ArticleCancer control : journal of the Moffitt Cancer Center
A Multimodal Graph Neural Network Based on MRI Radi-omics for Prognostic Prediction in Locally Advanced Cervical Squamous Cell Carcinoma.
Article in Cancer control : journal of the Moffitt Cancer Center. 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
IntroductionWe aimed to develop and validate a clinically interpretable multimodal deep learning framework integrating multi-sequence MRI and clinical variables for predicting overall survival (OS) in locally advanced cervical squamous cell carcinoma (LACSCC).MethodsThis retrospective cohort study included 247 patients with pathologically confirmed LACSCC. Pretreatment MRI sequences, including contrast-enhanced T1-weighted imaging (T1CE), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI), were analyzed. Tumor regions of interest were manually delineated, and slice-level deep features were extracted using a fine-tuned ResNet-50 network. A graph neural network was then constructed to model spatial relationships among MRI slices, aggregate slice-level features into patient-level representations, and integrate imaging features with clinical variables to generate a Deep Learning Prognostic Score (DLPS). Model performance was assessed using time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, Cox regression analysis, and nomogram calibration.ResultsIn the validation cohort, the model achieved area under the ROC curve values of 0.747, 0.749, and 0.706 for predicting 1-year, 3-year, and 5-year OS, respectively. Kaplan-Meier survival analysis showed significantly better OS in the high-DLPS group than in the low-DLPS group. Multivariable Cox regression analysis identified DLPS as an independent predictor of OS. The nomogram integrating DLPS and significant clinical variables showed good calibration in both the training and validation cohorts.ConclusionWe developed and validated a multimodal MRI-based GNN framework for individualized OS prediction in LACSCC. By integrating spatially structured imaging features with clinical variables, the model provided effective prognostic stratification and may support personalized risk assessment and treatment decision-making.
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.