ArticleCancer medicine2020
Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review.Skeletal radiology · 2023Pooled it
- Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance.Bioengineering (Basel, Switzerland) · 2026Article
- Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.Radiation protection dosimetry · 2026Article
- Whole-mount histopathology as spatial ground truth for artificial intelligence in prostate cancer: a structured narrative review of techniques, models, and translational gaps.American journal of clinical and experimental urology · 2026Review
- Non-invasive prediction of lymph node involvement in prostate cancer via machine learning on whole-prostate MRI.Frontiers in oncology · 2026Article
- PARALLEL ROUTE COHERENT MIXUP DEEP LEARNING FOR SINGLE SOURCE TO MULTI-DOMAIN GENERALIZED PROSTATE CANCER SEGMENTATION FOR MR GUIDED ADAPTIVE RADIOTHERAPY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy.Journal of biomedical physics & engineering · 2025Article
- Review
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
- Factors of interobserver variability in prostate tumor MRI delineation: impact of PI-QUAL score.La Radiologia medica · 2024Article
- Quality assurance of 3D-printed patient specific anatomical models: a systematic review.3D printing in medicine · 2024Article
- A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study.BMC medical imaging · 2024Article
- Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy.Physics and imaging in radiation oncology · 2024Article
- Inter-Rater Variability of Prostate Lesion Segmentation on Multiparametric Prostate MRI.Biomedicines · 2023Article
- Navigating the intersection of 3D printing, software regulation and quality control for point-of-care manufacturing of personalized anatomical models.3D printing in medicine · 2023Article
- Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model.Quantitative imaging in medicine and surgery · 2022Article
- Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology.Radiation oncology (London, England) · 2022Article
- Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.Radiation oncology (London, England) · 2021Article
- Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists.Cancer medicine · 2020Article
- Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is increasing research in using segmentation of prostate cancer to create a digital 3D model from magnetic resonance imaging (MRI) scans for purposes of education or surgical planning. However, the variation in segmentation of prostate cancer among users and potential inaccuracy has not been studied.
methodsFour consultant radiologists, four consultant urologists, four urology trainees, and four nonclinician segmentation scientists were asked to segment a single slice of a lateral T3 prostate tumor on MRI ("Prostate 1"), an anterior zone prostate tumor MRI ("Prostate 2"), and a kidney tumor computed tomography (CT) scan ("Kidney"). Time taken and self-rated subjective accuracy out of a maximum score of 10 were recorded. Root mean square error, Dice coefficient, Matthews correlation coefficient, Jaccard index, specificity, and sensitivity were calculated using the radiologists as the ground truth.
resultsThere was high variance among the radiologists in segmentation of Prostate 1 and 2 tumors with mean Dice coefficients of 0.81 and 0.58, respectively, compared to 0.96 for the kidney tumor. Urologists and urology trainees had similar accuracy, while nonclinicians had the lowest accuracy scores for Prostate 1 and 2 tumors (0.60 and 0.47) but similar for kidney tumor (0.95). Mean sensitivity in Prostate 1 (0.63) and Prostate 2 (0.61) was lower than specificity (0.92 and 0.93) suggesting under-segmentation of tumors in the non-radiologist groups. Participants spent less time on the kidney tumor segmentation and self-rated accuracy was higher than both prostate tumors.
conclusionSegmentation of prostate cancers is more difficult than other anatomy such as kidney tumors. Less experienced participants appear to under-segment models and underestimate the size of prostate tumors. Segmentation of prostate cancer is highly variable even among radiologists, and 3D modeling for clinical use must be performed with caution. Further work to develop a methodology to maximize segmentation accuracy is needed.
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