Breast cancer prediction model based on clinical and biochemical characteristics: clinical data from patients with benign and malignant breast tumors from a single center in South China
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Oncology,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00432-023-05181-4.pdf
Reference41 articles.
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3. Bent CK, Bassett LW, D’Orsi CJ, Sayre JW (2010) The positive predictive value of BI-RADS microcalcification descriptors and final assessment categories. AJR Am J Roentgenol 194(5):1378–1383. https://doi.org/10.2214/AJR.09.3423
4. Bhadra T, Mallik S, Hasan N, Zhao Z (2022) Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer. BMC Bioinform 23(Suppl 3):153. https://doi.org/10.1186/s12859-022-04678-y. (PMID:35484501; PMCID:PMC9052461)
5. Burnside ES, Rubin DL, Fine JP, Shachter RD, Sisney GA, Leung WK (2006) Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology 240(3):666–673. https://doi.org/10.1148/radiol.2403051096
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