ArticleJournal of breast cancer2022
The Predictive Value of Magnetic Resonance Imaging-based Texture Analysis in Evaluating Histopathological Grades of Breast Phyllodes Tumor.
Article in Journal of breast cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Integrated multi-regional multiomic profiling of breast phyllodes tumours reveals peritumoural immune activation and stromal remodelling.Clinical and translational medicine · 2026Article
- Integrating single-cell and spatial transcriptomes reveals COL4A1/2 facilitates the spatial organisation of stromal cells differentiation in breast phyllodes tumours.Clinical and translational medicine · 2024Article
- Differentiation of neurogenic tumours and pleomorphic adenomas in the parapharyngeal space based on texture analysis of T2WI.BMC oral health · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeKnowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs.
methodsForty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy.
resultsCompared with benign PTs, borderline/malignant ones presented a higher local recurrence (
conclusionMRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.
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