Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound.

Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to...

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Publicado en:Medical & Biological Engineering & Computing Vol. 58; no. 6; pp. 1285 - 1296
Autores principales: Chen, Fang, Liu, Jia, Wan, Peng, Liao, Hongen, Kong, Wentao
Formato: pictorial research tables/charts Journal Article
Publicado: Springer Nature Jun2020
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound.
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          Chen, Fang
          Liu, Jia
          Wan, Peng
          Liao, Hongen
          Kong, Wentao
        affil: Department of Computer Science and Engineering, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
      sug:
        subj:
          Imaging, Three-Dimensional Methods
          Immunohistochemistry Methods
          Diagnosis, Computer Assisted Methods
          Breast Neoplasms
          Ultrasonography Methods
          Breast Neoplasms Pathology
          Contrast Media
          Middle Age
          Sensitivity and Specificity
          Receptors, Cell Surface Metabolism
          Aged
          Retrospective Design
          Female
          Adult
          Funding Source
          Middle Aged: 45-64 years
          Aged: 65+ years
          Adult: 19-44 years
          Female
      ab: Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to determine immunohistochemistry index. However, this examination is invasive and commonly causes discomfort in patients. There has been a lack of noninvasive method capable of predicting immunohistochemistry index for breast cancer patients. This paper proposes a machine learning method to predict the immunohistochemical index of breast cancer patients by using noninvasive contrast-enhanced ultrasound. A total of 119 breast cancer patients were included in this retrospective study. Each patient implemented the pathological examination of immunohistochemical expression and underwent contrast-enhanced ultrasound imaging of breast tumor. The multi-dimension features including 266 three-dimension features and 837 two-dimension dynamic features were extracted from the contrast-enhanced ultrasound sequences. Using the machine learning prediction method, 21 selected multi-dimension features were integrated to generate a model for predicting the immunohistochemistry index noninvasively. The immunohistochemical index of human epidermal growth factor receptor-2 (HER2) was predicted based on multi-dimension features in contrast-enhanced ultrasound sequence with the sensitivity of 71%, and the specificity of 79% in the testing cohort. Therefore, the noninvasive contrast-enhanced ultrasound can be used to predict the immunohistochemical index. To our best knowledge, no studies have been reported about predicting immunohistochemical index by using contrast-enhanced ultrasound sequences for breast cancer patients. Our proposed method is noninvasive and can predict immunohistochemical index by using contrast-enhanced ultrasound in several minutes, instead of relying totally on the invasive and biopsy-based histopathological examination. Graphical abstract Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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