ArticleRadiology2016
Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.
Article in Radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
31 citing papers in PubMed.
- Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.Journal of magnetic resonance imaging : JMRI · 2017Trial
- Early Differentiation of Prostatic Intraepithelial Neoplasia via Multiparametric MRI Combined with PSA-derived ParametersCurrent medical imaging · 2026Article
- Comparison of tracer kinetic models in differentiating malignant from normal prostate tissue using dynamic contrast-enhanced MRI.Frontiers in oncology · 2024Article
- Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
- Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition.European journal of radiology · 2023Article
- Effects of dynamic contrast enhancement on transition zone prostate cancer in Prostate Imaging Reporting and Data System Version 2.1.Radiology and oncology · 2023Article
- Review
- Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate-Can We Make Better Use of It?Tomography (Ann Arbor, Mich.) · 2022Article
- Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology.Magnetic resonance imaging · 2022Article
- MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Diagnostic Performance of PI-RADS v2, Proposed Adjusted PI-RADS v2 and Biparametric Magnetic Resonance Imaging for Prostate Cancer Detection: A Preliminary Study.Current oncology (Toronto, Ont.) · 2021Article
- The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Article
- Radiomics of Multiparametric MRI to Predict Biochemical Recurrence of Localized Prostate Cancer After Radiation Therapy.Frontiers in oncology · 2020Article
- Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.Tomography (Ann Arbor, Mich.) · 2019Article
- Radiomics-Based Intracranial Thrombus Features on CT and CTA Predict Recanalization with Intravenous Alteplase in Patients with Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2019Article
- Automatic Detection of Prostate Tumor Habitats using Diffusion MRI.Scientific reports · 2018Article
- Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study.European radiology · 2018Article
- Prostate imaging features that indicate benign or malignant pathology on biopsy.Translational andrology and urology · 2018Review
- Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer.International journal of radiation oncology, biology, physics · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo determine the best features to discriminate prostate cancer from benign disease and its relationship to benign disease class and cancer grade. MATERIALS AND
methodsThe institutional review board approved this study and waived the need for informed consent. A retrospective cohort of 70 patients (age range, 48-70 years; median, 62 years), all of whom were scheduled to undergo radical prostatectomy and underwent preoperative 3-T multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging, were included. The digitized prostatectomy slides were annotated for cancer and noncancerous disease and coregistered to MR imaging with an interactive deformable coregistration scheme. Computer-identified features for each of the noncancerous disease categories (eg, benign prostatic hyperplasia [BPH], prostatic intraepithelial neoplasia [PIN], inflammation, and atrophy) and prostate cancer were extracted. Feature selection was performed to identify the features with the highest discriminatory power. The performance of these five features was evaluated by using the area under the receiver operating characteristic curve (AUC).
resultsHigh-b-value diffusion-weighted images were more discriminative in distinguishing BPH from prostate cancer than apparent diffusion coefficient, which was most suitable for distinguishing PIN from prostate cancer. The focal appearance of lesions on dynamic contrast-enhanced images may help discriminate atrophy and inflammation from cancer. Which imaging features are discriminative for different benign lesions is influenced by cancer grade. The apparent diffusion coefficient appeared to be the most discriminative feature in identifying high-grade cancer. Classification results showed increased performance by taking into account specific benign types (AUC = 0.70) compared with grouping all noncancerous findings together (AUC = 0.62).
conclusionThe best features with which to discriminate prostate cancer from noncancerous benign disease depend on the type of benign disease and cancer grade. Use of the best features may result in better diagnostic performance.
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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.