ArticleDiagnostics (Basel, Switzerland)2021
Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2021. 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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Who cites it
22 citing papers in PubMed.
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- A Deep Learning Model for Comprehensive Automated Bone Lesion Detection and Classification on Staging Computed Tomography Scans.Academic radiology · 2025Article
- A comprehensive deep learning approach to improve enchondroma detection on X-ray images.Scientific reports · 2025Article
- 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 Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.Academic radiology · 2024Article
- Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects.Journal of clinical medicine · 2024Review
- SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.Acta medica Philippina · 2024Article
- Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training.Diagnostics (Basel, Switzerland) · 2023Article
- Skeleton Segmentation on Bone Scintigraphy for BSI Computation.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.Journal of personalized medicine · 2022Article
- Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images.EJNMMI research · 2022Article
- Automated detection of lung cancer-caused metastasis by classifying scintigraphic images using convolutional neural network with residual connection and hybrid attention mechanism.Insights into imaging · 2022Article
- Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.Frontiers in oncology · 2022Review
- Semi-supervised segmentation of metastasis lesions in bone scan images.Frontiers in molecular biosciences · 2022Article
- Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.BMC medical imaging · 2021Article
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Authors and funding
4 authors.
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Abstract
This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians' final decisions.
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