ReviewJournal of bone oncology2026
From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.
Review in Journal of bone oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Current Treatment Options and Emerging Technologies for Pelvic Bone Tumors: From Limb Salvage to Precision Oncology.Current treatment options in oncology · 2026Review
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bone tumors such as osteosarcoma and Ewing sarcoma remain among the most challenging cancers to diagnose and monitor because of their biological heterogeneity and overlapping radiological features. Magnetic resonance imaging (MRI) provides detailed anatomical insights, whereas liquid biopsy offers minimally invasive access to tumor genetics through circulating DNA, RNA, and extracellular vesicles. Each modality alone is limited, but recent advances in deep learning have enabled multimodal fusion of imaging and molecular data, improving risk stratification, therapy monitoring, and prognostication in patients with osteosarcoma and Ewing sarcoma. This review highlights how multimodal AI frameworks are being applied to bone tumors, delineating evidence from sarcoma-specific studies and representative pan‑cancer models with direct methodological relevance. By integrating MRI radiomics with liquid biopsy omics, deep learning holds promise for redefining precision oncology in bone tumors, delivering earlier detection and more personalized treatment strategies.
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Identifiers
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.