ArticleNPJ digital medicine2025
Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 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
10 citing papers in PubMed.
- APEX-NET: automated pancreatic evaluation network using early non-contrast CT.European radiology · 2026Article
- Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.Scientific reports · 2026Article
- Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images.medRxiv : the preprint server for health sciences · 2026Article
- Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of postoperative cervical cancer patients.Scientific reports · 2026Article
- Machine learning-based prediction of long-term prognosis in cervical adenocarcinoma: a retrospective cohort study.BMC cancer · 2026Article
- Multimodal MRI radiomics and deep learning for brain age prediction: age-corrected brain age gap analysis in patients with insomnia.Biomedical engineering online · 2026Article
- Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence.International journal of surgery (London, England) · 2026Article
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Multitask learning for early treatment response and survival prediction in lung cancer radiotherapy using sequential CBCT imaging.Frontiers in oncology · 2026Article
- A Multimodal Graph Neural Network Based on MRI Radi-omics for Prognostic Prediction in Locally Advanced Cervical Squamous Cell Carcinoma.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
Abstract
For patients with locally advanced cervical cancer (LACC), precise survival prediction models could guide personalized treatment. We developed and validated CerviPro, a deep learning-based multimodal prognostic model, to predict disease-free survival (DFS) in 1018 patients with LACC receiving definitive radiotherapy. The model integrates pre- and post-treatment CT imaging, handcrafted radiomic features, and clinical variables. CerviPro demonstrated robust predictive performance in the internal validation cohort (C-index 0.81), and external validation cohorts (C-index 0.70&0.66), significantly stratifying patients into distinct high- and low-risk DFS groups. Multimodal feature fusion consistently outperformed models based on single feature categories (clinical data, imaging, or radiomics alone), highlighting the synergistic value of integrating diverse data sources. By integrating multimodal data to predict DFS and recurrence risk, CerviPro provides a clinically valuable prognostic tool for LACC, offering the potential to guide personalized treatment strategies.
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.