ArticleNPJ precision oncology2025
Deep learning models in classifying primary bone tumors and bone infections based on radiographs.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- BoneCoT: multicentre validation of a whole-body skeleton foundation model for bone metastases guided by clinician-derived chain of thought.Nature biomedical engineering · 2026Article
- Efficient low-dose CT image enhancement using MobileMamba-UNet with wavelet-enhanced long-range modeling.Journal of applied clinical medical physics · 2026Article
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- A large-scale vision foundation model for musculoskeletal radiographs.NPJ digital medicine · 2026Article
- A multimodal fusion model for bone tumor benign and malignant diagnosis: development and validation with clinical text and radiographs.Translational cancer research · 2026Article
- Emerging research themes in drug resistance in osteosarcoma: a 25-year bibliometric and visualized analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Bone-CNN: A Lightweight Deep Learning Architecture for Multi-Class Classification of Primary Bone Tumours in Radiographs.Biomedicines · 2026Article
- Domain Shift in Breast DCE-MRI Tumor Segmentation: A Balanced LoCoCV Study on the MAMA-MIA Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- Computer-aided detection of equivocal spinal tuberculosis on X-ray using a YOLOv11-based deep learning model.Frontiers in public health · 2026Article
- SAM3-AgeSeg: an adaptive segmentation model for bone tumors in the aging population.Frontiers in medicine · 2026Article
- Evaluation of the diagnostic accuracy of mammography and ultrasonography for breast cancer diagnosis using histopathology as the reference standard: a cross-sectional study.Annals of medicine and surgery (2012) · 2026Article
- A Novel Deep Learning Framework for Liver Fibrosis Staging and Etiology Diagnosis Using Integrated Liver-Spleen Elastography.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.Briefings in bioinformatics · 2025Article
- CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand-receptor interactions for cell-cell communication analysis.Briefings in bioinformatics · 2025Article
- Predicting bone cancer drugs properties through topological indices and machine learning.Scientific reports · 2025Article
- Bioengineered and biodegradable 3D scaffold for controlled drug delivery of 5-fluorouracil-loaded nanoparticle for bone tumor treatment.Medical oncology (Northwood, London, England) · 2025Article
- Nanoradiopharmaceuticals: Design Principles, Radiolabeling Strategies, and Biomedicine Applications.Pharmaceutics · 2025Review
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8 authors.
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Abstract
Primary bone tumors (PBTs) present significant diagnostic challenges due to their heterogeneous nature and similarities with bone infections. This study aimed to develop an ensemble deep learning framework that integrates multicenter radiographs and extensive clinical features to accurately differentiate between PBTs and bone infections. We compared the performance of the ensemble model with four imaging models based solely on radiographs utilizing EfficientNet B3, EfficientNet B4, Vision Transformer, and Swin Transformers. The patients were split into external dataset (N = 423) and internal dataset [including training (N = 1044), test (N = 354), and validation set (N = 171)]. The ensemble model outperformed imaging models, achieving areas under the curve (AUCs) of 0.948 and 0.963 on internal and external sets, respectively, with accuracies of 0.881 and 0.895. Its performance surpassed junior and mid-level radiologists and was comparable to senior radiologists (accuracy: 83.6%). These findings underscore the potential of deep learning in enhancing diagnostic precision for PBTs and bone infections (Research Registration Unique Identifying Number (UIN): researchregistry10483 and with details are available at https://www.researchregistry.com/register-now#home/registrationdetails/6693845995ba110026aeb754/ ).
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