Work

The emerging role of mixed reality and artificial intelligence in sarcoma care: A systematic review

Year: 2026

Type: review

Abstract: BACKGROUND: Sarcomas are a heterogeneous group of cancers requiring cautious monitoring and expert management. The emerging role of Artificial Intelligence (AI) and Mixed Reality (MR) may represent a turning point in sarcoma care. This systematic review evaluates their application in sarcoma management. METHODS: A comprehensive search of PubMed/MEDLINE, Embase, Scopus, and Web of Science up to June 2025 was conducted following PRISMA guidelines. Eligible studies included case series, cohort studies, multicenter studies, diagnostic accuracy studies and prediction model studies reporting on AI or MR use in sarcoma. Review articles and non-English studies were excluded. Data extracted included design, population, modality, sarcoma subtype, and reported outcomes. RESULTS: Twenty-three studies met inclusion criteria: 1 case series, 1 case report, 2 cohort studies, 8 diagnostic accuracy studies and 11 prediction model studies with a cumulative sample size of 8478 patients. The most frequently investigated tumors were osteosarcoma (n = 6), soft tissue sarcoma (STS) (n = 5), and chondrosarcoma (n = 4). AI was primarily applied in imaging-based diagnosis (n = 12, reported accuracy 78-95 %), histopathological grading (n = 5), and radiogenomic models (n = 4). MR was used in preoperative planning (n = 3), intraoperative navigation (n = 2), and surgical training (n = 2). No integrated AI/MR platforms were reported. CONCLUSION: AI and MR show strong potential in improving sarcoma management, particularly for diagnostic accuracy and surgical planning. However, the literature remains heterogeneous, consisting mostly of preliminary studies with limited statistical power. Large-scale multicenter studies are required to validate the impact of AI and MR on outcomes and safely integrate these technologies into routine care.

Source: European Journal of Surgical Oncology

DOI: 10.1016/j.ejso.2026.111447

Authors: Surbhi Joshi, Pharel Njessi, Olivier Camuzard, Marc-Olivier Gauci, Sylvie Bonvalot, Elise Lupon