95% CI, 1.56 ~ 6.73, P = 0.002; HR, 3.35, 95% CI, 1.30 ~ 8.62, P = 0.012), and AUC-CSH (HR, 0.64, 95% CI, 0.45 ~ 0.92, P = 0.015; HR, 0.59, 95% CI, 0.38 ~ 0.92, P = 0.018) as independent predictive factors for both progression-free survival (PFS) and overall survival (OS). Lower AUC-CSH value corresponded to higher tumor MH, indicating poorer survival outcome. The combined model outperformed the NCCN-IPI, achieving C-index of 0.688, 0.630 for PFS and 0.721, 0.688 for OS. The calibration curve showed good consistency, and the decision curve analysis (DCA) also confirmed its potential application value in individualized treatment decisions.
conclusionsTumor MH, assessed by 18F-FDG PET/CT, demonstrates potential as a prognostic biomarker in DLBCL. Its incorporation into predictive models may improve individualized prognostic evaluation and facilitate more precise treatment stratification for these patients.
trial registrationThis research has been retrospectively registered in the Ethics Committee institutional of the Third Affiliated Hospital of Soochow University [approval number (2022) KD155].
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.