Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support

Tongtong Huo; Wei Wu; Xiaoliang Chen; Mingdi Xue; Pengran Liu; Jiayao Zhang; Yi Xie; Honglin Wang; Hong Zhou; Zineng Yan; Songxiang Liu; Lin Lu; Jiaming Yang; Jin Liu; Zhewei Ye

doi:10.1016/j.intonc.2025.06.005

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Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support

更新时间：2026-06-03

- Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support
- Intelligent Oncology Vol. 1, Issue 3, Pages: 204-215(2025)
- 作者机构：
  
  1. School of Electronic Information, Wuhan University of Science and Technology,Hubei,Wuhan,China,430081
  2. Union Hospital, Tongji Medical College, Huazhong University of Science and Technology,Hubei,Wuhan,China,430022
- 作者简介：
- 基金信息：
- DOI：10.1016/j.intonc.2025.06.005
  CLC：
- Received：16 April 2025，
  
  Online First：03 July 2025，
  
  Published：15 July 2025
- 稿件说明：
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Tongtong Huo, Wei Wu, Xiaoliang Chen, et al. Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support[J]. Intelligent Oncology, 2025, 1(3): 204-215.
DOI：

Tongtong Huo, Wei Wu, Xiaoliang Chen, et al. Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support[J]. Intelligent Oncology, 2025, 1(3): 204-215. DOI： 10.1016/j.intonc.2025.06.005.

摘要

Bone tumors (BTs)—including osteosarcoma

Ewing sarcoma

and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location

histological subtype

and molecular alterations. Recent advances in artificial intelligence (AI)

particularly deep learning

have enabled the integration of diverse clinical data modalities to support diagnosis

treatment planning

and prognostication in bone oncology. This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging

digital pathology

multi-omics profiling

and electronic health records. We conducted a structured review of peer-reviewed literature published between 2015 and early 2025

focusing on the development

validation

and clinical applicability of AI models for BT diagnosis

subtyping

treatment response prediction

and recurrence monitoring. Although multimodal models have demonstrated advantages over unimodal approaches

especially in handling missing data and improving generalizability

most remain constrained by single-center study designs

small sample sizes

and limited prospective or external validation. Persistent technical and translational challenges include semantic misalignment across modalities

incomplete datasets

limited model interpretability

and regulatory and infrastructural barriers to clinical integration. To address these limitations

we highlight emerging directions such as contrastive representation learning

generative data augmentation

transformer-based fusion architectures

and privacy-preserving federated learning. We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability. In summary

multimodal AI represents a promising paradigm for advancing precision care in BTs. Realizing its full clinical potential will require methodologically rigorous

biologically informed

and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.

Abstract

Bone tumors (BTs)—including osteosarcoma

Ewing sarcoma

and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location

histological subtype

and molecular alterations. Recent advances in artificial intelligence (AI)

particularly deep learning

have enabled the integration of diverse clinical data modalities to support diagnosis

treatment planning

and prognostication in bone oncology. This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging

digital pathology

multi-omics profiling

and electronic health records. We conducted a structured review of peer-reviewed literature published between 2015 and early 2025

focusing on the development

validation

and clinical applicability of AI models for BT diagnosis

subtyping

treatment response prediction

and recurrence monitoring. Although multimodal models have demonstrated advantages over unimodal approaches

especially in handling missing data and improving generalizability

most remain constrained by single-center study designs

small sample sizes

and limited prospective or external validation. Persistent technical and translational challenges include semantic misalignment across modalities

incomplete datasets

limited model interpretability

and regulatory and infrastructural barriers to clinical integration. To address these limitations

we highlight emerging directions such as contrastive representation learning

generative data augmentation

transformer-based fusion architectures

and privacy-preserving federated learning. We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability. In summary

multimodal AI represents a promising paradigm for advancing precision care in BTs. Realizing its full clinical potential will require methodologically rigorous

biologically informed

and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.

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