Integrating radiomics, artificial intelligence, and molecular signatures in bone and soft tissue tumors: advances in diagnosis and prognostication
This systematic review evaluates the integration of radiomics, artificial intelligence (AI), and molecular signatures for diagnosing and prognosticating bone and soft tissue tumors (BSTTs). Following PRISMA 2020 guidelines, we analyzed 24 studies from 1,141 initial records across PubMed, Scopus, Web...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1613133/full |
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| Summary: | This systematic review evaluates the integration of radiomics, artificial intelligence (AI), and molecular signatures for diagnosing and prognosticating bone and soft tissue tumors (BSTTs). Following PRISMA 2020 guidelines, we analyzed 24 studies from 1,141 initial records across PubMed, Scopus, Web of Science, and Google Scholar. Our findings reveal that while radiomics-AI pipelines are well-developed for BSTT assessment - particularly using MRI (72% of studies) and CT (25%) with machine learning classifiers like random forests (42%) and CNNs (17%) - molecular data integration remains virtually absent. Only 2 studies incorporated histopathological correlations, and none achieved full tri-modal integration of imaging, AI, and omics data. Key applications included tumor grading (58% of studies), chemotherapy response prediction (33%), and metastasis detection (21%), with median AUCs of 0.82-0.91 in validated models. Critical gaps identified include: (1) lack of standardized multi-omic feature fusion methods, (2) limited external validation (only 17% of studies), and (3) insufficient explainability in deep learning approaches. The review highlights an urgent need for attention-based neural networks and graph-based models to bridge imaging-molecular divides, alongside consensus protocols for radiogenomic dataset sharing. These insights establish a roadmap for developing clinically translatable, multi-modal diagnostic systems in musculoskeletal oncology. |
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| ISSN: | 2234-943X |