ArticleEJNMMI research2018
Accuracy of tumor segmentation from multi-parametric prostate MRI and
Article in EJNMMI research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Trial
- Real-Time MRI-Guided Prostate Interventions.Cancers · 2022Review
- Future perspective of focal therapy for localized prostate cancer.Asian journal of urology · 2021Review
- Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.Medical physics · 2021Article
- Prostate cancer GTV delineation with biparametric MRI andThe British journal of radiology · 2021Article
- ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.Medical image analysis · 2021Article
- Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy.European radiology · 2020Article
- Comparison of cross-sectional imaging techniques for the detection of prostate cancer lymph node metastasis: a critical review.Translational andrology and urology · 2020Review
- Liver-specific 3D sectioning molds for correlating in vivo CT and MRI with tumor histopathology in woodchucks (Marmota monax).PloS one · 2020Article
- Contemporary treatments in prostate cancer focal therapy.Current opinion in oncology · 2019Review
- Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer.Frontiers in oncology · 2019Article
- Dosimetric Evaluation of PSMA PET-Delineated Dominant Intraprostatic Lesion Simultaneous Infield Boosts.Advances in radiation oncologyArticle
- Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundThe study aims to assess the accuracy of multi-parametric prostate MRI (mpMRI) and
resultsAll segmentation methods greatly underestimated the true tumor volumes. Human readers (seg1, seg2) and the PET-based segmentation (seg3) underestimated an average of 79, 80, and 58% of the tumor volumes, respectively. Combining segmentation volumes (union of seg1, seg2, seg3 = seg4) decreased the mean underestimated tumor volume to 42% of the true tumor volume. When using the combined segmentation with 5 mm contour expansion, the mean underestimated tumor volume was significantly reduced to 0.03 ± 0.05 mL (2.04 ± 2.84%). Substantial safety margins up to 11-15 mm were needed to include all tumors when the initial segmentation boundaries were drawn by human readers or the semi-automated
conclusionsTo improve the outcome of focal therapies for significant prostate cancer, it is imperative to recognize the full extent of the underestimation of tumor volumes by mpMRI. Combining metabolic information from
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Registered trials
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