ArticleLa Radiologia medica2026
A two-stage model combining clinical and delta-radiomics features for prognosis in high-grade gliomas after chemoradiotherapy: a retrospective study.
Article in La Radiologia medica, 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
10 authors.
Funding
Abstract
objectiveThis study aims to evaluate the efficacy of integrating pre-radiotherapy and mid-radiotherapy radiomics, temporal changes in tumor radiomics (delta radiomics) during chemoradiotherapy, and clinical variables into prognostic models for individualized overall survival (OS) prediction in patients with high-grade glioma (HGG).
methodsA total of 143 patients with HGG who underwent standard chemoradiotherapy were retrospectively enrolled from two centers. Patients were divided into a training cohort (n = 116), in which 5-fold cross-validation was performed, and an external test cohort (n = 27). Radiomics features were extracted from pre-radiotherapy and mid-radiotherapy MRI scans, and delta radiomic features were calculated as the differences between the two timepoints. Feature selection was performed using elastic-net-based stability selection, and radiomics scores (Rad-scores) were generated for each selected feature set using Cox models. Rad-scores were subsequently combined with clinical variables in a random survival forest (RSF) model for OS prediction. Kaplan-Meier survival analysis was performed to evaluate prognostic stratification.
resultsThe combined model integrating clinical variables and delta radiomics achieved a C-index of 0.818 and 0.761 in the validation and external test cohorts, respectively, outperforming models based solely on radiomics or clinical features. In the external test cohort, time-dependent discrimination yielded 12-, 18-, 24- and 30-month OS AUCs of 0.842, 0.856, 0.796, and 0.943, and Kaplan-Meier analysis showed significant risk stratification (p = 0.002).
conclusionThe findings of this retrospective study suggest that integrating multi-timepoint radiomics, delta radiomics features, and clinical variables may facilitate OS prediction in patients with HGG following standard chemoradiotherapy. While promising, the exploratory findings require further validation in larger prospective cohorts, given the limited size of the external validation cohort and the absence of MGMT promoter methylation status from the main model.
Indexed as
Identifiers
42873435What 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.