Finite Element Modeling, CT Radiomics, and AI-Driven Structural Analysis of Bone Tumors: Translational Applications from Canine Osteosarcoma to Human Orthopedic Oncology

Introduction

Primary malignant bone tumors and skeletal metastases profoundly compromise skeletal integrity through osteolytic and osteoblastic remodeling, frequently culminating in pathological fractures associated with substantial morbidity and reduced survival. Accurate prediction of impending fracture remains one of the most challenging problems in orthopedic oncology. Conventional clinical decision-making relies heavily on subjective radiographic assessment and scoring systems such as Mirels, which exhibit limited specificity and may lead to unnecessary prophylactic interventions. Recent advances in quantitative computed tomography (QCT), finite element modeling (FEM), radiomics, and artificial intelligence have enabled the development of patient-specific computational frameworks capable of transforming tomographic data into quantitative biomechanical predictions. Simultaneously, naturally occurring canine osteosarcoma has emerged as a powerful comparative oncology model due to its close biological, genomic, and biomechanical resemblance to human osteosarcoma. This review synthesizes current evidence regarding the integration of CT-derived structural analysis, computational biomechanics, and comparative oncology for improving fracture prediction and personalized treatment planning in bone tumors.

Methods

A PRISMA-guided systematic review was conducted using PubMed, Scopus, Web of Science, Embase, IEEE Xplore, and ScienceDirect. The study selection process involved an initial title and abstract screening followed by a full-text evaluation. Inclusion criteria comprised peer-reviewed English-language studies published from 2020 onward focusing on CT-based biomechanics, canine comparative oncology, and artificial intelligence in orthopedic oncology. Exclusion criteria included non-English articles, case reports, and studies lacking quantitative biomechanical validation. Studies meeting the predefined eligibility criteria were included for qualitative synthesis. Data related to CT-to-FEM pipelines, comparative transcriptomics, radiomics, and emerging computational models were extracted and critically evaluated.

Results

Current evidence suggests that CT-based structural assessment substantially improves fracture-risk prediction compared with conventional clinical scoring systems. CT-based Rigidity Analysis (CTRA) achieved sensitivity approaching 100% with specificity ranging from 61% to 90%, significantly outperforming Mirels-based assessment. Patient-specific finite element models further enhanced biomechanical characterization by incorporating three-dimensional geometry, heterogeneous material properties, and physiologic loading conditions to estimate stress distribution, strain concentration, stiffness, and failure load.

Experimental validation studies in canine osteosarcoma models demonstrated strong agreement between computational predictions and mechanical testing outcomes, with reported correlations reaching R² = 0.93 for structural stiffness estimation. Comparative transcriptomic analyses further revealed conserved tumor microenvironment subtypes between canine and human osteosarcoma, including immune-enriched, extracellular matrix-rich, and immune-desert phenotypes, supporting the translational relevance of canine models for both biological and biomechanical investigations.

Recent developments in CT radiomics have expanded the clinical utility of tomographic imaging beyond anatomical assessment. Quantitative texture, intensity, and morphological features extracted from routine CT examinations have demonstrated encouraging predictive performance for chemotherapy response, metastatic progression, and tumor aggressiveness, with some machine learning models reporting exceptionally high diagnostic performance. Concurrently, deep learning-assisted segmentation frameworks, including advanced convolutional neural network architectures, have achieved Dice similarity coefficients exceeding 90%, substantially reducing segmentation time and improving reproducibility of patient-specific modeling workflows.

While CT-based Rigidity Analysis (CTRA) and patient-specific FEA have been consistently validated across multiple studies, emerging technologies such as digital twins and Physics-Informed Neural Networks (PINNs) currently represent promising future directions rather than clinically established solutions. These technologies offer dynamic virtual patient representations and accelerated biomechanical simulations, but require broader clinical validation before routine deployment.

Furthermore, translating AI-driven computational frameworks into clinical practice requires overcoming the opaque nature of advanced algorithms. Integrating explainable AI (XAI) methodologies is increasingly recognized as essential for supporting trustworthy clinical decision-making. For instance, hybrid explainable AI frameworks applied in broader medical imaging domains have demonstrated that providing multi-level explainability can significantly enhance the transparency and clinical reliability of deep learning models. Similar explainable AI strategies could improve transparency and clinician trust in CT-based orthopedic oncology workflows.

Conclusions

The convergence of QCT-derived finite element modeling, CT radiomics, artificial intelligence, and comparative oncology is transforming the assessment and management of malignant bone disease. Evidence from both human studies and naturally occurring canine osteosarcoma models demonstrates that patient-specific computational biomechanics can provide more accurate and objective fracture-risk evaluation than conventional clinical approaches. Continued advances in automated image analysis, digital twin platforms, and physics-informed machine learning are expected to further improve predictive accuracy and facilitate clinical implementation. Collectively, these technologies represent a major step toward precision orthopedic oncology, enabling individualized risk assessment, optimized surgical planning, and more effective translational integration between veterinary and human medicine. Nevertheless, further prospective multicenter validation studies are required before widespread clinical adoption.