ArticleEuropean journal of nuclear medicine and molecular imaging2022
Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 57 citations in OpenAlex.
- Feasibility of No-Code Deep Learning for Diagnosing Bone Metastasis in Bone Scans: A Comparative Study of Teachable Machine and ResNet.Journal of imaging informatics in medicine · 2026Article
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- Artificial Intelligence and Prostate-Breast Cancer Biology: Recent Advances in Treatments and Perspectives - A Comprehensive Review.Health science reports · 2026Article
- Assessing an Automated Noncontrast CT-based Pipeline for Sacral Tumor Classification Using a Hip Bone Reference Frame.Radiology. Imaging cancer · 2026Article
- YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study.JMIR medical informatics · 2025Article
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Artificial Intelligence and Radiomics Applied to Prostate Cancer Bone Metastasis Imaging: A Review.iRadiology · 2024Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- Deep learning-based measurement of split glomerular filtration rate withEJNMMI physics · 2024Article
- Diagnostic performance of a deep-learning model usingAnnals of nuclear medicine · 2024Article
- Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.Zeitschrift fur medizinische Physik · 2024Article
- Performance of deep learning models for response evaluation on whole-body bone scans in prostate cancer.Annals of nuclear medicine · 2023Article
- ChatGPT-assisted deep learning for diagnosing bone metastasis in bone scans: Bridging the AI Gap for Clinicians.Heliyon · 2023Article
- Imaging signs and the qualitative diagnosis of solitary rib lesions usingQuantitative imaging in medicine and surgery · 2023Article
- Deep learning-based detection of patients with bone metastasis from Japanese radiology reports.Japanese journal of radiology · 2023Article
- MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization.Nuclear medicine and molecular imaging · 2023Article
- Review
- Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.Frontiers in oncology · 2022Review
- Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams.Cancer management and research · 2022Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeWe evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients.
methodsA total of 9113 consecutive bone scans (5342 prostate cancer patients) were initially evaluated. Bone scans were labeled as positive/negative for bone metastasis using clinical reports and image review for ground truth diagnosis. Two different 2D convolutional neural network (CNN) architectures were proposed: (1) whole body-based (WB) and (2) tandem architectures integrating whole body and local patches, here named as "global-local unified emphasis" (GLUE). Both models were trained using abundant (72%:8%:20% for training:validation:test sets) and limited training data (10%:40%:50%). The allocation of test sets was rotated across all images: therefore, fivefold and twofold cross-validation test results were available for abundant and limited settings, respectively.
resultsA total of 2991 positive and 6142 negative bone scans were used as input. For the abundant training setting, the receiver operating characteristics curves of both the GLUE and WB models indicated excellent diagnostic ability in terms of the area under the curve (GLUE: 0.936-0.955, WB: 0.933-0.957, P > 0.05 in four of the fivefold tests). The overall accuracies of the GLUE and WB models were 0.900 and 0.889, respectively. With the limited training setting, the GLUE models showed significantly higher AUCs than the WB models (0.894-0.908 vs. 0.870-0.877, P < 0.0001).
conclusionOur 2D-CNN models accurately classified bone scans of prostate cancer patients. While both showed excellent performance with the abundant dataset, the GLUE model showed higher performance than the WB model in the limited data setting.
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