ArticleScientific reports2024
Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07305727 (Developing a Radiomic MRI Model Predictive of Response to Concomitant Chemoradiotherapy in Locally Advanced Cervical Cancer. A Prognostic, Retrospective, Open-label, Multicenter, Descriptive and Analytical Clinical Cohort Study), which is not on this map. Cited by 3 papers.
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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.
Developing a Radiomic MRI Model Predictive of Response to Concomitant Chemoradiotherapy in Locally Advanced Cervical Cancer. A Prognostic, Retrospective, Open-label, Multicenter, Descriptive and Analytical Clinical Cohort Study
Who cites it
3 citing papers in PubMed.
- Development and validation of a multimodal MRI habitat-based deep learning fusion model for predictingWorld journal of radiology · 2026Article
- Radiomics to discriminate between axial spondyloarthritis and axial psoriatic arthritis and to predict TNFi therapy persistence.EULAR rheumatology open · 2026Article
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
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Authors and funding
14 authors.
Funding
Abstract
Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).
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