ArticleRadiation oncology (London, England)2024
MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer.
Article in Radiation oncology (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04815694 (THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI Guided Radiotherapy), which is not on this map. Cited by 4 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.
THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI Guided Radiotherapy
Who cites it
4 citing papers in PubMed.
- Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy.European radiology experimental · 2025Review
- Gut-Microbiome Signatures Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: A Systematic Review.Metabolites · 2025Review
- Recent innovations in offline and online Magnetic Resonance Imaging guided radiation oncology.Physics and imaging in radiation oncology · 2025Article
- Rectal Cancer: Exploring Predictive Biomarkers Through Molecular Pathways Involved in Carcinogenesis.Biology · 2024Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
backgroundComplete response prediction in locally advanced rectal cancer (LARC) patients is generally focused on the radiomics analysis of staging MRI. Until now, omics information extracted from gut microbiota and circulating tumor DNA (ctDNA) have not been integrated in composite biomarkers-based models, thereby omitting valuable information from the decision-making process. In this study, we aim to integrate radiomics with gut microbiota and ctDNA-based genomics tracking during neoadjuvant chemoradiotherapy (nCRT).
methodsThe main hypothesis of the MOREOVER study is that the incorporation of composite biomarkers with radiomics-based models used in the THUNDER-2 trial will improve the pathological complete response (pCR) predictive power of such models, paving the way for more accurate and comprehensive personalized treatment approaches. This is due to the inclusion of actionable omics variables that may disclose previously unknown correlations with radiomics. Aims of this study are: - to generate longitudinal microbiome data linked to disease resistance to nCRT and postulate future therapeutic strategies in terms of both type of treatment and timing, such as fecal microbiota transplant in non-responding patients. - to describe the genomics pattern and ctDNA data evolution throughout the nCRT treatment in order to support the prediction outcome and identify new risk-category stratification agents. - to mine and combine collected data through integrated multi-omics approaches (radiomics, metagenomics, metabolomics, metatranscriptomics, human genomics, ctDNA) in order to increase the performance of the radiomics-based response predictive model for LARC patients undergoing nCRT on MR-Linac. EXPERIMENTAL
designThe objective of the MOREOVER project is to enrich the phase II THUNDER-2 trial (NCT04815694) with gut microbiota and ctDNA omics information, by exploring the possibility to enhance predictive performance of the developed model. Longitudinal ctDNA genomics, microbiome and genomics data will be analyzed on 7 timepoints: prior to nCRT, during nCRT on a weekly basis and prior to surgery. Specific modelling will be performed for data harvested, according to the TRIPOD statements. DISCUSSION: We expect to find differences in fecal microbiome, ctDNA and radiomics profiles between the two groups of patients (pCR and not pCR). In addition, we expect to find a variability in the stability of the considered omics features over time. The identified profiles will be inserted into dedicated modelling solutions to set up a multiomics decision support system able to achieve personalized treatments.
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