ArticleBreast cancer research and treatment2019
Preliminary results of computer-aided diagnosis for magnetic resonance imaging of solid breast lesions.
Article in Breast cancer research and treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Review
- Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system.Health and technology · 2023Review
- Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction.Healthcare (Basel, Switzerland) · 2022Article
- Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer.Frontiers in oncology · 2021Article
- Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer.The British journal of radiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to compare magnetic resonance (MR)-CAD with MR imaging (MRI) in distinguishing benign from malignant solid breast masses.
methodsWe analyzed a total of 251 patients (mean age: 44.8 ± 12.3 years; range: 21-81 years) with 274 breast masses (154 benign masses, 120 malignant masses) using a Gaussian mixture model and a random forest machine model for segmentation and classification.
resultsThe diagnostic performance of MRI alone and MRI plus CAD were compared with respect to sensitivity, specificity, and area under the curve (AUC), using receiver operating characteristic curve analysis. The discriminating power to detect malignancy using MR-CAD with an AUC of 0.955 (sensitivity was 95.8% and the specificity was 92.9%) was significantly higher than that of MRI alone with an AUC of 0.785 (sensitivity was 71.7% and the specificity was 85.7%).
conclusionCAD is feasible to differentiate breast lesions, and it can complement MRI, thereby making it easier to diagnose breast lesions and obviating the need for unnecessary biopsies.
Indexed as
Identifiers
What OpenQuestion holds
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.