ReviewEuropean radiology experimental2025
Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy.
Review in European radiology experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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
The integration of diagnostic imaging with radiation therapy (RT) is evolving into a continuous workflow, significantly advancing personalised oncology care. Recent technological innovations, particularly the incorporation of real-time magnetic resonance imaging (MRI) with linear accelerators, have markedly enhanced RT precision, improving target coverage and reducing radiation exposure to surrounding healthy tissues. Furthermore, real-time MRI enables the collection of quantitative imaging data during each treatment fraction, potentially leading to the identification of quantitative imaging biomarkers. These biomarkers can capture dynamic biological changes during RT, offering unprecedented insights into treatment response. The integration of these imaging biomarkers with clinical, genomic, and pathological data into artificial intelligence (AI)-supported clinical decision support systems promises to further refine therapeutic personalisation. In this context, AI plays a central role by automating labour-intensive tasks, extracting quantitative metrics, and integrating multidimensional data into clinically meaningful predictive models. This review outlines a vision for the future of RT, highlighting how the synergy of advanced imaging, AI, and multidomain data through three logical steps: (1) rethinking and reorganising the patient care journey; (2) from imaging "for" to imaging "with" RT; and (3) incorporation into clinical decision support systems. This integration will support the development of personalised, biologically driven treatment strategies. RELEVANCE STATEMENT: The longitudinal integration of diagnostic imaging and RT, facilitated by AI, could significantly enhance clinical workflow efficiency and therapeutic accuracy in oncology. KEY POINTS: Oncological care is transitioning from disease-centred to patient-centred, with tumour boards representing the junction for shared multidisciplinary decisions. Integrating advanced imaging with RT enables quantitative imaging biomarkers extraction that captures tumour changes throughout the course of treatment. Artificial intelligence plays a central role in automating resource-intensive processes and integrating large-scale multidomain data towards personalised medicine.
Indexed as
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What Socratic 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 Socratic graph.