ArticleFrontiers in oncology2021
Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.Journal of bone oncology · 2026Review
- Fibrosis of Periprostatic Adipose Tissue: A Potential Marker of Prostate Cancer Aggressiveness.Cancers · 2026Article
- Deep learning-based detection and segmentation of osseous metastatic prostate cancer lesions on computed tomography.European journal of radiology artificial intelligence · 2025Article
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Management of metastatic bone disease of the pelvis: current concepts.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Review
- Uncertainty quantification via localized gradients for deep learning-based medical image assessments.Physics in medicine and biology · 2024Article
- A convolutional neural network-based system for fully automatic segmentation of whole-body [European journal of nuclear medicine and molecular imaging · 2024Article
- Segmentation of pelvic structures in T2 MRI via MR-to-CT synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.Biomedicines · 2023Article
- RECIST 1.1 Target Lesion Categorical Response in Metastatic Renal Cell Carcinoma: A Comparison of Conventional versus Volumetric Assessment.Radiology. Imaging cancer · 2023Article
- Automatic segmentation of the female pelvic floor muscles on MRI for pelvic floor function assessment.Quantitative imaging in medicine and surgery · 2023Article
- Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
- Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.Frontiers in oncology · 2023Review
- Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [European journal of nuclear medicine and molecular imaging · 2022Article
- Article
- Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using diffusion-weighted imaging (DWI) and T1 weighted imaging (T1WI) images.
methodsThe model consisted of two 3D U-Net algorithms. A total of 859 patients with clinically suspected or confirmed PCa between January 2017 and December 2020 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. Then, 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from January to May 2021 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision, and F1-score were assessed at the lesion level.
resultsThe pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85, and the HD values were <15 mm. In the testing set, the AUC of the metastases detection at the patient level were 0.85 and 0.80 on DWI and T1WI images. At the lesion level, the F1-score achieved 87.6% and 87.8% concerning metastases detection on DWI and T1WI images, respectively. In the external dataset, the AUC of the model for M-staging was 0.94 and 0.89 on DWI and T1WI images.
conclusionThe deep learning-based 3D U-Net network yields accurate detection and segmentation of pelvic bone metastases for PCa patients on DWI and T1WI images, which lays a foundation for the whole-body skeletal metastases assessment.
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