Methodology for the application of data science in breast cancer diagnosis

Yıl 2023, Cilt: 3 Sayı: 2, 106 - 117, 01.12.2023

Jorge Armando Millan Gomez Lilia Edith Aparicio Pico

Öz

In 2020 the detected cases of breast cancer in Colombia were 15,509 of which 4,411 ended in death. The anticipate prognosis of this disease has become a research need because it can facilitate preventive treatment to avoid its lethality in an advanced stage. This paper proposes the DSM-BCD methodology (Data science methodology for breast cancer diagnosis) designed to speed up the diagnosis of breast cancer through the continuous improvement of Machine Learning and Deep Learning techniques based on the insight of the oncology specialist and the feedback of knowledge according to the behavior of the data in the various techniques for the detection of breast cancer.

Anahtar Kelimeler

Breast Cancer, Data Science, Diagnostics, Machine Learning, Methodology

Destekleyen Kurum

UNIVERSIDAD DISTRITAL FRANCISCO JOSÉ DE CALDAS

Kaynakça

• [1] International Agency for Research on Cancer. 170 Colombia fact sheets. Vol. 509, Globocan 2020. 2020.
• [2] Sauer AG, Jemal A, Siegel RL, Miller KD. Breast Cancer Facts & Figures 2019-2020. Oncology. CA - A Cancer Journal for Clinicians; 2019. p. 1–43.
• [3] Duarte C, Salazar A, Strasser-Weippl K, de Vries E, Wiesner C, Arango-Gutiérrez A, et al. Breast cancer in Colombia: a growing challenge for the healthcare system. Breast Cancer Res Treat [Internet]. 2021;186(1):15–24. Available from: https://doi.org/10.1007/s10549-020-06091-6
• [4] Turin A. A Turing test for artificial intelligence in cancer. Vol. 1, Nature Reviews Cancer. 2020.
• [5] Mann RM, Hooley R, Barr RG, Moy L. Novel approaches to screening for breast cancer. Radiology. 2020;297(2):266–85.
• [6] Baker DJ. Artificial Intelligence: The Future Landscape of Genomic Medical Diagnosis: Dataset, In Silico Artificial Intelligent Clinical Information, and Machine Learning Systems [Internet]. Human Genome Informatics: Translating Genes into Health. Elsevier Inc.; 2018. 223–267 p. Available from: http://dx.doi.org/10.1016/B978-0-12-809414-3.00011-5
• [7] Pillai N. Data Science for all (DS4A). In: MinTIC-. Bogota, Colombia: Correlation One; 2020.
• [8] Rollins JB. Foundational Methodology for Data Science. IBM. 2015;
• [9] Bland KI, Copeland EM. The Breast: comprehensive management of benign and malignant diseases. Saunders/Elsevier; 2009. 62 p.
• [10] Fatima N, Liu L, Hong S, Ahmed H. Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis. IEEE Access. 2020; 8:150360–76.
• [11] Sun YS, Zhao Z, Yang ZN, Xu F, Lu HJ, Zhu ZY, et al. Risk Factors and Preventions of Breast Cancer. Int J Biol Sci [Internet]. 2017 [cited 2022 Apr 18];13(11):1387. Available from: /pmc/articles/PMC5715522/
• [12] Hou R, Mazurowski MA, Grimm LJ, Marks JR, King LM, Maley CC, et al. Prediction of upstaged ductal carcinoma in situ using forced labeling and domain adaptation. IEEE Trans Biomed Eng. 2020 Jun 1;67(6):1565–72.
• [13] Chaudhury AR, Iyer R, Iychettira KK, Sreedevi A. Diagnosis of invasive ductal carcinoma using image processing techniques. ICIIP 2011 - Proceedings: 2011 International Conference on Image Information Processing. 2011;
