ArticleFrontiers in oncology2021
Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.Renal failure · 2023Trial
- Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features.BMC women's health · 2024Article
- An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.Frontiers in oncology · 2023Article
- Artificial intelligence diagnosis based on breast ultrasound imaging.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
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8 authors.
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Abstract
objectivesMucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA. MATERIALS AND
methodsThe study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA.
resultsThe nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000-0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively.
conclusionOur study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.
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