ArticleDiagnostics (Basel, Switzerland)2020
A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.The British journal of radiology · 2021Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- U-Net benign prostatic hyperplasia-trained deep learning model for prostate ultrasound image segmentation in prostate cancer.Quantitative imaging in medicine and surgery · 2025Article
- Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.PloS one · 2025Article
- Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients.Abdominal radiology (New York) · 2024Article
- Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study.Discover oncology · 2023Article
- Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
- Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images.Journal of personalized medicine · 2023Article
- Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges.European radiology experimental · 2022Review
- Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.Magma (New York, N.Y.) · 2022Article
- The role of MRI in prostate cancer: current and future directions.Magma (New York, N.Y.) · 2022Review
- The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images.Diagnostics (Basel, Switzerland) · 2021Article
- Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization.International journal of molecular sciences · 2021Review
- Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review.Cancers · 2021Review
- Radiomics in prostate cancer: an up-to-date review.Therapeutic advances in urologyReview
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.