ArticleRadiologia brasileira
Refining the assessment of BI-RADS 4 lesions on breast magnetic resonance imaging.
Article in Radiologia brasileira. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To evaluate the positive predictive value (PPV) of the imaging characteristics of breast lesions classified as BI-RADS category 4 (risk of malignancy > 2% to < 95%) on magnetic resonance imaging (MRI), in order to create an algorithm to subcategorize such lesions. Materials and Methods: This was a retrospective study including 199 breast lesions (131 nodules and 68 non-mass lesions) classified as BI-RADS 4 on MRI. Of the 199 lesions, 93 were excluded, for various reasons: they were lymph nodes; they were not biopsied or were not followed at our center; they were additional findings in patients with an established diagnosis of malignancy who underwent mastectomy without further investigation; or they were identified in examinations that were not recovered from the digital archive. Multivariate analysis was performed to identify the most relevant descriptors to predict malignancy and to build an algorithm to subcategorize lesions into BI-RADS 4A, 4B, and 4C. Four breast radiologists then tested the algorithm in another 95 patients with breast lesions classified as BI-RADS 4 on MRI, 27 (28.4%) of those lesions having previously been classified as malignant. Results: The descriptors statistically associated with malignancy in the multivariate analysis of the nodules were background parenchymal enhancement, margins, and the initial phase of the kinetic curve. An algorithm was developed by using these resources, and the PPV obtained for each category was 4.3% for BI-RADS 4A, 21.4% for BI-RADS 4B, and 78.9% for BI-RADS 4C. In the validation of the algorithm by the four breast radiologists, the PPV of the subcategories was within the BI-RADS malignancy ranges in almost all situations, the exceptions being the 11.1% that one evaluator obtained for category 4A and the 46.4% obtained for category 4C by another evaluator. Conclusion: The objective analysis employing the proposed algorithm proved useful for subdividing BI-RADS 4 mass lesions on MRI and showed better interobserver agreement than did the subjective analysis.
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.