ArticleEuropean radiology2013
Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography.
Article in European radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Automated detection of bone lesions using CT and MRI: a systematic review.La Radiologia medica · 2024Pooled it
- Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout.Journal of imaging informatics in medicine · 2025Article
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans.Nature communications · 2025Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- Computational Modeling, Augmented Reality, and Artificial Intelligence in Spine Surgery.Advances in experimental medicine and biology · 2024Review
- Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.International journal of computer assisted radiology and surgery · 2023Article
- Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography.Frontiers in oncology · 2023Article
- Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings.Quantitative imaging in medicine and surgery · 2022Article
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- Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI.Insights into imaging · 2022Article
- Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.Skeletal radiology · 2022Article
- Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions.Global spine journal · 2021Article
- Rapid study assessment in follow-up whole-body computed tomography in patients with multiple myeloma using a dedicated bone subtraction software.European radiology · 2020Article
- Applications of Machine Learning Using Electronic Medical Records in Spine Surgery.Neurospine · 2019Article
- Artificial intelligence and machine learning in spine research.JOR spine · 2019Review
- Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases.Radiology · 2017Article
- Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier.Journal of medical imaging (Bellingham, Wash.) · 2017Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT).
methodsWe retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed.
resultsAlgorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector.
conclusionThe CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists' accuracy and efficiency in a clinical setting. KEY POINTS: • Computer-aided detection (CADe) of bone metastases has been developed for spinal CT. • The CADe system exhibits high sensitivity with a tolerable false-positive rate. • Analysis of false-positive detection may further improve the system. • CADe may reduce the number of missed spinal metastases at CT interpretation.
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