ArticleOncology letters2019
Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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
4 citing papers in PubMed, 10 citations in OpenAlex.
- Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.Journal of imaging · 2026Article
- Case Report: Preliminary evaluation of time-resolved imaging of contrast kinetics magnetic resonance imaging for assessing temporal enhancement patterns in small animal head and neck tumors.Frontiers in veterinary science · 2026Article
- Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI.PloS one · 2023Article
- An Automated System for Analysis and Classification of Kinetic Curve on Dynamic Contrast-enhanced Magnetic Resonance Imaging Breast Lesion.Journal of medical physicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the current study was to develop a semi-automatic and quantitative method for the analysis of a time-intensity curve (TIC) from breast dynamic contrast-enhanced magnetic resonance imaging. The performance of the proposed method, based on the level set segmentation algorithm, was evaluated by comparison with the traditional method. In the traditional method, the lesion area is delineated manually and the corresponding mean TIC is classified subjectively as one of three washout patterns. In addition, only one quantitative parameter, the maximum slope of increase (MSI), is calculated. In the proposed method, the lesion region was determined semi-automatically and the corresponding mean TIC was categorized quantitatively. In addition to MSI, a number of quantitative parameters were derived from the mean TIC and lesion area, including signal intensity slope (SI
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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.