ArticleEuropean radiology2022
A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- Performance of multimodal large language models for the detection and characterization of bone lesions on radiographs.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Development and validation of a deep learning model for radiographic classification of pediatric femoral neck fractures.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- A clinical-radiomics model based on multiparametric MRI for discriminating solitary primary spinal tumors from solitary spinal metastases.BMC medical imaging · 2026Article
- Medical image pretraining-based transfer learning for generalizable and robust diagnosis of bone tumors on radiographs: a multi-center study.Insights into imaging · 2026Article
- Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation.Insights into imaging · 2026Article
- A multimodal fusion model for bone tumor benign and malignant diagnosis: development and validation with clinical text and radiographs.Translational cancer research · 2026Article
- Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.Frontiers in bioengineering and biotechnology · 2026Article
- Research progress of artificial intelligence in bone tumor imaging.Frontiers in oncology · 2026Review
- Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.International journal of biomedical imaging · 2026Review
- Diagnostic performance of dual-layer spectral CT Radiomics and deep learning for differentiating osteoblastic bone metastases from bone islands.European journal of radiology open · 2025Article
- Multimodal deep learning for bone tumor diagnosis with clinical imaging, pathology, and blood biomarkers.Journal of bone oncology · 2025Article
- End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study.NPJ precision oncology · 2025Article
- Enhancing YOLOv11 with Large Kernel Attention and Multi-Scale Fusion for Accurate Small and Multi-Lesion Bone Tumor Detection in Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors.Scientific data · 2025Article
- Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning.Health information science and systems · 2024Article
- Impact of metadata in multimodal classification of bone tumours.BMC musculoskeletal disorders · 2024Article
- A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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Funding
Abstract
objectivesTo build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion.
methodsIn this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data. Five radiologists compared diagnostic performance with and without the model. Diagnostic performance was evaluated using the area under the curve (AUC).
resultsA total of 643 patients' (median age, 21 years; interquartile range, 12-38 years; 244 women) 982 radiographs were included. In the test set, the binary category classification task, the radiological model of classification for benign/not benign, malignant/nonmalignant, and intermediate/not intermediate had AUCs of 0.846, 0.827, and 0.820, respectively; the fusion models had an AUC of 0.898, 0.894, and 0.865, respectively. In the three-category classification task, the radiological model achieved a macro average AUC of 0.813, and the fusion model had a macro average AUC of 0.872. In the observation test, the mean macro average AUC of all radiologists was 0.819. With the three-category classification fusion model support, the macro AUC improved by 0.026.
conclusionWe built, validated, and tested deep learning and machine learning models that classified bone tumors at a level comparable with that of senior radiologists. Model assistance may somewhat help radiologists' differential diagnoses of bone tumors. KEY POINTS: • The deep learning model can be used to classify benign, malignant, and intermediate bone tumors. • The machine learning model fusing information from radiographs and clinical characteristics can improve the classification capacity for bone tumors. • The diagnostic performance of the fusion model is comparable with that of senior radiologists and is potentially useful as a complement to radiologists in a bone tumor differential diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.