ArticleBMC cancer2025
Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma.
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 2 papers.
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
2 citing papers in PubMed.
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Leveraging vision transformer for histological grade prediction in laryngeal and hypopharyngeal squamous cell carcinoma: a large-scale multicenter study.Neuroradiology · 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
12 authors.
Funding
Abstract
backgroundAccurate prediction of prognosis and risk stratification in patients with laryngeal cancer can inform appropriate treatment decision-making. This study aims to develop a multi-channel deep learning radiomics model based on contrast-enhanced computed tomography (CECT) for predicting postoperative overall survival (OS) in patients.
methodsA total of 272 patients with laryngeal cancer were retrospectively recruited from two hospitals between January 2016 and July 2021. Specifically, 156 patients were enrolled from Center 1 as the training cohort, and 116 patients from Center 2 as the external test cohort. Two imaging signatures, reflecting phenotypes of the radiomics and multi-channel deep learning features, were constructed using pretreatment venous-phase CECT images. Feature selection involved reproducibility evaluation, Spearman correlation coefficient, and least absolute shrinkage and selection operator (LASSO) regression. Ten machine learning algorithms were employed to construct the signatures. An integrated Deep Learning Radiomics Nomogram (DLRN) was subsequently developed to predict OS. Predictive performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Kaplan-Meier survival curves derived from the DLRN were used for patient risk stratification, and subgroup analyses were conducted to validate the model's robustness.
resultsThe DLRN demonstrated satisfactory prognostic performance for laryngeal cancer. In the external test cohort, the 1-, 2-, and 3-year AUC values were 0.74, 0.75, and 0.80, respectively, and achieved a C-index of 0.73, outperforming individual models. Calibration curves and DCA indicated excellent calibration and the highest clinical net benefit. Subgroup analyses confirmed consistent DLRN performance across clinical stages, age groups, and surgical modalities.
conclusionThe proposed multi-channel deep learning radiomics model showed promising performance for predicting OS in laryngeal cancer patients. This approach may support individualized risk stratification and assist clinical decision-making in patients with laryngeal carcinoma.
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.