ArticleJournal of medical imaging (Bellingham, Wash.)2017
Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.Journal of imaging · 2026Review
- Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.Journal of clinical medicine · 2025Review
- Weakly-Supervised Detection of Bone Lesions in CT.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Article
- Review
- Application of SPECT and PET / CT with computer-aided diagnosis in bone metastasis of prostate cancer: a review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Review
- Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.Skeletal radiology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Bone metastases are a frequent occurrence with cancer, and early detection can guide the patient's treatment regimen. Metastatic bone disease can present in density extremes as sclerotic (high density) and lytic (low density) or in a continuum with an admixture of both sclerotic and lytic components. We design a framework to detect and characterize the varying spectrum of presentation of spine metastasis on positron emission tomography/computed tomography (PET/CT) data. A technique is proposed to synthesize CT and PET images to enhance the lesion appearance for computer detection. A combination of watershed, graph cut, and level set algorithms is first run to obtain the initial detections. Detections are then sent to multiple classifiers for sclerotic, lytic, and mixed lesions. The system was tested on 44 cases with 225 sclerotic, 139 lytic, and 92 mixed lesions. The results showed that sensitivity (false positive per patient) was 0.81 (2.1), 0.81 (1.3), and 0.76 (2.1) for sclerotic, lytic, and mixed lesions, respectively. It also demonstrates that using PET/CT data significantly improves the computer aided detection performance over using CT alone.
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Registered trials
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