ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
Deep learning-based fully automated detection and segmentation of pelvic lymph nodes on diffusion-weighted images for prostate cancer: a multicenter study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The diagnostic value of radiomics-based machine learning for lymph node metastasis in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- CT Radiomics Models Did Not Outperform Experts in Predicting [Current oncology (Toronto, Ont.) · 2026Article
- Critical dialogue: how imaging evaluates and guides pelvic exenteration surgery-a multidisciplinary perspective.Insights into imaging · 2026Article
- Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early,Brain communications · 2026Article
- BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI.BioData mining · 2025Article
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Abstract
backgroundAccurate identification and evaluation of lymph nodes (LNs) in prostate cancer (PCa) patients is crucial for effective staging but can be time-consuming. We utilized a 3D V-Net model to improve the efficiency and accuracy of LN detection and segmentation.
methodsUtilizing pelvic diffusion-weighted imaging (DWI) scans, the 3D V-Net framework underwent training on a dataset comprising data from a hospital with 1,151 patients, encompassing 32,507 annotated LNs, following data augmentation procedures. Subsequently, external validation was conducted on data from 401 patients across three additional hospitals, encompassing 7,707 LNs. The segmentation performance was evaluated using the Dice similarity coefficient (DSC). The comparison between automated and manual segmentation regarding the short diameter and volume of LNs was conducted using Bland-Altman plots and correlation analysis. The performance for suspicious metastatic LN detection (short diameter > 8 mm) was evaluated using sensitivity, positive predictive value (PPV), and per-patient false-positive rate (FP/vol) at the LN level and sensitivity, specificity, and PPV at the patient level.
resultsIn the external validation test dataset, the model achieved a DSC of 0.77-0.82 for all, suspicious, and largest LNs. The model achieved a sensitivity, PPV, and FP/vol of 60.1% (95% confidence interval (CI), 57.6-62.6%), 79.2% (95% CI, 76.6-81.5%), and 0.56 at the LN level, respectively. At the patient level, the model achieved a sensitivity, specificity, and PPV of 81.1% (95% CI, 76.5-85.0%), 75.6% (95% CI, 65.1-83.8%), and 93.2% (95% CI, 89.7-95.6%), respectively. The model achieved a strong correlation and good consistency between the short diameter and volume of the automatically segmented and manually annotated LNs.
conclusionThis 3D V-Net model can segment LNs effectively based on pelvic DWI images for PCa and holds great potential for facilitating N-staging in clinical practice.
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