ArticleRadiology2013
Automated detection of sclerotic metastases in the thoracolumbar spine at CT.
Article in Radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT00057252 (Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images), which is not on this map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
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.
Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Automated detection of bone lesions using CT and MRI: a systematic review.La Radiologia medica · 2024Pooled it
- BoneCoT: multicentre validation of a whole-body skeleton foundation model for bone metastases guided by clinician-derived chain of thought.Nature biomedical engineering · 2026Article
- Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout.Journal of imaging informatics in medicine · 2025Article
- 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
- Weakly-Supervised Detection of Bone Lesions in CT.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
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- Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.International journal of computer assisted radiology and surgery · 2023Article
- Review
- Review
- 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
- Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods.Brain & spine · 2022Review
- Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions.Global spine journal · 2021Article
- Artificial intelligence in musculoskeletal oncological radiology.Radiology and oncology · 2020Article
- 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
- Article
- Progress in Fully Automated Abdominal CT Interpretation.AJR. American journal of roentgenology · 2016Review
Corrections and comments
- Erratum issuedResponse.2013
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images. MATERIALS AND
methodsThis retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm(3) in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed.
resultsLesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). False-negative findings were most commonly (37 [40%] of 93) a result of endplate proximity, with 32 (34% of 93) caused by low CT attenuation. Marginal sclerosis caused by degenerative change (174 [28.1%] of 620 actual detections) was the most common cause of false-positive detections, followed by partial volume averaging with vertebral endplates (173 [27.9%] of 620) and pedicle cortex parallel to the axial imaging plane (121 [19.5%] 620).
conclusionThis CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.
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Registered trials
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.