• [14] Page DL, Dupont WD, Rogers LW, Landenberger M. lntraductal Carcinoma of the Breast: Follow-up After Biopsy Only. American Cancer Society. 1982;
• [15] A. B. T, O’Malley FP, Singhal H, Tonkin KS. Osteopontin and p53 expression are associated with tumor progression in a case of synchronous, bilateral, invasive mammary carcinomas. Arch Pathol Lab Med. 1997;
• [16] Lee B, Kim K, Choi JY, Suh DH, No JH, Lee HY, et al. Efficacy of the multidisciplinary tumor board conference in gynecologic oncology. Medicine (United States) [Internet]. 2017; [cited 2022 Apr 18];96(48). Available from: https://journals.lww.com/md-journal/Fulltext/2017/12010/Efficacy_of_the_multidisciplinary_tumor_board.4.aspx
• [17] Masciari S, Larsson N, Senz J, Boyd N, Kaurah P, Kandel MJ, et al. Germline E-cadherin mutations in familial lobular breast cancer. J Med Genet [Internet]. 2007 Nov 1 [cited 2022 Apr 18];44(11):726–31. Available from: https://jmg.bmj.com/content/44/11/726
• [18] Memis A, Ozdemir N, Parildar M, Ustun EE, Erhan Y. Mucinous (colloid) breast cancer: Mammographic and US features with histologic correlation. Eur J Radiol. 2000;35(1):39–43.
• [19] Gradilone A, Naso G, Raimondi C, Cortesi E, Gandini O, Vincenzi B, et al. Circulating tumor cells (CTCs) in metastatic breast cancer (MBC): prognosis, drug resistance and phenotypic characterization. Annals of Oncology. 2011 Jan 1;22(1):86–92.
• [20] Robertson FM, Bondy M, Yang W, Yamauchi H, Wiggins S, Kamrudin S, et al. Inflammatory Breast Cancer: The Disease, the Biology, the Treatment. CA Cancer J Clin. 2010 Nov 1;60(6):351–75.
• [21] Brunicardi CF. Schwartz- Principles of Surgery. Novena. Andersen DK, R. Billiar T, Dunn DL, Hunter JG, Matthews JB, Pollock RE, editors. Vol. 9. Bogotá: McGRAW-HILL INTERAMERICANA EDITORES, S. A. de C. V; 2010. 424–469
• [22] Tamam N, Salah H, Rabbaa M, Abuljoud M, Sulieman A, Alkhorayef M, et al. Evaluation of patients radiation dose during mammography imaging procedure. Radiation Physics and Chemistry. 2021 Nov 1;188.
• [23] Ebrahimi M. Breast imaging: Mammography, digital tomosynthesis, dynamic contrast enhancement. Encyclopedia of Biomedical Engineering. 2019 Jan 1;1–3:501–4.
• [24] Hirose M, Nobusawa H, Gokan T. MR ductography: Comparison with conventional ductography as a diagnostic method in patients with nipple discharge. Radiographics. 2007 Oct;27(SPEC. ISS.).
• [25] Hasan MK, Ara SR. Detection and classification of breast lesions using ultrasound-based imaging modalities. Encyclopedia of Biomedical Engineering. 2019 Jan 1;1–3:331–48.
• [26] Tse GM, Yeung DKW, Chu WCW. MRI of the Breast. Comprehensive Biomedical Physics. 2014 Jul 25; 3:205–20.
• [27] Greenfield LJ, Mulholland MW, Ovid Technologies Inc. Greenfield’s surgery: scientific principles and practice. Wolters Kluwer Health; 2012.
• [28] Obeng-Gyasi S, Grimm LJ, Hwang ES, Klimberg VS, Bland KI. Indications and techniques for biopsy. The Breast: Comprehensive Management of Benign and Malignant Diseases. 2018;377-385.e2.
• [29] Martinez I, Viles E, G. Olaizola I. Data Science Methodologies: Current Challenges and Future Approaches. Big Data Research [Internet]. 2021; 24:100183. Available from: https://doi.org/10.1016/j.bdr.2020.100183